librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 138	author stringlengths 2 42	last_modified unknowndate 2020-02-15 11:33:14 2025-05-23 06:28:04	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 474 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt unknowndate 2022-03-02 23:29:04 2025-05-23 06:27:34	card stringlengths 11 1.01M
vladimir707/gpt-mini1	vladimir707	"2025-04-19T12:09:43Z"	0	0	null	[ "pytorch", "region:us" ]	null	"2025-04-19T12:09:30Z"	# GPT Tiny Shakespeare (Decoder-only) Ein leichtgewichtiges autoregressives Transformer-Modell (GPT-artig), trainiert auf dem Tiny Shakespeare-Datensatz. ## Architektur - Decoder-only Transformer (ähnlich GPT-2) - <1M Parameter - 2 Layers, 4 Attention Heads - Embedding-Dimension: 128 ## Trainingsdaten Tiny Shakespeare (ca. 100k Zeichen an Theater-Dialogen von Shakespeare). ## Verwendung Für einfache Textgenerierung und Experimente auf CPUs. ## Tags - gpt - decoder-only - tiny - shakespeare - text-generation - educational
SanderGi/PCB-OBB	SanderGi	"2025-04-19T12:08:57Z"	0	0	ultralytics	[ "ultralytics", "printed-circuit-boards", "base_model:Ultralytics/YOLO11", "base_model:finetune:Ultralytics/YOLO11", "license:mit", "model-index", "region:us" ]	null	"2025-04-19T11:43:34Z"	--- license: mit base_model: - Ultralytics/YOLO11 tags: - printed-circuit-boards library_name: ultralytics model-index: - name: ultralytics/yolo11 results: - task: type: object-detection metrics: - type: f1 value: 93.8% name: F1 Score - type: mAP50 value: 93.0% name: mAP50 metrics: - f1 - 93.8% - mAP50 - 93.0% --- # PCB Detection There are [a lot of models](https://universe.roboflow.com/roboflow-100/printed-circuit-board/model/3) for detecting components within a Printed Circuit Board (PCB), but not as many for detecting which pixels (if any) in an image contain the PCB itself. Being able to determine if and where a PCB is in an image is useful for [calculating its size to estimate carbon footprint]((https://github.com/SanderGi/LCA)), as a preprocessing step for detecting components, to limit the amount of image more expensive PCB defect detection models have to process, and more. Read more [here](https://github.com/SanderGi/PCB-Detection). ## Usage 1. Download [`the model weights`](https://huggingface.co/SanderGi/PCB-OBB/resolve/main/best.pt?download=true) 2. `pip install ultralytics` 3. Run the model with `yolo task=obb mode=predict model=[path to model weights] source=[path to test image]` from the terminal or with Python: ```python from ultralytics import YOLO model = YOLO('[path to model weights]') results = model.predict('[path/to/test/image.jpg]') ``` ## Results Dataset \| Precision \| Recall \| F1 Score \| mAP50 \| mAP50-95 -----------\|-----------\|--------\|----------\|--------\|--------- Training \| 100.0% \| 100.0% \| 100.0% \| 100.0% \| 100.0% Validation \| 100.0% \| 100.0% \| 100.0% \| 99.5% \| 97.0% Test \| 100.0% \| 88.4% \| 93.8% \| 93.0% \| 91.2% Sample predictions: ![sample predictions](https://github.com/SanderGi/PCB-Detection/raw/refs/heads/main/data/augmented_obb/runs/no_perspective3/val_batch1_pred.jpg)
phospho-app/lerobot_v2_ball040-mnsx4wnl5s	phospho-app	"2025-04-19T12:08:20Z"	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "replicate", "region:us" ]	null	"2025-04-19T11:31:55Z"	--- tags: - phosphobot - gr00t - replicate task_categories: - robotics --- # Gr00t Model - phospho Replication Pipeline This model was trained using phospho's Replicate pipeline for gr00t models. Training parameters: - Dataset: [pgoffin/lerobot_v2_ball040](https://huggingface.co/datasets/pgoffin/lerobot_v2_ball040) - Wandb run URL: https://wandb.ai/artcomputer123-artcomputer/gr00t-replicate/runs/tcprmxc0 - Epochs: 20 - Batch size: 64 - Training steps: 1810 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline) 🔗 Explore on Replicate: [Replicate](https://replicate.com/phospho-app/gr00t-policy)
Mehrdadslehi/Qwen2-0.5B-GRPO-SFT1_RL	Mehrdadslehi	"2025-04-19T12:05:13Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "endpoints_compatible", "region:us" ]	null	"2025-04-18T14:43:03Z"	--- datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-SFT1_RL tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-SFT1_RL This model is a fine-tuned version of [None](https://huggingface.co/None) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Mehrdadslehi/Qwen2-0.5B-GRPO-SFT1_RL", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
saisasanky/Llama-3.1-8B-Instruct-4bit-aish_gguf	saisasanky	"2025-04-19T12:05:09Z"	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T12:02:51Z"	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: saisasanky - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
TOMFORD79/Cake_13	TOMFORD79	"2025-04-19T12:02:30Z"	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	"2025-04-19T10:57:12Z"	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
hyokwan/llama31_famili_2025	hyokwan	"2025-04-19T11:56:41Z"	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	"2025-04-19T11:43:12Z"	--- license: apache-2.0 ---
rbelanec/train_mrpc_1744902647	rbelanec	"2025-04-19T11:55:12Z"	0	0	peft	[ "peft", "safetensors", "llama-factory", "prompt-tuning", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	"2025-04-19T03:05:12Z"	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - llama-factory - prompt-tuning - generated_from_trainer model-index: - name: train_mrpc_1744902647 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # train_mrpc_1744902647 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the mrpc dataset. It achieves the following results on the evaluation set: - Loss: 0.1153 - Num Input Tokens Seen: 65784064 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.3 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:--------:\|:-----:\|:---------------:\|:-----------------:\| \| 0.178 \| 0.9685 \| 200 \| 0.2087 \| 329312 \| \| 0.1823 \| 1.9395 \| 400 \| 0.1658 \| 658560 \| \| 0.1749 \| 2.9104 \| 600 \| 0.1568 \| 987040 \| \| 0.1421 \| 3.8814 \| 800 \| 0.1488 \| 1316448 \| \| 0.1589 \| 4.8523 \| 1000 \| 0.1386 \| 1644608 \| \| 0.1413 \| 5.8232 \| 1200 \| 0.1437 \| 1974016 \| \| 0.1574 \| 6.7942 \| 1400 \| 0.1415 \| 2303584 \| \| 0.1059 \| 7.7651 \| 1600 \| 0.1394 \| 2630688 \| \| 0.1251 \| 8.7361 \| 1800 \| 0.1364 \| 2959808 \| \| 0.1088 \| 9.7070 \| 2000 \| 0.1153 \| 3287584 \| \| 0.0996 \| 10.6780 \| 2200 \| 0.1288 \| 3617920 \| \| 0.0731 \| 11.6489 \| 2400 \| 0.1425 \| 3945536 \| \| 0.0462 \| 12.6199 \| 2600 \| 0.1264 \| 4274560 \| \| 0.0608 \| 13.5908 \| 2800 \| 0.1430 \| 4603168 \| \| 0.022 \| 14.5617 \| 3000 \| 0.1643 \| 4932448 \| \| 0.0288 \| 15.5327 \| 3200 \| 0.1873 \| 5261312 \| \| 0.0596 \| 16.5036 \| 3400 \| 0.1731 \| 5589632 \| \| 0.0199 \| 17.4746 \| 3600 \| 0.2195 \| 5918112 \| \| 0.0126 \| 18.4455 \| 3800 \| 0.1945 \| 6246368 \| \| 0.0278 \| 19.4165 \| 4000 \| 0.2397 \| 6574848 \| \| 0.0266 \| 20.3874 \| 4200 \| 0.2409 \| 6903520 \| \| 0.005 \| 21.3584 \| 4400 \| 0.2498 \| 7231904 \| \| 0.0084 \| 22.3293 \| 4600 \| 0.2794 \| 7561504 \| \| 0.0394 \| 23.3002 \| 4800 \| 0.3202 \| 7890912 \| \| 0.0073 \| 24.2712 \| 5000 \| 0.2466 \| 8218592 \| \| 0.0014 \| 25.2421 \| 5200 \| 0.2287 \| 8548256 \| \| 0.0264 \| 26.2131 \| 5400 \| 0.2157 \| 8876704 \| \| 0.0123 \| 27.1840 \| 5600 \| 0.2764 \| 9206272 \| \| 0.0033 \| 28.1550 \| 5800 \| 0.2357 \| 9534720 \| \| 0.0057 \| 29.1259 \| 6000 \| 0.2608 \| 9864384 \| \| 0.0052 \| 30.0969 \| 6200 \| 0.2388 \| 10193376 \| \| 0.0011 \| 31.0678 \| 6400 \| 0.3327 \| 10521952 \| \| 0.0081 \| 32.0387 \| 6600 \| 0.3200 \| 10851520 \| \| 0.0102 \| 33.0097 \| 6800 \| 0.2724 \| 11180544 \| \| 0.0056 \| 33.9782 \| 7000 \| 0.3153 \| 11509344 \| \| 0.0106 \| 34.9492 \| 7200 \| 0.3401 \| 11838208 \| \| 0.0221 \| 35.9201 \| 7400 \| 0.2370 \| 12167872 \| \| 0.0417 \| 36.8910 \| 7600 \| 0.2576 \| 12496352 \| \| 0.017 \| 37.8620 \| 7800 \| 0.2671 \| 12826048 \| \| 0.0308 \| 38.8329 \| 8000 \| 0.2404 \| 13155040 \| \| 0.0004 \| 39.8039 \| 8200 \| 0.3460 \| 13483008 \| \| 0.0026 \| 40.7748 \| 8400 \| 0.3096 \| 13812064 \| \| 0.0096 \| 41.7458 \| 8600 \| 0.2907 \| 14140576 \| \| 0.0053 \| 42.7167 \| 8800 \| 0.3575 \| 14469248 \| \| 0.0004 \| 43.6877 \| 9000 \| 0.3422 \| 14796672 \| \| 0.0004 \| 44.6586 \| 9200 \| 0.3874 \| 15126752 \| \| 0.0001 \| 45.6295 \| 9400 \| 0.4214 \| 15456160 \| \| 0.0 \| 46.6005 \| 9600 \| 0.4649 \| 15784928 \| \| 0.0 \| 47.5714 \| 9800 \| 0.4702 \| 16113248 \| \| 0.0 \| 48.5424 \| 10000 \| 0.4790 \| 16442496 \| \| 0.0 \| 49.5133 \| 10200 \| 0.4879 \| 16772640 \| \| 0.0 \| 50.4843 \| 10400 \| 0.4982 \| 17100000 \| \| 0.0 \| 51.4552 \| 10600 \| 0.5047 \| 17428768 \| \| 0.0 \| 52.4262 \| 10800 \| 0.5145 \| 17757344 \| \| 0.0 \| 53.3971 \| 11000 \| 0.5212 \| 18085920 \| \| 0.0 \| 54.3680 \| 11200 \| 0.5300 \| 18414336 \| \| 0.0 \| 55.3390 \| 11400 \| 0.5361 \| 18743040 \| \| 0.0 \| 56.3099 \| 11600 \| 0.5398 \| 19072928 \| \| 0.0 \| 57.2809 \| 11800 \| 0.5495 \| 19401376 \| \| 0.0 \| 58.2518 \| 12000 \| 0.5575 \| 19730336 \| \| 0.0 \| 59.2228 \| 12200 \| 0.5617 \| 20059488 \| \| 0.0 \| 60.1937 \| 12400 \| 0.5684 \| 20388064 \| \| 0.0 \| 61.1646 \| 12600 \| 0.5766 \| 20718144 \| \| 0.0 \| 62.1356 \| 12800 \| 0.5837 \| 21048224 \| \| 0.0 \| 63.1065 \| 13000 \| 0.5898 \| 21376576 \| \| 0.0 \| 64.0775 \| 13200 \| 0.5944 \| 21706080 \| \| 0.0 \| 65.0484 \| 13400 \| 0.6017 \| 22034624 \| \| 0.0 \| 66.0194 \| 13600 \| 0.6081 \| 22364128 \| \| 0.0 \| 66.9879 \| 13800 \| 0.6153 \| 22692352 \| \| 0.0 \| 67.9588 \| 14000 \| 0.6185 \| 23020864 \| \| 0.0 \| 68.9298 \| 14200 \| 0.6256 \| 23349920 \| \| 0.0 \| 69.9007 \| 14400 \| 0.6316 \| 23679072 \| \| 0.0 \| 70.8717 \| 14600 \| 0.6375 \| 24007776 \| \| 0.0 \| 71.8426 \| 14800 \| 0.6423 \| 24336640 \| \| 0.0 \| 72.8136 \| 15000 \| 0.6482 \| 24664576 \| \| 0.0 \| 73.7845 \| 15200 \| 0.6532 \| 24994848 \| \| 0.0 \| 74.7554 \| 15400 \| 0.6600 \| 25322720 \| \| 0.0 \| 75.7264 \| 15600 \| 0.6636 \| 25650784 \| \| 0.0 \| 76.6973 \| 15800 \| 0.6717 \| 25980512 \| \| 0.0 \| 77.6683 \| 16000 \| 0.6783 \| 26309536 \| \| 0.0 \| 78.6392 \| 16200 \| 0.6823 \| 26638944 \| \| 0.0 \| 79.6102 \| 16400 \| 0.6852 \| 26967360 \| \| 0.0 \| 80.5811 \| 16600 \| 0.6886 \| 27297120 \| \| 0.0 \| 81.5521 \| 16800 \| 0.6960 \| 27626144 \| \| 0.0 \| 82.5230 \| 17000 \| 0.6995 \| 27954656 \| \| 0.0 \| 83.4939 \| 17200 \| 0.7026 \| 28284160 \| \| 0.0 \| 84.4649 \| 17400 \| 0.7082 \| 28612224 \| \| 0.0 \| 85.4358 \| 17600 \| 0.7175 \| 28940448 \| \| 0.0 \| 86.4068 \| 17800 \| 0.7151 \| 29270912 \| \| 0.0 \| 87.3777 \| 18000 \| 0.7190 \| 29599424 \| \| 0.0 \| 88.3487 \| 18200 \| 0.7216 \| 29929280 \| \| 0.0 \| 89.3196 \| 18400 \| 0.7255 \| 30257504 \| \| 0.0 \| 90.2906 \| 18600 \| 0.7287 \| 30586944 \| \| 0.0 \| 91.2615 \| 18800 \| 0.7326 \| 30915744 \| \| 0.0 \| 92.2324 \| 19000 \| 0.7370 \| 31245216 \| \| 0.0 \| 93.2034 \| 19200 \| 0.7390 \| 31573600 \| \| 0.0 \| 94.1743 \| 19400 \| 0.7438 \| 31903616 \| \| 0.0 \| 95.1453 \| 19600 \| 0.7448 \| 32232032 \| \| 0.0 \| 96.1162 \| 19800 \| 0.7473 \| 32560480 \| \| 0.0 \| 97.0872 \| 20000 \| 0.7519 \| 32889696 \| \| 0.0 \| 98.0581 \| 20200 \| 0.7500 \| 33218016 \| \| 0.0 \| 99.0291 \| 20400 \| 0.7546 \| 33547296 \| \| 0.0 \| 99.9976 \| 20600 \| 0.7566 \| 33876000 \| \| 0.0 \| 100.9685 \| 20800 \| 0.7594 \| 34205376 \| \| 0.0 \| 101.9395 \| 21000 \| 0.7605 \| 34534496 \| \| 0.0 \| 102.9104 \| 21200 \| 0.7605 \| 34864000 \| \| 0.0 \| 103.8814 \| 21400 \| 0.7587 \| 35192256 \| \| 0.0 \| 104.8523 \| 21600 \| 0.7634 \| 35521376 \| \| 0.0 \| 105.8232 \| 21800 \| 0.7686 \| 35851264 \| \| 0.0 \| 106.7942 \| 22000 \| 0.7694 \| 36180000 \| \| 0.0 \| 107.7651 \| 22200 \| 0.7666 \| 36508832 \| \| 0.0 \| 108.7361 \| 22400 \| 0.7679 \| 36837600 \| \| 0.0 \| 109.7070 \| 22600 \| 0.7702 \| 37166720 \| \| 0.0 \| 110.6780 \| 22800 \| 0.7690 \| 37495520 \| \| 0.0 \| 111.6489 \| 23000 \| 0.7686 \| 37824352 \| \| 0.0 \| 112.6199 \| 23200 \| 0.7735 \| 38153856 \| \| 0.0 \| 113.5908 \| 23400 \| 0.7741 \| 38483200 \| \| 0.0 \| 114.5617 \| 23600 \| 0.7726 \| 38812672 \| \| 0.0 \| 115.5327 \| 23800 \| 0.7704 \| 39142400 \| \| 0.0 \| 116.5036 \| 24000 \| 0.7778 \| 39471200 \| \| 0.0 \| 117.4746 \| 24200 \| 0.7778 \| 39798848 \| \| 0.0 \| 118.4455 \| 24400 \| 0.7782 \| 40127360 \| \| 0.0 \| 119.4165 \| 24600 \| 0.7768 \| 40456736 \| \| 0.0 \| 120.3874 \| 24800 \| 0.7763 \| 40785312 \| \| 0.0 \| 121.3584 \| 25000 \| 0.7760 \| 41112576 \| \| 0.0 \| 122.3293 \| 25200 \| 0.7755 \| 41442112 \| \| 0.0 \| 123.3002 \| 25400 \| 0.7797 \| 41771552 \| \| 0.0 \| 124.2712 \| 25600 \| 0.7775 \| 42101248 \| \| 0.0 \| 125.2421 \| 25800 \| 0.7784 \| 42427392 \| \| 0.0 \| 126.2131 \| 26000 \| 0.7776 \| 42756704 \| \| 0.0 \| 127.1840 \| 26200 \| 0.7778 \| 43085664 \| \| 0.0 \| 128.1550 \| 26400 \| 0.7804 \| 43414240 \| \| 0.0 \| 129.1259 \| 26600 \| 0.7818 \| 43743072 \| \| 0.0 \| 130.0969 \| 26800 \| 0.7803 \| 44072768 \| \| 0.0 \| 131.0678 \| 27000 \| 0.7818 \| 44400192 \| \| 0.0 \| 132.0387 \| 27200 \| 0.7777 \| 44729632 \| \| 0.0 \| 133.0097 \| 27400 \| 0.7763 \| 45058976 \| \| 0.0 \| 133.9782 \| 27600 \| 0.7758 \| 45388352 \| \| 0.0 \| 134.9492 \| 27800 \| 0.7759 \| 45717952 \| \| 0.0 \| 135.9201 \| 28000 \| 0.7753 \| 46046144 \| \| 0.0 \| 136.8910 \| 28200 \| 0.7770 \| 46375168 \| \| 0.0 \| 137.8620 \| 28400 \| 0.7772 \| 46702816 \| \| 0.0 \| 138.8329 \| 28600 \| 0.7731 \| 47033152 \| \| 0.0 \| 139.8039 \| 28800 \| 0.7766 \| 47361472 \| \| 0.0 \| 140.7748 \| 29000 \| 0.7751 \| 47691424 \| \| 0.0 \| 141.7458 \| 29200 \| 0.7738 \| 48019712 \| \| 0.0 \| 142.7167 \| 29400 \| 0.7735 \| 48348832 \| \| 0.0 \| 143.6877 \| 29600 \| 0.7778 \| 48678560 \| \| 0.0 \| 144.6586 \| 29800 \| 0.7774 \| 49008256 \| \| 0.0 \| 145.6295 \| 30000 \| 0.7776 \| 49337088 \| \| 0.0 \| 146.6005 \| 30200 \| 0.7748 \| 49665344 \| \| 0.0 \| 147.5714 \| 30400 \| 0.7795 \| 49996128 \| \| 0.0 \| 148.5424 \| 30600 \| 0.7759 \| 50324736 \| \| 0.0 \| 149.5133 \| 30800 \| 0.7778 \| 50652864 \| \| 0.0 \| 150.4843 \| 31000 \| 0.7747 \| 50981920 \| \| 0.0 \| 151.4552 \| 31200 \| 0.7766 \| 51310752 \| \| 0.0 \| 152.4262 \| 31400 \| 0.7740 \| 51640352 \| \| 0.0 \| 153.3971 \| 31600 \| 0.7767 \| 51969184 \| \| 0.0 \| 154.3680 \| 31800 \| 0.7749 \| 52297280 \| \| 0.0 \| 155.3390 \| 32000 \| 0.7770 \| 52625600 \| \| 0.0 \| 156.3099 \| 32200 \| 0.7763 \| 52953920 \| \| 0.0 \| 157.2809 \| 32400 \| 0.7756 \| 53283648 \| \| 0.0 \| 158.2518 \| 32600 \| 0.7734 \| 53613056 \| \| 0.0 \| 159.2228 \| 32800 \| 0.7745 \| 53941632 \| \| 0.0 \| 160.1937 \| 33000 \| 0.7744 \| 54270272 \| \| 0.0 \| 161.1646 \| 33200 \| 0.7751 \| 54599104 \| \| 0.0 \| 162.1356 \| 33400 \| 0.7762 \| 54929056 \| \| 0.0 \| 163.1065 \| 33600 \| 0.7753 \| 55257728 \| \| 0.0 \| 164.0775 \| 33800 \| 0.7750 \| 55587456 \| \| 0.0 \| 165.0484 \| 34000 \| 0.7761 \| 55916576 \| \| 0.0 \| 166.0194 \| 34200 \| 0.7766 \| 56245664 \| \| 0.0 \| 166.9879 \| 34400 \| 0.7779 \| 56574272 \| \| 0.0 \| 167.9588 \| 34600 \| 0.7757 \| 56903360 \| \| 0.0 \| 168.9298 \| 34800 \| 0.7782 \| 57232032 \| \| 0.0 \| 169.9007 \| 35000 \| 0.7748 \| 57561504 \| \| 0.0 \| 170.8717 \| 35200 \| 0.7741 \| 57891168 \| \| 0.0 \| 171.8426 \| 35400 \| 0.7750 \| 58220352 \| \| 0.0 \| 172.8136 \| 35600 \| 0.7764 \| 58548960 \| \| 0.0 \| 173.7845 \| 35800 \| 0.7737 \| 58878688 \| \| 0.0 \| 174.7554 \| 36000 \| 0.7754 \| 59207104 \| \| 0.0 \| 175.7264 \| 36200 \| 0.7763 \| 59536800 \| \| 0.0 \| 176.6973 \| 36400 \| 0.7769 \| 59865312 \| \| 0.0 \| 177.6683 \| 36600 \| 0.7765 \| 60194816 \| \| 0.0 \| 178.6392 \| 36800 \| 0.7797 \| 60523584 \| \| 0.0 \| 179.6102 \| 37000 \| 0.7767 \| 60852352 \| \| 0.0 \| 180.5811 \| 37200 \| 0.7763 \| 61181024 \| \| 0.0 \| 181.5521 \| 37400 \| 0.7752 \| 61510624 \| \| 0.0 \| 182.5230 \| 37600 \| 0.7787 \| 61840672 \| \| 0.0 \| 183.4939 \| 37800 \| 0.7763 \| 62167808 \| \| 0.0 \| 184.4649 \| 38000 \| 0.7755 \| 62496960 \| \| 0.0 \| 185.4358 \| 38200 \| 0.7765 \| 62826016 \| \| 0.0 \| 186.4068 \| 38400 \| 0.7740 \| 63154784 \| \| 0.0 \| 187.3777 \| 38600 \| 0.7765 \| 63483904 \| \| 0.0 \| 188.3487 \| 38800 \| 0.7759 \| 63811808 \| \| 0.0 \| 189.3196 \| 39000 \| 0.7742 \| 64139488 \| \| 0.0 \| 190.2906 \| 39200 \| 0.7752 \| 64467808 \| \| 0.0 \| 191.2615 \| 39400 \| 0.7753 \| 64798112 \| \| 0.0 \| 192.2324 \| 39600 \| 0.7759 \| 65126304 \| \| 0.0 \| 193.2034 \| 39800 \| 0.7780 \| 65455776 \| \| 0.0 \| 194.1743 \| 40000 \| 0.7753 \| 65784064 \| ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
shadenmoh/xlm-roberta-base	shadenmoh	"2025-04-19T11:54:04Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:tner/xlm-roberta-base-panx-dataset-ar", "base_model:finetune:tner/xlm-roberta-base-panx-dataset-ar", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2025-04-19T11:51:06Z"	--- library_name: transformers base_model: tner/xlm-roberta-base-panx-dataset-ar tags: - generated_from_trainer model-index: - name: xlm-roberta-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base This model is a fine-tuned version of [tner/xlm-roberta-base-panx-dataset-ar](https://huggingface.co/tner/xlm-roberta-base-panx-dataset-ar) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Vergill2345/Razor	Vergill2345	"2025-04-19T11:53:44Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-04-19T11:53:44Z"	--- license: apache-2.0 ---
rbelanec/train_sst2_1744902627	rbelanec	"2025-04-19T11:52:17Z"	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]	null	"2025-04-18T23:09:42Z"	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - llama-factory - lora - generated_from_trainer model-index: - name: train_sst2_1744902627 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # train_sst2_1744902627 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0593 - Num Input Tokens Seen: 33458560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:-----------------:\| \| 0.0745 \| 0.0528 \| 200 \| 0.0783 \| 166688 \| \| 0.1164 \| 0.1056 \| 400 \| 0.1055 \| 334048 \| \| 0.0125 \| 0.1584 \| 600 \| 0.0809 \| 500448 \| \| 0.0293 \| 0.2112 \| 800 \| 0.0696 \| 667872 \| \| 0.0633 \| 0.2640 \| 1000 \| 0.0697 \| 834848 \| \| 0.037 \| 0.3167 \| 1200 \| 0.0937 \| 1002816 \| \| 0.0586 \| 0.3695 \| 1400 \| 0.0752 \| 1169088 \| \| 0.1164 \| 0.4223 \| 1600 \| 0.0692 \| 1337088 \| \| 0.0527 \| 0.4751 \| 1800 \| 0.0690 \| 1505536 \| \| 0.0513 \| 0.5279 \| 2000 \| 0.0648 \| 1673024 \| \| 0.0544 \| 0.5807 \| 2200 \| 0.0687 \| 1842304 \| \| 0.0687 \| 0.6335 \| 2400 \| 0.0648 \| 2007328 \| \| 0.0739 \| 0.6863 \| 2600 \| 0.0617 \| 2174880 \| \| 0.1184 \| 0.7391 \| 2800 \| 0.0758 \| 2341280 \| \| 0.113 \| 0.7919 \| 3000 \| 0.0676 \| 2509440 \| \| 0.0589 \| 0.8447 \| 3200 \| 0.0651 \| 2674784 \| \| 0.0484 \| 0.8975 \| 3400 \| 0.0629 \| 2843680 \| \| 0.1224 \| 0.9502 \| 3600 \| 0.0674 \| 3011904 \| \| 0.0241 \| 1.0029 \| 3800 \| 0.0719 \| 3178064 \| \| 0.0495 \| 1.0557 \| 4000 \| 0.0655 \| 3345904 \| \| 0.0462 \| 1.1085 \| 4200 \| 0.0620 \| 3514608 \| \| 0.0457 \| 1.1613 \| 4400 \| 0.0608 \| 3680560 \| \| 0.0455 \| 1.2141 \| 4600 \| 0.0638 \| 3849328 \| \| 0.0395 \| 1.2669 \| 4800 \| 0.0638 \| 4017200 \| \| 0.037 \| 1.3197 \| 5000 \| 0.0653 \| 4187184 \| \| 0.0199 \| 1.3724 \| 5200 \| 0.0640 \| 4354416 \| \| 0.0455 \| 1.4252 \| 5400 \| 0.0654 \| 4519856 \| \| 0.0231 \| 1.4780 \| 5600 \| 0.0632 \| 4687280 \| \| 0.0568 \| 1.5308 \| 5800 \| 0.0660 \| 4856112 \| \| 0.0563 \| 1.5836 \| 6000 \| 0.0604 \| 5022736 \| \| 0.1588 \| 1.6364 \| 6200 \| 0.0595 \| 5188656 \| \| 0.0301 \| 1.6892 \| 6400 \| 0.0716 \| 5356208 \| \| 0.0572 \| 1.7420 \| 6600 \| 0.0593 \| 5523952 \| \| 0.0309 \| 1.7948 \| 6800 \| 0.0615 \| 5690672 \| \| 0.0549 \| 1.8476 \| 7000 \| 0.0636 \| 5857072 \| \| 0.0461 \| 1.9004 \| 7200 \| 0.0662 \| 6024976 \| \| 0.0753 \| 1.9531 \| 7400 \| 0.0680 \| 6191664 \| \| 0.0403 \| 2.0058 \| 7600 \| 0.0632 \| 6357472 \| \| 0.0261 \| 2.0586 \| 7800 \| 0.0631 \| 6525984 \| \| 0.0126 \| 2.1114 \| 8000 \| 0.0692 \| 6692320 \| \| 0.0039 \| 2.1642 \| 8200 \| 0.0756 \| 6860064 \| \| 0.0463 \| 2.2170 \| 8400 \| 0.0737 \| 7026528 \| \| 0.0118 \| 2.2698 \| 8600 \| 0.0701 \| 7192384 \| \| 0.0211 \| 2.3226 \| 8800 \| 0.0734 \| 7358816 \| \| 0.0369 \| 2.3753 \| 9000 \| 0.0806 \| 7526496 \| \| 0.0336 \| 2.4281 \| 9200 \| 0.1007 \| 7696064 \| \| 0.0252 \| 2.4809 \| 9400 \| 0.0934 \| 7863456 \| \| 0.0041 \| 2.5337 \| 9600 \| 0.0711 \| 8031776 \| \| 0.0297 \| 2.5865 \| 9800 \| 0.0753 \| 8199584 \| \| 0.0712 \| 2.6393 \| 10000 \| 0.0741 \| 8366016 \| \| 0.008 \| 2.6921 \| 10200 \| 0.0924 \| 8531808 \| \| 0.0324 \| 2.7449 \| 10400 \| 0.0728 \| 8702976 \| \| 0.0155 \| 2.7977 \| 10600 \| 0.0793 \| 8870944 \| \| 0.057 \| 2.8505 \| 10800 \| 0.0790 \| 9039680 \| \| 0.0472 \| 2.9033 \| 11000 \| 0.0685 \| 9206880 \| \| 0.0067 \| 2.9561 \| 11200 \| 0.0759 \| 9372128 \| \| 0.023 \| 3.0087 \| 11400 \| 0.1036 \| 9538768 \| \| 0.0009 \| 3.0615 \| 11600 \| 0.1041 \| 9705232 \| \| 0.0026 \| 3.1143 \| 11800 \| 0.1420 \| 9871632 \| \| 0.007 \| 3.1671 \| 12000 \| 0.1002 \| 10039472 \| \| 0.0023 \| 3.2199 \| 12200 \| 0.1041 \| 10206320 \| \| 0.003 \| 3.2727 \| 12400 \| 0.1281 \| 10376240 \| \| 0.0087 \| 3.3255 \| 12600 \| 0.1209 \| 10544464 \| \| 0.0146 \| 3.3782 \| 12800 \| 0.1133 \| 10712240 \| \| 0.0024 \| 3.4310 \| 13000 \| 0.1138 \| 10879120 \| \| 0.0002 \| 3.4838 \| 13200 \| 0.1368 \| 11045072 \| \| 0.0046 \| 3.5366 \| 13400 \| 0.1290 \| 11211312 \| \| 0.0263 \| 3.5894 \| 13600 \| 0.1125 \| 11378128 \| \| 0.0051 \| 3.6422 \| 13800 \| 0.1019 \| 11544592 \| \| 0.019 \| 3.6950 \| 14000 \| 0.0911 \| 11713040 \| \| 0.0066 \| 3.7478 \| 14200 \| 0.1016 \| 11880432 \| \| 0.0148 \| 3.8006 \| 14400 \| 0.1121 \| 12048176 \| \| 0.0001 \| 3.8534 \| 14600 \| 0.1492 \| 12215792 \| \| 0.0281 \| 3.9062 \| 14800 \| 0.1034 \| 12383792 \| \| 0.0056 \| 3.9590 \| 15000 \| 0.0986 \| 12549680 \| \| 0.0001 \| 4.0116 \| 15200 \| 0.1283 \| 12716448 \| \| 0.0037 \| 4.0644 \| 15400 \| 0.2098 \| 12882752 \| \| 0.0078 \| 4.1172 \| 15600 \| 0.1637 \| 13051200 \| \| 0.0078 \| 4.1700 \| 15800 \| 0.1420 \| 13217024 \| \| 0.0162 \| 4.2228 \| 16000 \| 0.1648 \| 13382784 \| \| 0.0003 \| 4.2756 \| 16200 \| 0.1115 \| 13549216 \| \| 0.0021 \| 4.3284 \| 16400 \| 0.1300 \| 13719072 \| \| 0.0028 \| 4.3812 \| 16600 \| 0.1370 \| 13884928 \| \| 0.0028 \| 4.4339 \| 16800 \| 0.1263 \| 14051584 \| \| 0.0001 \| 4.4867 \| 17000 \| 0.1414 \| 14220704 \| \| 0.0038 \| 4.5395 \| 17200 \| 0.1233 \| 14387008 \| \| 0.0052 \| 4.5923 \| 17400 \| 0.2157 \| 14555808 \| \| 0.0 \| 4.6451 \| 17600 \| 0.2525 \| 14723456 \| \| 0.0196 \| 4.6979 \| 17800 \| 0.1433 \| 14890880 \| \| 0.0229 \| 4.7507 \| 18000 \| 0.1838 \| 15059744 \| \| 0.057 \| 4.8035 \| 18200 \| 0.1577 \| 15224512 \| \| 0.0453 \| 4.8563 \| 18400 \| 0.1232 \| 15392960 \| \| 0.0066 \| 4.9091 \| 18600 \| 0.1784 \| 15561696 \| \| 0.0162 \| 4.9619 \| 18800 \| 0.1757 \| 15728800 \| \| 0.0485 \| 5.0145 \| 19000 \| 0.1900 \| 15897552 \| \| 0.0009 \| 5.0673 \| 19200 \| 0.1809 \| 16064688 \| \| 0.0005 \| 5.1201 \| 19400 \| 0.1684 \| 16231120 \| \| 0.0044 \| 5.1729 \| 19600 \| 0.1712 \| 16397744 \| \| 0.1072 \| 5.2257 \| 19800 \| 0.1722 \| 16564176 \| \| 0.037 \| 5.2785 \| 20000 \| 0.1688 \| 16731600 \| \| 0.0052 \| 5.3313 \| 20200 \| 0.1799 \| 16898064 \| \| 0.0 \| 5.3841 \| 20400 \| 0.1782 \| 17064080 \| \| 0.0002 \| 5.4368 \| 20600 \| 0.2476 \| 17231888 \| \| 0.0057 \| 5.4896 \| 20800 \| 0.1952 \| 17399184 \| \| 0.0022 \| 5.5424 \| 21000 \| 0.2121 \| 17566160 \| \| 0.0063 \| 5.5952 \| 21200 \| 0.1922 \| 17732304 \| \| 0.004 \| 5.6480 \| 21400 \| 0.1764 \| 17900880 \| \| 0.0001 \| 5.7008 \| 21600 \| 0.1741 \| 18070192 \| \| 0.0001 \| 5.7536 \| 21800 \| 0.1708 \| 18237168 \| \| 0.0058 \| 5.8064 \| 22000 \| 0.2006 \| 18403856 \| \| 0.002 \| 5.8592 \| 22200 \| 0.2176 \| 18571248 \| \| 0.0 \| 5.9120 \| 22400 \| 0.2260 \| 18738672 \| \| 0.0002 \| 5.9648 \| 22600 \| 0.1856 \| 18905744 \| \| 0.0 \| 6.0174 \| 22800 \| 0.2380 \| 19073440 \| \| 0.0 \| 6.0702 \| 23000 \| 0.1898 \| 19241920 \| \| 0.0036 \| 6.1230 \| 23200 \| 0.1885 \| 19409408 \| \| 0.0 \| 6.1758 \| 23400 \| 0.1937 \| 19577024 \| \| 0.0532 \| 6.2286 \| 23600 \| 0.1851 \| 19744608 \| \| 0.0036 \| 6.2814 \| 23800 \| 0.1758 \| 19911488 \| \| 0.0001 \| 6.3342 \| 24000 \| 0.2023 \| 20078944 \| \| 0.0 \| 6.3870 \| 24200 \| 0.2283 \| 20244928 \| \| 0.0107 \| 6.4398 \| 24400 \| 0.1919 \| 20411232 \| \| 0.0 \| 6.4925 \| 24600 \| 0.2069 \| 20578080 \| \| 0.0002 \| 6.5453 \| 24800 \| 0.1839 \| 20746592 \| \| 0.0 \| 6.5981 \| 25000 \| 0.1915 \| 20913344 \| \| 0.0 \| 6.6509 \| 25200 \| 0.2142 \| 21081952 \| \| 0.0051 \| 6.7037 \| 25400 \| 0.1797 \| 21248384 \| \| 0.0027 \| 6.7565 \| 25600 \| 0.1834 \| 21415872 \| \| 0.0004 \| 6.8093 \| 25800 \| 0.1750 \| 21584000 \| \| 0.0004 \| 6.8621 \| 26000 \| 0.2093 \| 21751168 \| \| 0.0 \| 6.9149 \| 26200 \| 0.1849 \| 21918816 \| \| 0.0291 \| 6.9677 \| 26400 \| 0.1955 \| 22084384 \| \| 0.0001 \| 7.0203 \| 26600 \| 0.2036 \| 22251776 \| \| 0.0 \| 7.0731 \| 26800 \| 0.2368 \| 22418080 \| \| 0.0 \| 7.1259 \| 27000 \| 0.2574 \| 22587392 \| \| 0.0 \| 7.1787 \| 27200 \| 0.2346 \| 22753056 \| \| 0.0 \| 7.2315 \| 27400 \| 0.2273 \| 22920768 \| \| 0.0 \| 7.2843 \| 27600 \| 0.2511 \| 23087296 \| \| 0.0 \| 7.3371 \| 27800 \| 0.2544 \| 23254400 \| \| 0.0 \| 7.3899 \| 28000 \| 0.2559 \| 23422752 \| \| 0.0 \| 7.4427 \| 28200 \| 0.2674 \| 23588352 \| \| 0.0055 \| 7.4954 \| 28400 \| 0.2443 \| 23755840 \| \| 0.0001 \| 7.5482 \| 28600 \| 0.2886 \| 23923680 \| \| 0.0 \| 7.6010 \| 28800 \| 0.3007 \| 24091168 \| \| 0.0 \| 7.6538 \| 29000 \| 0.2789 \| 24258016 \| \| 0.0 \| 7.7066 \| 29200 \| 0.2825 \| 24427808 \| \| 0.0 \| 7.7594 \| 29400 \| 0.2882 \| 24596288 \| \| 0.0 \| 7.8122 \| 29600 \| 0.2831 \| 24764192 \| \| 0.0 \| 7.8650 \| 29800 \| 0.2660 \| 24932000 \| \| 0.0 \| 7.9178 \| 30000 \| 0.2519 \| 25100224 \| \| 0.0229 \| 7.9706 \| 30200 \| 0.2555 \| 25267808 \| \| 0.0 \| 8.0232 \| 30400 \| 0.2564 \| 25433440 \| \| 0.0 \| 8.0760 \| 30600 \| 0.2685 \| 25600672 \| \| 0.0 \| 8.1288 \| 30800 \| 0.2806 \| 25769408 \| \| 0.0 \| 8.1816 \| 31000 \| 0.2828 \| 25936160 \| \| 0.0 \| 8.2344 \| 31200 \| 0.2733 \| 26103744 \| \| 0.0 \| 8.2872 \| 31400 \| 0.2767 \| 26270560 \| \| 0.0 \| 8.3400 \| 31600 \| 0.2765 \| 26437536 \| \| 0.0 \| 8.3928 \| 31800 \| 0.2866 \| 26604480 \| \| 0.0 \| 8.4456 \| 32000 \| 0.2873 \| 26771680 \| \| 0.0 \| 8.4984 \| 32200 \| 0.2827 \| 26940256 \| \| 0.0 \| 8.5511 \| 32400 \| 0.2836 \| 27107680 \| \| 0.0 \| 8.6039 \| 32600 \| 0.2861 \| 27274048 \| \| 0.0 \| 8.6567 \| 32800 \| 0.2895 \| 27440544 \| \| 0.0 \| 8.7095 \| 33000 \| 0.2866 \| 27608000 \| \| 0.0 \| 8.7623 \| 33200 \| 0.2908 \| 27776704 \| \| 0.0 \| 8.8151 \| 33400 \| 0.2903 \| 27942752 \| \| 0.0 \| 8.8679 \| 33600 \| 0.2963 \| 28108864 \| \| 0.0 \| 8.9207 \| 33800 \| 0.2992 \| 28275296 \| \| 0.0 \| 8.9735 \| 34000 \| 0.2939 \| 28443520 \| \| 0.0 \| 9.0261 \| 34200 \| 0.3001 \| 28609776 \| \| 0.0 \| 9.0789 \| 34400 \| 0.3015 \| 28777712 \| \| 0.0 \| 9.1317 \| 34600 \| 0.3028 \| 28944144 \| \| 0.0 \| 9.1845 \| 34800 \| 0.3033 \| 29111152 \| \| 0.0002 \| 9.2373 \| 35000 \| 0.2992 \| 29278000 \| \| 0.0 \| 9.2901 \| 35200 \| 0.2962 \| 29443792 \| \| 0.0 \| 9.3429 \| 35400 \| 0.2975 \| 29609072 \| \| 0.0 \| 9.3957 \| 35600 \| 0.2983 \| 29776592 \| \| 0.0 \| 9.4485 \| 35800 \| 0.2987 \| 29941616 \| \| 0.0 \| 9.5013 \| 36000 \| 0.2990 \| 30110160 \| \| 0.0 \| 9.5540 \| 36200 \| 0.2988 \| 30277744 \| \| 0.0 \| 9.6068 \| 36400 \| 0.2996 \| 30447152 \| \| 0.0 \| 9.6596 \| 36600 \| 0.3006 \| 30612976 \| \| 0.0 \| 9.7124 \| 36800 \| 0.3007 \| 30780240 \| \| 0.0 \| 9.7652 \| 37000 \| 0.3018 \| 30948048 \| \| 0.0 \| 9.8180 \| 37200 \| 0.3031 \| 31116368 \| \| 0.0 \| 9.8708 \| 37400 \| 0.3022 \| 31283888 \| \| 0.0 \| 9.9236 \| 37600 \| 0.3025 \| 31452560 \| \| 0.0 \| 9.9764 \| 37800 \| 0.3033 \| 31620720 \| \| 0.0 \| 10.0290 \| 38000 \| 0.3030 \| 31786016 \| \| 0.0 \| 10.0818 \| 38200 \| 0.3018 \| 31952768 \| \| 0.0 \| 10.1346 \| 38400 \| 0.3032 \| 32120320 \| \| 0.0 \| 10.1874 \| 38600 \| 0.3024 \| 32287584 \| \| 0.0 \| 10.2402 \| 38800 \| 0.3030 \| 32455072 \| \| 0.0 \| 10.2930 \| 39000 \| 0.3025 \| 32621184 \| \| 0.0 \| 10.3458 \| 39200 \| 0.3031 \| 32788960 \| \| 0.0 \| 10.3986 \| 39400 \| 0.3028 \| 32955776 \| \| 0.0 \| 10.4514 \| 39600 \| 0.3024 \| 33122816 \| \| 0.0 \| 10.5042 \| 39800 \| 0.3032 \| 33291072 \| \| 0.0 \| 10.5569 \| 40000 \| 0.3027 \| 33458560 \| ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
swarup3204/byt5-sanskrit-original-data-ft	swarup3204	"2025-04-19T11:47:47Z"	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "sa", "arxiv:1910.09700", "base_model:chronbmm/sanskrit5-multitask", "base_model:finetune:chronbmm/sanskrit5-multitask", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	"2025-04-19T07:46:59Z"	--- library_name: transformers license: apache-2.0 language: - sa base_model: - chronbmm/sanskrit5-multitask --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Beinsezii/Mistral-Small-24B-Instruct-2501-Q6_K_C-GGUF	Beinsezii	"2025-04-19T11:47:22Z"	0	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T11:16:34Z"	--- license: apache-2.0 --- https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501 Q6_K_C: Q6_K weights, copied output, copied embed Fits 24K CTX on a 24GiB GPU
Dans-DiscountModels/7b-m-dans-optimizersweeps-repremover-1-ademamix-hi-lr-b1_0.9-b2_0.999-b3_0.999-a10	Dans-DiscountModels	"2025-04-19T11:45:52Z"	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "axolotl", "generated_from_trainer", "conversational", "dataset:Dans-DiscountModels/pretokenization-test-3", "base_model:Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2", "base_model:finetune:Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T11:12:01Z"	--- library_name: transformers base_model: Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2 tags: - axolotl - generated_from_trainer datasets: - Dans-DiscountModels/pretokenization-test-3 model-index: - name: 7b-m-dans-optimizersweeps-repremover-1-ademamix-hi-lr-b1_0.9-b2_0.999-b3_0.999-a10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.8.0` ```yaml base_model: Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer trust_remote_code: # wandb configuration wandb_project: 7b-m-dans-optimizersweeps wandb_watch: wandb_run_id: repremover-1-1-ademamix-hi-lr-b1_0.9-b2_0.999-b3_0.999-a10 wandb_log_model: # push checkpoints to hub hub_model_id: Dans-DiscountModels/7b-m-dans-optimizersweeps-repremover-1-ademamix-hi-lr-b1_0.9-b2_0.999-b3_0.999-a10 # how to push checkpoints to hub # https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy hub_strategy: "every_save" # Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets # Required to be true when used in combination with `push_dataset_to_hub` hf_use_auth_token: true # where to save the finished model to output_dir: ./7b-m-dans-optimizersweeps # where to save the dataset to dataset_prepared_path: ./7b-m-dans-optimizersweeps-data save_safetensors: true # dataset settings (local or huggingface repo) datasets: - path: Dans-DiscountModels/pretokenization-test-3 ds_type: parquet type: plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true liger_fused_linear_cross_entropy: false cut_cross_entropy: true load_in_8bit: false load_in_4bit: false strict: false adapter: lora_model_dir: val_set_size: 0.01 sequence_len: 8192 sample_packing: false eval_sample_packing: false pad_to_sequence_len: true gradient_checkpointing: true # gradient_checkpointing_kwargs: # use_reentrant: false gradient_accumulation_steps: 1 micro_batch_size: 4 num_epochs: 3 optimizer: ademamix optim_args: "beta1=0.9,beta2=0.999,beta3=0.999,alpha=10" lr_scheduler: rex learning_rate: 0.0000003 cosine_min_lr_ratio: # weight_decay: 0.03 max_grad_norm: 0.001 train_on_inputs: false group_by_length: true bf16: true fp16: false tf32: false early_stopping_patience: resume_from_checkpoint: auto_resume_from_checkpoints: false local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_ratio: 0.1 evals_per_epoch: 24 eval_table_size: eval_max_new_tokens: saves_per_epoch: 1 save_total_limit: 2 debug: false deepspeed: deepspeed_configs/zero3_bf16.json fsdp: fsdp_config: special_tokens: ``` </details><br> # 7b-m-dans-optimizersweeps-repremover-1-ademamix-hi-lr-b1_0.9-b2_0.999-b3_0.999-a10 This model is a fine-tuned version of [Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2](https://huggingface.co/Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2) on the Dans-DiscountModels/pretokenization-test-3 dataset. It achieves the following results on the evaluation set: - Loss: 2.0850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-07 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Use ademamix and the args are: beta1=0.9,beta2=0.999,beta3=0.999,alpha=10 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 41 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.0376 \| 0.0072 \| 1 \| 2.1457 \| \| 2.2661 \| 0.0432 \| 6 \| 2.1208 \| \| 2.307 \| 0.0863 \| 12 \| 2.1567 \| \| 2.1831 \| 0.1295 \| 18 \| 2.1175 \| \| 2.2321 \| 0.1727 \| 24 \| 2.1267 \| \| 2.0512 \| 0.2158 \| 30 \| 2.1440 \| \| 2.1275 \| 0.2590 \| 36 \| 2.1177 \| \| 2.0276 \| 0.3022 \| 42 \| 2.1115 \| \| 2.0803 \| 0.3453 \| 48 \| 2.1289 \| \| 2.1525 \| 0.3885 \| 54 \| 2.1259 \| \| 2.0461 \| 0.4317 \| 60 \| 2.1080 \| \| 2.0416 \| 0.4748 \| 66 \| 2.1281 \| \| 2.2091 \| 0.5180 \| 72 \| 2.1134 \| \| 2.1002 \| 0.5612 \| 78 \| 2.1163 \| \| 2.0207 \| 0.6043 \| 84 \| 2.1346 \| \| 2.1418 \| 0.6475 \| 90 \| 2.1178 \| \| 2.0907 \| 0.6906 \| 96 \| 2.1061 \| \| 2.1079 \| 0.7338 \| 102 \| 2.1228 \| \| 2.0733 \| 0.7770 \| 108 \| 2.1221 \| \| 2.0229 \| 0.8201 \| 114 \| 2.1011 \| \| 2.0239 \| 0.8633 \| 120 \| 2.1111 \| \| 1.9952 \| 0.9065 \| 126 \| 2.1107 \| \| 2.1515 \| 0.9496 \| 132 \| 2.0999 \| \| 1.9878 \| 0.9928 \| 138 \| 2.1050 \| \| 2.0482 \| 1.0360 \| 144 \| 2.1151 \| \| 1.9203 \| 1.0791 \| 150 \| 2.0964 \| \| 2.0638 \| 1.1223 \| 156 \| 2.1202 \| \| 1.9855 \| 1.1655 \| 162 \| 2.1308 \| \| 1.9788 \| 1.2086 \| 168 \| 2.1189 \| \| 1.9651 \| 1.2518 \| 174 \| 2.1124 \| \| 1.9656 \| 1.2950 \| 180 \| 2.1234 \| \| 2.0319 \| 1.3381 \| 186 \| 2.1157 \| \| 2.0527 \| 1.3813 \| 192 \| 2.1175 \| \| 2.0895 \| 1.4245 \| 198 \| 2.1198 \| \| 1.9853 \| 1.4676 \| 204 \| 2.1186 \| \| 2.0482 \| 1.5108 \| 210 \| 2.1123 \| \| 1.892 \| 1.5540 \| 216 \| 2.1013 \| \| 2.0457 \| 1.5971 \| 222 \| 2.1133 \| \| 1.9954 \| 1.6403 \| 228 \| 2.1084 \| \| 1.9719 \| 1.6835 \| 234 \| 2.1045 \| \| 2.0459 \| 1.7266 \| 240 \| 2.1159 \| \| 1.9969 \| 1.7698 \| 246 \| 2.1020 \| \| 1.9273 \| 1.8129 \| 252 \| 2.1154 \| \| 1.9269 \| 1.8561 \| 258 \| 2.1231 \| \| 1.9751 \| 1.8993 \| 264 \| 2.1132 \| \| 1.9338 \| 1.9424 \| 270 \| 2.0767 \| \| 1.9924 \| 1.9856 \| 276 \| 2.1092 \| \| 1.9114 \| 2.0288 \| 282 \| 2.1149 \| \| 1.9014 \| 2.0719 \| 288 \| 2.1025 \| \| 1.9959 \| 2.1151 \| 294 \| 2.0986 \| \| 1.9145 \| 2.1583 \| 300 \| 2.1133 \| \| 1.8756 \| 2.2014 \| 306 \| 2.1224 \| \| 1.8999 \| 2.2446 \| 312 \| 2.1034 \| \| 1.963 \| 2.2878 \| 318 \| 2.1198 \| \| 1.9189 \| 2.3309 \| 324 \| 2.1308 \| \| 1.9539 \| 2.3741 \| 330 \| 2.1069 \| \| 1.9463 \| 2.4173 \| 336 \| 2.1014 \| \| 1.9892 \| 2.4604 \| 342 \| 2.1129 \| \| 1.9526 \| 2.5036 \| 348 \| 2.1019 \| \| 2.0455 \| 2.5468 \| 354 \| 2.1284 \| \| 1.9248 \| 2.5899 \| 360 \| 2.1191 \| \| 1.8867 \| 2.6331 \| 366 \| 2.0985 \| \| 1.7824 \| 2.6763 \| 372 \| 2.1137 \| \| 1.6577 \| 2.7194 \| 378 \| 2.0967 \| \| 1.7822 \| 2.7626 \| 384 \| 2.0938 \| \| 1.84 \| 2.8058 \| 390 \| 2.1322 \| \| 1.8023 \| 2.8489 \| 396 \| 2.0898 \| \| 1.8613 \| 2.8921 \| 402 \| 2.1231 \| \| 1.7858 \| 2.9353 \| 408 \| 2.1254 \| \| 1.7629 \| 2.9784 \| 414 \| 2.0850 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
rbelanec/train_mrpc_1744902649	rbelanec	"2025-04-19T11:45:31Z"	0	0	peft	[ "peft", "safetensors", "llama-factory", "lntuning", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	"2025-04-19T04:29:20Z"	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - llama-factory - lntuning - generated_from_trainer model-index: - name: train_mrpc_1744902649 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # train_mrpc_1744902649 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the mrpc dataset. It achieves the following results on the evaluation set: - Loss: 0.1272 - Num Input Tokens Seen: 65784064 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:--------:\|:-----:\|:---------------:\|:-----------------:\| \| 0.1792 \| 0.9685 \| 200 \| 0.1595 \| 329312 \| \| 0.1616 \| 1.9395 \| 400 \| 0.1593 \| 658560 \| \| 0.1105 \| 2.9104 \| 600 \| 0.1366 \| 987040 \| \| 0.093 \| 3.8814 \| 800 \| 0.1343 \| 1316448 \| \| 0.1363 \| 4.8523 \| 1000 \| 0.1369 \| 1644608 \| \| 0.1061 \| 5.8232 \| 1200 \| 0.1310 \| 1974016 \| \| 0.1695 \| 6.7942 \| 1400 \| 0.1355 \| 2303584 \| \| 0.0548 \| 7.7651 \| 1600 \| 0.1272 \| 2630688 \| \| 0.1025 \| 8.7361 \| 1800 \| 0.1282 \| 2959808 \| \| 0.1103 \| 9.7070 \| 2000 \| 0.1275 \| 3287584 \| \| 0.073 \| 10.6780 \| 2200 \| 0.1276 \| 3617920 \| \| 0.0808 \| 11.6489 \| 2400 \| 0.1375 \| 3945536 \| \| 0.0832 \| 12.6199 \| 2600 \| 0.1372 \| 4274560 \| \| 0.043 \| 13.5908 \| 2800 \| 0.1441 \| 4603168 \| \| 0.1192 \| 14.5617 \| 3000 \| 0.1549 \| 4932448 \| \| 0.0522 \| 15.5327 \| 3200 \| 0.1576 \| 5261312 \| \| 0.0495 \| 16.5036 \| 3400 \| 0.1620 \| 5589632 \| \| 0.0463 \| 17.4746 \| 3600 \| 0.1867 \| 5918112 \| \| 0.0316 \| 18.4455 \| 3800 \| 0.2041 \| 6246368 \| \| 0.0302 \| 19.4165 \| 4000 \| 0.2137 \| 6574848 \| \| 0.0489 \| 20.3874 \| 4200 \| 0.2474 \| 6903520 \| \| 0.0428 \| 21.3584 \| 4400 \| 0.2885 \| 7231904 \| \| 0.0092 \| 22.3293 \| 4600 \| 0.2955 \| 7561504 \| \| 0.0247 \| 23.3002 \| 4800 \| 0.3410 \| 7890912 \| \| 0.0108 \| 24.2712 \| 5000 \| 0.3752 \| 8218592 \| \| 0.003 \| 25.2421 \| 5200 \| 0.3848 \| 8548256 \| \| 0.0001 \| 26.2131 \| 5400 \| 0.4551 \| 8876704 \| \| 0.0038 \| 27.1840 \| 5600 \| 0.5005 \| 9206272 \| \| 0.0011 \| 28.1550 \| 5800 \| 0.5081 \| 9534720 \| \| 0.0186 \| 29.1259 \| 6000 \| 0.5632 \| 9864384 \| \| 0.0001 \| 30.0969 \| 6200 \| 0.6207 \| 10193376 \| \| 0.0001 \| 31.0678 \| 6400 \| 0.6485 \| 10521952 \| \| 0.0001 \| 32.0387 \| 6600 \| 0.6780 \| 10851520 \| \| 0.0 \| 33.0097 \| 6800 \| 0.7211 \| 11180544 \| \| 0.0001 \| 33.9782 \| 7000 \| 0.7425 \| 11509344 \| \| 0.0 \| 34.9492 \| 7200 \| 0.7592 \| 11838208 \| \| 0.0022 \| 35.9201 \| 7400 \| 0.7934 \| 12167872 \| \| 0.0 \| 36.8910 \| 7600 \| 0.7808 \| 12496352 \| \| 0.0 \| 37.8620 \| 7800 \| 0.8029 \| 12826048 \| \| 0.0 \| 38.8329 \| 8000 \| 0.8225 \| 13155040 \| \| 0.0003 \| 39.8039 \| 8200 \| 0.8472 \| 13483008 \| \| 0.0 \| 40.7748 \| 8400 \| 0.8385 \| 13812064 \| \| 0.0 \| 41.7458 \| 8600 \| 0.9110 \| 14140576 \| \| 0.0001 \| 42.7167 \| 8800 \| 0.8721 \| 14469248 \| \| 0.0006 \| 43.6877 \| 9000 \| 0.8889 \| 14796672 \| \| 0.0002 \| 44.6586 \| 9200 \| 0.8948 \| 15126752 \| \| 0.0042 \| 45.6295 \| 9400 \| 0.9480 \| 15456160 \| \| 0.0006 \| 46.6005 \| 9600 \| 0.8594 \| 15784928 \| \| 0.0 \| 47.5714 \| 9800 \| 0.9047 \| 16113248 \| \| 0.0 \| 48.5424 \| 10000 \| 0.8897 \| 16442496 \| \| 0.0 \| 49.5133 \| 10200 \| 0.8949 \| 16772640 \| \| 0.0 \| 50.4843 \| 10400 \| 0.8780 \| 17100000 \| \| 0.0 \| 51.4552 \| 10600 \| 0.8463 \| 17428768 \| \| 0.0 \| 52.4262 \| 10800 \| 0.8947 \| 17757344 \| \| 0.0 \| 53.3971 \| 11000 \| 0.8721 \| 18085920 \| \| 0.0 \| 54.3680 \| 11200 \| 0.8789 \| 18414336 \| \| 0.0 \| 55.3390 \| 11400 \| 0.8813 \| 18743040 \| \| 0.0 \| 56.3099 \| 11600 \| 0.8915 \| 19072928 \| \| 0.0 \| 57.2809 \| 11800 \| 0.8866 \| 19401376 \| \| 0.0 \| 58.2518 \| 12000 \| 0.8915 \| 19730336 \| \| 0.0 \| 59.2228 \| 12200 \| 0.8939 \| 20059488 \| \| 0.0 \| 60.1937 \| 12400 \| 0.8958 \| 20388064 \| \| 0.0 \| 61.1646 \| 12600 \| 0.8991 \| 20718144 \| \| 0.0 \| 62.1356 \| 12800 \| 0.9055 \| 21048224 \| \| 0.0 \| 63.1065 \| 13000 \| 0.9546 \| 21376576 \| \| 0.0029 \| 64.0775 \| 13200 \| 0.9045 \| 21706080 \| \| 0.0 \| 65.0484 \| 13400 \| 0.9358 \| 22034624 \| \| 0.0 \| 66.0194 \| 13600 \| 0.8919 \| 22364128 \| \| 0.0 \| 66.9879 \| 13800 \| 0.8877 \| 22692352 \| \| 0.0101 \| 67.9588 \| 14000 \| 0.8636 \| 23020864 \| \| 0.0 \| 68.9298 \| 14200 \| 0.9585 \| 23349920 \| \| 0.0 \| 69.9007 \| 14400 \| 0.8971 \| 23679072 \| \| 0.0 \| 70.8717 \| 14600 \| 0.8881 \| 24007776 \| \| 0.0 \| 71.8426 \| 14800 \| 0.9130 \| 24336640 \| \| 0.0 \| 72.8136 \| 15000 \| 0.9017 \| 24664576 \| \| 0.0 \| 73.7845 \| 15200 \| 0.9239 \| 24994848 \| \| 0.0 \| 74.7554 \| 15400 \| 0.9034 \| 25322720 \| \| 0.0 \| 75.7264 \| 15600 \| 0.9104 \| 25650784 \| \| 0.0013 \| 76.6973 \| 15800 \| 0.9375 \| 25980512 \| \| 0.0007 \| 77.6683 \| 16000 \| 0.9748 \| 26309536 \| \| 0.0 \| 78.6392 \| 16200 \| 0.9272 \| 26638944 \| \| 0.0 \| 79.6102 \| 16400 \| 0.9310 \| 26967360 \| \| 0.0 \| 80.5811 \| 16600 \| 0.9371 \| 27297120 \| \| 0.0 \| 81.5521 \| 16800 \| 0.9354 \| 27626144 \| \| 0.0 \| 82.5230 \| 17000 \| 0.9427 \| 27954656 \| \| 0.0 \| 83.4939 \| 17200 \| 0.9468 \| 28284160 \| \| 0.0 \| 84.4649 \| 17400 \| 0.9542 \| 28612224 \| \| 0.0 \| 85.4358 \| 17600 \| 0.9413 \| 28940448 \| \| 0.0 \| 86.4068 \| 17800 \| 0.9482 \| 29270912 \| \| 0.0 \| 87.3777 \| 18000 \| 0.9457 \| 29599424 \| \| 0.0 \| 88.3487 \| 18200 \| 0.9610 \| 29929280 \| \| 0.0 \| 89.3196 \| 18400 \| 0.9695 \| 30257504 \| \| 0.0 \| 90.2906 \| 18600 \| 0.9427 \| 30586944 \| \| 0.0 \| 91.2615 \| 18800 \| 0.9648 \| 30915744 \| \| 0.0 \| 92.2324 \| 19000 \| 0.9566 \| 31245216 \| \| 0.0 \| 93.2034 \| 19200 \| 1.0410 \| 31573600 \| \| 0.0 \| 94.1743 \| 19400 \| 1.0290 \| 31903616 \| \| 0.0 \| 95.1453 \| 19600 \| 1.0224 \| 32232032 \| \| 0.0 \| 96.1162 \| 19800 \| 1.0002 \| 32560480 \| \| 0.0 \| 97.0872 \| 20000 \| 1.0333 \| 32889696 \| \| 0.0 \| 98.0581 \| 20200 \| 0.9999 \| 33218016 \| \| 0.0 \| 99.0291 \| 20400 \| 1.0188 \| 33547296 \| \| 0.0 \| 99.9976 \| 20600 \| 1.0259 \| 33876000 \| \| 0.0 \| 100.9685 \| 20800 \| 1.0148 \| 34205376 \| \| 0.0 \| 101.9395 \| 21000 \| 1.0062 \| 34534496 \| \| 0.0 \| 102.9104 \| 21200 \| 0.9976 \| 34864000 \| \| 0.0 \| 103.8814 \| 21400 \| 1.0242 \| 35192256 \| \| 0.0 \| 104.8523 \| 21600 \| 1.0044 \| 35521376 \| \| 0.0 \| 105.8232 \| 21800 \| 1.0179 \| 35851264 \| \| 0.0 \| 106.7942 \| 22000 \| 1.0085 \| 36180000 \| \| 0.0 \| 107.7651 \| 22200 \| 1.0040 \| 36508832 \| \| 0.0 \| 108.7361 \| 22400 \| 1.0053 \| 36837600 \| \| 0.0 \| 109.7070 \| 22600 \| 0.9748 \| 37166720 \| \| 0.0 \| 110.6780 \| 22800 \| 1.0201 \| 37495520 \| \| 0.0 \| 111.6489 \| 23000 \| 1.0137 \| 37824352 \| \| 0.0 \| 112.6199 \| 23200 \| 1.0274 \| 38153856 \| \| 0.0 \| 113.5908 \| 23400 \| 1.0198 \| 38483200 \| \| 0.0 \| 114.5617 \| 23600 \| 1.0236 \| 38812672 \| \| 0.0 \| 115.5327 \| 23800 \| 1.0075 \| 39142400 \| \| 0.0 \| 116.5036 \| 24000 \| 1.0092 \| 39471200 \| \| 0.0 \| 117.4746 \| 24200 \| 1.0208 \| 39798848 \| \| 0.0 \| 118.4455 \| 24400 \| 1.0163 \| 40127360 \| \| 0.0 \| 119.4165 \| 24600 \| 1.0297 \| 40456736 \| \| 0.0 \| 120.3874 \| 24800 \| 1.0208 \| 40785312 \| \| 0.0 \| 121.3584 \| 25000 \| 1.0032 \| 41112576 \| \| 0.0 \| 122.3293 \| 25200 \| 1.0071 \| 41442112 \| \| 0.0 \| 123.3002 \| 25400 \| 1.0182 \| 41771552 \| \| 0.0 \| 124.2712 \| 25600 \| 1.0241 \| 42101248 \| \| 0.0 \| 125.2421 \| 25800 \| 0.9986 \| 42427392 \| \| 0.0 \| 126.2131 \| 26000 \| 1.0178 \| 42756704 \| \| 0.0 \| 127.1840 \| 26200 \| 1.0377 \| 43085664 \| \| 0.0 \| 128.1550 \| 26400 \| 1.0162 \| 43414240 \| \| 0.0 \| 129.1259 \| 26600 \| 1.0307 \| 43743072 \| \| 0.0 \| 130.0969 \| 26800 \| 1.0224 \| 44072768 \| \| 0.0 \| 131.0678 \| 27000 \| 1.0235 \| 44400192 \| \| 0.0 \| 132.0387 \| 27200 \| 1.0353 \| 44729632 \| \| 0.0 \| 133.0097 \| 27400 \| 1.0296 \| 45058976 \| \| 0.0 \| 133.9782 \| 27600 \| 1.0324 \| 45388352 \| \| 0.0 \| 134.9492 \| 27800 \| 1.0443 \| 45717952 \| \| 0.0 \| 135.9201 \| 28000 \| 1.0478 \| 46046144 \| \| 0.0 \| 136.8910 \| 28200 \| 1.0435 \| 46375168 \| \| 0.0 \| 137.8620 \| 28400 \| 1.0442 \| 46702816 \| \| 0.0 \| 138.8329 \| 28600 \| 1.0448 \| 47033152 \| \| 0.0 \| 139.8039 \| 28800 \| 1.0729 \| 47361472 \| \| 0.0 \| 140.7748 \| 29000 \| 1.0439 \| 47691424 \| \| 0.0 \| 141.7458 \| 29200 \| 1.0689 \| 48019712 \| \| 0.0 \| 142.7167 \| 29400 \| 1.0791 \| 48348832 \| \| 0.0 \| 143.6877 \| 29600 \| 1.0849 \| 48678560 \| \| 0.0 \| 144.6586 \| 29800 \| 1.0461 \| 49008256 \| \| 0.0 \| 145.6295 \| 30000 \| 1.0701 \| 49337088 \| \| 0.0 \| 146.6005 \| 30200 \| 1.0699 \| 49665344 \| \| 0.0 \| 147.5714 \| 30400 \| 1.0625 \| 49996128 \| \| 0.0 \| 148.5424 \| 30600 \| 1.0711 \| 50324736 \| \| 0.0 \| 149.5133 \| 30800 \| 1.0653 \| 50652864 \| \| 0.0 \| 150.4843 \| 31000 \| 1.0867 \| 50981920 \| \| 0.0 \| 151.4552 \| 31200 \| 1.0732 \| 51310752 \| \| 0.0 \| 152.4262 \| 31400 \| 1.0587 \| 51640352 \| \| 0.0 \| 153.3971 \| 31600 \| 1.0614 \| 51969184 \| \| 0.0 \| 154.3680 \| 31800 \| 1.0761 \| 52297280 \| \| 0.0 \| 155.3390 \| 32000 \| 1.0690 \| 52625600 \| \| 0.0 \| 156.3099 \| 32200 \| 1.0777 \| 52953920 \| \| 0.0 \| 157.2809 \| 32400 \| 1.0818 \| 53283648 \| \| 0.0 \| 158.2518 \| 32600 \| 1.0866 \| 53613056 \| \| 0.0 \| 159.2228 \| 32800 \| 1.0812 \| 53941632 \| \| 0.0 \| 160.1937 \| 33000 \| 1.0887 \| 54270272 \| \| 0.0 \| 161.1646 \| 33200 \| 1.0782 \| 54599104 \| \| 0.0 \| 162.1356 \| 33400 \| 1.0808 \| 54929056 \| \| 0.0 \| 163.1065 \| 33600 \| 1.0965 \| 55257728 \| \| 0.0 \| 164.0775 \| 33800 \| 1.0854 \| 55587456 \| \| 0.0 \| 165.0484 \| 34000 \| 1.0979 \| 55916576 \| \| 0.0 \| 166.0194 \| 34200 \| 1.0962 \| 56245664 \| \| 0.0 \| 166.9879 \| 34400 \| 1.1092 \| 56574272 \| \| 0.0 \| 167.9588 \| 34600 \| 1.1052 \| 56903360 \| \| 0.0 \| 168.9298 \| 34800 \| 1.1229 \| 57232032 \| \| 0.0 \| 169.9007 \| 35000 \| 1.0853 \| 57561504 \| \| 0.0 \| 170.8717 \| 35200 \| 1.1070 \| 57891168 \| \| 0.0 \| 171.8426 \| 35400 \| 1.1014 \| 58220352 \| \| 0.0 \| 172.8136 \| 35600 \| 1.1065 \| 58548960 \| \| 0.0 \| 173.7845 \| 35800 \| 1.0964 \| 58878688 \| \| 0.0 \| 174.7554 \| 36000 \| 1.0980 \| 59207104 \| \| 0.0 \| 175.7264 \| 36200 \| 1.1023 \| 59536800 \| \| 0.0 \| 176.6973 \| 36400 \| 1.0831 \| 59865312 \| \| 0.0 \| 177.6683 \| 36600 \| 1.0948 \| 60194816 \| \| 0.0 \| 178.6392 \| 36800 \| 1.1205 \| 60523584 \| \| 0.0 \| 179.6102 \| 37000 \| 1.0883 \| 60852352 \| \| 0.0 \| 180.5811 \| 37200 \| 1.0916 \| 61181024 \| \| 0.0 \| 181.5521 \| 37400 \| 1.1090 \| 61510624 \| \| 0.0 \| 182.5230 \| 37600 \| 1.1083 \| 61840672 \| \| 0.0 \| 183.4939 \| 37800 \| 1.1169 \| 62167808 \| \| 0.0 \| 184.4649 \| 38000 \| 1.1141 \| 62496960 \| \| 0.0 \| 185.4358 \| 38200 \| 1.0932 \| 62826016 \| \| 0.0 \| 186.4068 \| 38400 \| 1.1050 \| 63154784 \| \| 0.0 \| 187.3777 \| 38600 \| 1.0873 \| 63483904 \| \| 0.0 \| 188.3487 \| 38800 \| 1.1244 \| 63811808 \| \| 0.0 \| 189.3196 \| 39000 \| 1.1015 \| 64139488 \| \| 0.0 \| 190.2906 \| 39200 \| 1.0930 \| 64467808 \| \| 0.0 \| 191.2615 \| 39400 \| 1.0899 \| 64798112 \| \| 0.0 \| 192.2324 \| 39600 \| 1.0952 \| 65126304 \| \| 0.0 \| 193.2034 \| 39800 \| 1.1142 \| 65455776 \| \| 0.0 \| 194.1743 \| 40000 \| 1.1018 \| 65784064 \| ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
mlx-community/InternVL3-8B-bf16	mlx-community	"2025-04-19T11:43:56Z"	4	0	transformers	[ "transformers", "safetensors", "internvl_chat", "feature-extraction", "internvl", "custom_code", "mlx", "image-text-to-text", "conversational", "multilingual", "dataset:OpenGVLab/MMPR-v1.2", "base_model:OpenGVLab/InternVL3-1B-Instruct", "base_model:finetune:OpenGVLab/InternVL3-1B-Instruct", "license:other", "region:us" ]	image-text-to-text	"2025-04-18T12:20:43Z"	--- license: other license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE pipeline_tag: image-text-to-text library_name: transformers base_model: - OpenGVLab/InternVL3-1B-Instruct base_model_relation: finetune datasets: - OpenGVLab/MMPR-v1.2 language: - multilingual tags: - internvl - custom_code - mlx --- # mlx-community/InternVL3-8B-bf16 This model was converted to MLX format from [`models/InternVL3-8B`]() using mlx-vlm version 0.1.23. Refer to the [original model card](https://huggingface.co/models/InternVL3-8B) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/InternVL3-8B-bf16 --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image> ```
warmachine68/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-solitary_zealous_cat	warmachine68	"2025-04-19T11:42:54Z"	4	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am solitary zealous cat", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-08T22:38:25Z"	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-solitary_zealous_cat tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am solitary zealous cat - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-solitary_zealous_cat This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="warmachine68/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-solitary_zealous_cat", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.1 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF	mradermacher	"2025-04-19T11:41:26Z"	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B", "base_model:quantized:grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2025-04-19T10:55:02Z"	--- base_model: grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q4_1.gguf) \| i1-Q4_1 \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF	mradermacher	"2025-04-19T11:38:07Z"	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B", "base_model:quantized:grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T09:00:09Z"	--- base_model: grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/teapotllm-chat-GGUF	mradermacher	"2025-04-19T11:36:48Z"	0	0	transformers	[ "transformers", "gguf", "en", "base_model:teapotai/teapotllm-chat", "base_model:quantized:teapotai/teapotllm-chat", "license:mit", "endpoints_compatible", "region:us" ]	null	"2025-04-19T11:30:30Z"	--- base_model: teapotai/teapotllm-chat language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/teapotai/teapotllm-chat <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q3_K_S.gguf) \| Q3_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q4_K_S.gguf) \| Q4_K_S \| 0.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q4_K_M.gguf) \| Q4_K_M \| 0.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q5_K_S.gguf) \| Q5_K_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q5_K_M.gguf) \| Q5_K_M \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q6_K.gguf) \| Q6_K \| 0.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q8_0.gguf) \| Q8_0 \| 0.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.f16.gguf) \| f16 \| 1.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
fedovtt/a1097dee-9584-49fe-8602-d38eff229ef6	fedovtt	"2025-04-19T11:35:05Z"	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-04-19T10:02:03Z"	--- library_name: peft license: apache-2.0 base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B tags: - axolotl - generated_from_trainer model-index: - name: a1097dee-9584-49fe-8602-d38eff229ef6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 33ca191ef861beb8_train_data.json ds_type: json format: custom path: /workspace/input_data/33ca191ef861beb8_train_data.json type: field_instruction: premise field_output: hypothesis format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: fedovtt/a1097dee-9584-49fe-8602-d38eff229ef6 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/33ca191ef861beb8_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 36fecfe5-f91d-46c3-aeef-47b486bafbe0 wandb_project: 01-31 wandb_run: your_name wandb_runid: 36fecfe5-f91d-46c3-aeef-47b486bafbe0 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # a1097dee-9584-49fe-8602-d38eff229ef6 This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0082 \| 200 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
jethrowang/whisper-tiny_tat-esc_exp_nr_0.5_embed	jethrowang	"2025-04-19T11:30:36Z"	1	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:formospeech/tat_asr_aligned", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2025-04-18T10:47:51Z"	--- library_name: transformers language: - zh license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - formospeech/tat_asr_aligned model-index: - name: Whisper Tiny Taiwanese (exp_nr_0.5_embed) results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Tiny Taiwanese (exp_nr_0.5_embed) This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the TAT ASR Aligned dataset. It achieves the following results on the evaluation set: - Loss: 1.3660 - Cer: 32.6917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 681 - training_steps: 6810 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.4831 \| 0.9985 \| 681 \| 0.9359 \| 46.4492 \| \| 0.2884 \| 1.9971 \| 1362 \| 0.8960 \| 35.3926 \| \| 0.1794 \| 2.9956 \| 2043 \| 0.9427 \| 38.7004 \| \| 0.1035 \| 3.9941 \| 2724 \| 1.0423 \| 35.5451 \| \| 0.0585 \| 4.9927 \| 3405 \| 1.1355 \| 35.1530 \| \| 0.0287 \| 5.9912 \| 4086 \| 1.2120 \| 35.6566 \| \| 0.0146 \| 6.9897 \| 4767 \| 1.2845 \| 33.8159 \| \| 0.0052 \| 7.9883 \| 5448 \| 1.3243 \| 33.4713 \| \| 0.0018 \| 8.9868 \| 6129 \| 1.3504 \| 32.9579 \| \| 0.0008 \| 9.9853 \| 6810 \| 1.3660 \| 32.6917 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.0.0.post304 - Datasets 3.3.2 - Tokenizers 0.21.0
YOYO-AI/QwQ-Olympic-coder-32B	YOYO-AI	"2025-04-19T11:28:28Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2408.07990", "base_model:Qwen/QwQ-32B", "base_model:merge:Qwen/QwQ-32B", "base_model:Qwen/Qwen2.5-Coder-32B", "base_model:merge:Qwen/Qwen2.5-Coder-32B", "base_model:open-r1/OlympicCoder-32B", "base_model:merge:open-r1/OlympicCoder-32B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T11:14:10Z"	--- base_model: - Qwen/Qwen2.5-Coder-32B - Qwen/QwQ-32B - open-r1/OlympicCoder-32B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [Qwen/Qwen2.5-Coder-32B](https://huggingface.co/Qwen/Qwen2.5-Coder-32B) as a base. ### Models Merged The following models were included in the merge: * [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) * [open-r1/OlympicCoder-32B](https://huggingface.co/open-r1/OlympicCoder-32B) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: sce models: # Pivot model - model: Qwen/Qwen2.5-Coder-32B # Target models - model: Qwen/QwQ-32B - model: open-r1/OlympicCoder-32B base_model: Qwen/Qwen2.5-Coder-32B parameters: select_topk: 1 dtype: bfloat16 tokenizer_source: Qwen/QwQ-32B normalize: true int8_mask: true ```
mradermacher/Nemo-Chuckles-12B-GGUF	mradermacher	"2025-04-19T11:28:26Z"	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:spacematt/Nemo-Chuckles-12B", "base_model:quantized:spacematt/Nemo-Chuckles-12B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T09:22:20Z"	--- base_model: spacematt/Nemo-Chuckles-12B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/spacematt/Nemo-Chuckles-12B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Nemo-Chuckles-12B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q3_K_M.gguf) \| Q3_K_M \| 6.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q5_K_S.gguf) \| Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q5_K_M.gguf) \| Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q6_K.gguf) \| Q6_K \| 10.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q8_0.gguf) \| Q8_0 \| 13.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
pnle/Qwen2.5-1.5B-Open-R1-Distill	pnle	"2025-04-19T11:26:39Z"	8	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:open-r1/OpenR1-Math-220k", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-08T08:45:49Z"	--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: open-r1/OpenR1-Math-220k library_name: transformers model_name: Qwen2.5-1.5B-Open-R1-Distill tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-1.5B-Open-R1-Distill This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="pnle/Qwen2.5-1.5B-Open-R1-Distill", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/pnnnnnnn-sun-yat-sen-university/huggingface/runs/6wo0gpqn) This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.0 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ppagare/Meta-Llama-3.1-8B-Instruct-pg-chatbot-LORA	ppagare	"2025-04-19T11:23:17Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-04-18T10:11:22Z"	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ppagare - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Gemma-3-1B-Roblox-Luau-GGUF	mradermacher	"2025-04-19T11:23:08Z"	0	0	transformers	[ "transformers", "gguf", "chat", "roblox", "luau", "en", "dataset:boatbomber/roblox-info-dump", "dataset:boatbomber/the-luau-stack", "base_model:boatbomber/Gemma-3-1B-Roblox-Luau", "base_model:quantized:boatbomber/Gemma-3-1B-Roblox-Luau", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T11:11:15Z"	--- base_model: boatbomber/Gemma-3-1B-Roblox-Luau datasets: - boatbomber/roblox-info-dump - boatbomber/the-luau-stack language: - en library_name: transformers license: gemma license_link: https://ai.google.dev/gemma/terms quantized_by: mradermacher tags: - chat - roblox - luau --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/boatbomber/Gemma-3-1B-Roblox-Luau <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q3_K_S.gguf) \| Q3_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q2_K.gguf) \| Q2_K \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q4_K_S.gguf) \| Q4_K_S \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q8_0.gguf) \| Q8_0 \| 1.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.f16.gguf) \| f16 \| 2.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-3	Shaleen123	"2025-04-19T11:22:49Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T11:16:58Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/Llama_3.x_70b_Triads_V7-GGUF	mradermacher	"2025-04-19T11:19:59Z"	19	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:NexesMess/Llama_3.x_70b_Triads_V7", "base_model:quantized:NexesMess/Llama_3.x_70b_Triads_V7", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-03-14T10:04:24Z"	--- base_model: NexesMess/Llama_3.x_70b_Triads_V7 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/NexesMess/Llama_3.x_70b_Triads_V7 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q5_K_M.gguf) \| Q5_K_M \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
RioShiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2	RioShiina	"2025-04-19T11:17:30Z"	0	0	null	[ "ja", "base_model:abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1", "base_model:quantized:abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1", "license:apache-2.0", "region:us" ]	null	"2025-04-19T11:17:28Z"	--- license: apache-2.0 base_model: abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1 base_model_relation: quantized language: - ja --- Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.2.8">turboderp's ExLlamaV2 v0.2.8</a> for quantization. [2.2bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/2.2bpw) [3.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/3.0bpw) [4.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/4.0bpw) [5.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/5.0bpw) [6.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/6.0bpw) [7.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/7.0bpw) [8.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/8.0bpw) ## Calibration Dataset [TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm) ## ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2 - Model creator: [abeja](https://huggingface.co/abeja) - Original model: [ABEJA-Qwen2.5-7b-Japanese-v0.1](https://huggingface.co/abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1)
rbelanec/train_qqp_1744902597	rbelanec	"2025-04-19T11:17:03Z"	0	0	peft	[ "peft", "safetensors", "llama-factory", "prompt-tuning", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	"2025-04-17T15:18:48Z"	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - llama-factory - prompt-tuning - generated_from_trainer model-index: - name: train_qqp_1744902597 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # train_qqp_1744902597 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the qqp dataset. It achieves the following results on the evaluation set: - Loss: 0.2268 - Num Input Tokens Seen: 49022016 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.3 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:-----------------:\| \| 0.3195 \| 0.0098 \| 200 \| 0.3106 \| 245536 \| \| 0.2888 \| 0.0195 \| 400 \| 0.3139 \| 489696 \| \| 0.256 \| 0.0293 \| 600 \| 0.2695 \| 737824 \| \| 0.2961 \| 0.0391 \| 800 \| 0.2602 \| 981856 \| \| 0.2767 \| 0.0489 \| 1000 \| 0.2627 \| 1225952 \| \| 0.3652 \| 0.0586 \| 1200 \| 0.3079 \| 1469920 \| \| 0.285 \| 0.0684 \| 1400 \| 0.2651 \| 1715360 \| \| 0.2802 \| 0.0782 \| 1600 \| 0.2552 \| 1961952 \| \| 0.2617 \| 0.0879 \| 1800 \| 0.2957 \| 2205952 \| \| 0.3155 \| 0.0977 \| 2000 \| 0.2709 \| 2453792 \| \| 0.3359 \| 0.1075 \| 2200 \| 0.2968 \| 2698976 \| \| 0.2723 \| 0.1173 \| 2400 \| 0.2812 \| 2944000 \| \| 0.3139 \| 0.1270 \| 2600 \| 0.2977 \| 3190496 \| \| 0.228 \| 0.1368 \| 2800 \| 0.2626 \| 3439104 \| \| 0.2619 \| 0.1466 \| 3000 \| 0.2564 \| 3684640 \| \| 0.3061 \| 0.1564 \| 3200 \| 0.2572 \| 3931744 \| \| 0.2571 \| 0.1661 \| 3400 \| 0.2869 \| 4179680 \| \| 0.2624 \| 0.1759 \| 3600 \| 0.2561 \| 4424000 \| \| 0.2622 \| 0.1857 \| 3800 \| 0.2517 \| 4667488 \| \| 0.2741 \| 0.1954 \| 4000 \| 0.2555 \| 4910752 \| \| 0.2699 \| 0.2052 \| 4200 \| 0.2509 \| 5157152 \| \| 0.2502 \| 0.2150 \| 4400 \| 0.2667 \| 5403360 \| \| 0.2635 \| 0.2248 \| 4600 \| 0.2509 \| 5647360 \| \| 0.2642 \| 0.2345 \| 4800 \| 0.2685 \| 5889632 \| \| 0.2264 \| 0.2443 \| 5000 \| 0.2568 \| 6135424 \| \| 0.2425 \| 0.2541 \| 5200 \| 0.2495 \| 6380320 \| \| 0.2447 \| 0.2638 \| 5400 \| 0.2495 \| 6627360 \| \| 0.2926 \| 0.2736 \| 5600 \| 0.2547 \| 6873760 \| \| 0.3135 \| 0.2834 \| 5800 \| 0.2659 \| 7121504 \| \| 0.2274 \| 0.2932 \| 6000 \| 0.2668 \| 7366208 \| \| 0.2814 \| 0.3029 \| 6200 \| 0.2612 \| 7615264 \| \| 0.2345 \| 0.3127 \| 6400 \| 0.2557 \| 7860128 \| \| 0.2643 \| 0.3225 \| 6600 \| 0.2545 \| 8103360 \| \| 0.2393 \| 0.3323 \| 6800 \| 0.2910 \| 8350976 \| \| 0.2462 \| 0.3420 \| 7000 \| 0.2714 \| 8597664 \| \| 0.2514 \| 0.3518 \| 7200 \| 0.2714 \| 8842400 \| \| 0.2713 \| 0.3616 \| 7400 \| 0.2500 \| 9087456 \| \| 0.358 \| 0.3713 \| 7600 \| 0.2564 \| 9331520 \| \| 0.2397 \| 0.3811 \| 7800 \| 0.2481 \| 9576704 \| \| 0.2357 \| 0.3909 \| 8000 \| 0.2521 \| 9819200 \| \| 0.2608 \| 0.4007 \| 8200 \| 0.2518 \| 10064928 \| \| 0.2234 \| 0.4104 \| 8400 \| 0.2646 \| 10308768 \| \| 0.272 \| 0.4202 \| 8600 \| 0.2483 \| 10551296 \| \| 0.2518 \| 0.4300 \| 8800 \| 0.2720 \| 10798144 \| \| 0.242 \| 0.4397 \| 9000 \| 0.2520 \| 11047776 \| \| 0.2411 \| 0.4495 \| 9200 \| 0.2625 \| 11292384 \| \| 0.2778 \| 0.4593 \| 9400 \| 0.2483 \| 11534944 \| \| 0.2841 \| 0.4691 \| 9600 \| 0.3225 \| 11778880 \| \| 0.2869 \| 0.4788 \| 9800 \| 0.2492 \| 12025472 \| \| 0.2562 \| 0.4886 \| 10000 \| 0.2491 \| 12267968 \| \| 0.245 \| 0.4984 \| 10200 \| 0.2480 \| 12511488 \| \| 0.2534 \| 0.5081 \| 10400 \| 0.2492 \| 12755904 \| \| 0.2657 \| 0.5179 \| 10600 \| 0.2542 \| 13002048 \| \| 0.2525 \| 0.5277 \| 10800 \| 0.2583 \| 13246272 \| \| 0.2363 \| 0.5375 \| 11000 \| 0.2486 \| 13491456 \| \| 0.2056 \| 0.5472 \| 11200 \| 0.2683 \| 13735936 \| \| 0.2512 \| 0.5570 \| 11400 \| 0.2502 \| 13982176 \| \| 0.2496 \| 0.5668 \| 11600 \| 0.2485 \| 14227136 \| \| 0.2499 \| 0.5766 \| 11800 \| 0.2652 \| 14472704 \| \| 0.221 \| 0.5863 \| 12000 \| 0.2578 \| 14717856 \| \| 0.3302 \| 0.5961 \| 12200 \| 0.2479 \| 14963520 \| \| 0.2619 \| 0.6059 \| 12400 \| 0.2624 \| 15208224 \| \| 0.2512 \| 0.6156 \| 12600 \| 0.2487 \| 15453408 \| \| 0.2312 \| 0.6254 \| 12800 \| 0.2576 \| 15698016 \| \| 0.2569 \| 0.6352 \| 13000 \| 0.2521 \| 15942720 \| \| 0.252 \| 0.6450 \| 13200 \| 0.2469 \| 16186528 \| \| 0.2567 \| 0.6547 \| 13400 \| 0.2460 \| 16433472 \| \| 0.2417 \| 0.6645 \| 13600 \| 0.2502 \| 16679360 \| \| 0.2363 \| 0.6743 \| 13800 \| 0.2536 \| 16924896 \| \| 0.2023 \| 0.6840 \| 14000 \| 0.2571 \| 17171072 \| \| 0.2533 \| 0.6938 \| 14200 \| 0.2453 \| 17416704 \| \| 0.2489 \| 0.7036 \| 14400 \| 0.2610 \| 17663488 \| \| 0.2735 \| 0.7134 \| 14600 \| 0.2442 \| 17910272 \| \| 0.2151 \| 0.7231 \| 14800 \| 0.2596 \| 18151712 \| \| 0.2568 \| 0.7329 \| 15000 \| 0.2432 \| 18395744 \| \| 0.2308 \| 0.7427 \| 15200 \| 0.2456 \| 18642368 \| \| 0.2532 \| 0.7524 \| 15400 \| 0.2430 \| 18889312 \| \| 0.2515 \| 0.7622 \| 15600 \| 0.2442 \| 19133312 \| \| 0.2491 \| 0.7720 \| 15800 \| 0.2443 \| 19376992 \| \| 0.2613 \| 0.7818 \| 16000 \| 0.2443 \| 19620672 \| \| 0.2467 \| 0.7915 \| 16200 \| 0.2485 \| 19866240 \| \| 0.2548 \| 0.8013 \| 16400 \| 0.2481 \| 20112160 \| \| 0.2564 \| 0.8111 \| 16600 \| 0.2450 \| 20358464 \| \| 0.2713 \| 0.8209 \| 16800 \| 0.2460 \| 20602112 \| \| 0.2777 \| 0.8306 \| 17000 \| 0.2397 \| 20845696 \| \| 0.2217 \| 0.8404 \| 17200 \| 0.2408 \| 21089792 \| \| 0.251 \| 0.8502 \| 17400 \| 0.2454 \| 21334176 \| \| 0.1997 \| 0.8599 \| 17600 \| 0.2507 \| 21577600 \| \| 0.2264 \| 0.8697 \| 17800 \| 0.2566 \| 21822848 \| \| 0.2631 \| 0.8795 \| 18000 \| 0.2381 \| 22067296 \| \| 0.2491 \| 0.8893 \| 18200 \| 0.2405 \| 22313824 \| \| 0.2151 \| 0.8990 \| 18400 \| 0.2373 \| 22558912 \| \| 0.2593 \| 0.9088 \| 18600 \| 0.2564 \| 22803456 \| \| 0.2586 \| 0.9186 \| 18800 \| 0.2396 \| 23047552 \| \| 0.2396 \| 0.9283 \| 19000 \| 0.2389 \| 23293856 \| \| 0.2385 \| 0.9381 \| 19200 \| 0.2390 \| 23539488 \| \| 0.2906 \| 0.9479 \| 19400 \| 0.2493 \| 23786464 \| \| 0.2623 \| 0.9577 \| 19600 \| 0.2394 \| 24032064 \| \| 0.2404 \| 0.9674 \| 19800 \| 0.2371 \| 24278464 \| \| 0.2486 \| 0.9772 \| 20000 \| 0.2393 \| 24521632 \| \| 0.2454 \| 0.9870 \| 20200 \| 0.2435 \| 24765600 \| \| 0.2408 \| 0.9968 \| 20400 \| 0.2354 \| 25007520 \| \| 0.2772 \| 1.0065 \| 20600 \| 0.2488 \| 25253920 \| \| 0.28 \| 1.0163 \| 20800 \| 0.2345 \| 25498432 \| \| 0.2189 \| 1.0261 \| 21000 \| 0.2350 \| 25745120 \| \| 0.2038 \| 1.0359 \| 21200 \| 0.2462 \| 25989952 \| \| 0.2399 \| 1.0456 \| 21400 \| 0.2449 \| 26234080 \| \| 0.2399 \| 1.0554 \| 21600 \| 0.2423 \| 26482784 \| \| 0.2106 \| 1.0652 \| 21800 \| 0.2333 \| 26728608 \| \| 0.194 \| 1.0750 \| 22000 \| 0.2438 \| 26977792 \| \| 0.2419 \| 1.0847 \| 22200 \| 0.2328 \| 27218080 \| \| 0.2607 \| 1.0945 \| 22400 \| 0.2352 \| 27463456 \| \| 0.2204 \| 1.1043 \| 22600 \| 0.2364 \| 27708832 \| \| 0.2387 \| 1.1140 \| 22800 \| 0.2334 \| 27956000 \| \| 0.2512 \| 1.1238 \| 23000 \| 0.2327 \| 28204704 \| \| 0.2076 \| 1.1336 \| 23200 \| 0.2332 \| 28452992 \| \| 0.2111 \| 1.1434 \| 23400 \| 0.2331 \| 28696640 \| \| 0.2251 \| 1.1531 \| 23600 \| 0.2315 \| 28937792 \| \| 0.2526 \| 1.1629 \| 23800 \| 0.2321 \| 29186016 \| \| 0.2118 \| 1.1727 \| 24000 \| 0.2375 \| 29431872 \| \| 0.253 \| 1.1824 \| 24200 \| 0.2321 \| 29673216 \| \| 0.2585 \| 1.1922 \| 24400 \| 0.2326 \| 29916864 \| \| 0.2545 \| 1.2020 \| 24600 \| 0.2308 \| 30163136 \| \| 0.2238 \| 1.2118 \| 24800 \| 0.2309 \| 30405920 \| \| 0.2053 \| 1.2215 \| 25000 \| 0.2311 \| 30652960 \| \| 0.2021 \| 1.2313 \| 25200 \| 0.2308 \| 30897184 \| \| 0.2241 \| 1.2411 \| 25400 \| 0.2310 \| 31141248 \| \| 0.2576 \| 1.2508 \| 25600 \| 0.2547 \| 31385376 \| \| 0.1997 \| 1.2606 \| 25800 \| 0.2426 \| 31630880 \| \| 0.2535 \| 1.2704 \| 26000 \| 0.2305 \| 31876320 \| \| 0.2404 \| 1.2802 \| 26200 \| 0.2330 \| 32120640 \| \| 0.2961 \| 1.2899 \| 26400 \| 0.2408 \| 32365056 \| \| 0.2544 \| 1.2997 \| 26600 \| 0.2298 \| 32611072 \| \| 0.2309 \| 1.3095 \| 26800 \| 0.2332 \| 32855648 \| \| 0.2574 \| 1.3193 \| 27000 \| 0.2293 \| 33097440 \| \| 0.2147 \| 1.3290 \| 27200 \| 0.2324 \| 33342208 \| \| 0.2189 \| 1.3388 \| 27400 \| 0.2373 \| 33587968 \| \| 0.2572 \| 1.3486 \| 27600 \| 0.2337 \| 33831872 \| \| 0.2355 \| 1.3583 \| 27800 \| 0.2298 \| 34076864 \| \| 0.19 \| 1.3681 \| 28000 \| 0.2303 \| 34319616 \| \| 0.2344 \| 1.3779 \| 28200 \| 0.2296 \| 34563968 \| \| 0.2514 \| 1.3877 \| 28400 \| 0.2300 \| 34808704 \| \| 0.2254 \| 1.3974 \| 28600 \| 0.2340 \| 35054656 \| \| 0.2162 \| 1.4072 \| 28800 \| 0.2293 \| 35297248 \| \| 0.2615 \| 1.4170 \| 29000 \| 0.2393 \| 35543232 \| \| 0.2474 \| 1.4267 \| 29200 \| 0.2354 \| 35787200 \| \| 0.2404 \| 1.4365 \| 29400 \| 0.2288 \| 36033344 \| \| 0.2282 \| 1.4463 \| 29600 \| 0.2294 \| 36277664 \| \| 0.2092 \| 1.4561 \| 29800 \| 0.2296 \| 36522912 \| \| 0.2429 \| 1.4658 \| 30000 \| 0.2293 \| 36766912 \| \| 0.2493 \| 1.4756 \| 30200 \| 0.2291 \| 37010880 \| \| 0.1932 \| 1.4854 \| 30400 \| 0.2317 \| 37255808 \| \| 0.2201 \| 1.4952 \| 30600 \| 0.2287 \| 37500256 \| \| 0.2086 \| 1.5049 \| 30800 \| 0.2282 \| 37744128 \| \| 0.2215 \| 1.5147 \| 31000 \| 0.2332 \| 37989600 \| \| 0.2221 \| 1.5245 \| 31200 \| 0.2286 \| 38233760 \| \| 0.202 \| 1.5342 \| 31400 \| 0.2285 \| 38480384 \| \| 0.2619 \| 1.5440 \| 31600 \| 0.2291 \| 38728448 \| \| 0.2256 \| 1.5538 \| 31800 \| 0.2300 \| 38975296 \| \| 0.2269 \| 1.5636 \| 32000 \| 0.2287 \| 39221728 \| \| 0.2851 \| 1.5733 \| 32200 \| 0.2288 \| 39465280 \| \| 0.2444 \| 1.5831 \| 32400 \| 0.2297 \| 39712992 \| \| 0.2923 \| 1.5929 \| 32600 \| 0.2279 \| 39960032 \| \| 0.221 \| 1.6026 \| 32800 \| 0.2297 \| 40206624 \| \| 0.225 \| 1.6124 \| 33000 \| 0.2322 \| 40449856 \| \| 0.2395 \| 1.6222 \| 33200 \| 0.2280 \| 40693312 \| \| 0.2141 \| 1.6320 \| 33400 \| 0.2278 \| 40936672 \| \| 0.2278 \| 1.6417 \| 33600 \| 0.2273 \| 41180480 \| \| 0.2443 \| 1.6515 \| 33800 \| 0.2271 \| 41422272 \| \| 0.2371 \| 1.6613 \| 34000 \| 0.2290 \| 41666752 \| \| 0.198 \| 1.6710 \| 34200 \| 0.2277 \| 41912096 \| \| 0.2219 \| 1.6808 \| 34400 \| 0.2272 \| 42157856 \| \| 0.2452 \| 1.6906 \| 34600 \| 0.2273 \| 42402496 \| \| 0.2011 \| 1.7004 \| 34800 \| 0.2276 \| 42645088 \| \| 0.2132 \| 1.7101 \| 35000 \| 0.2270 \| 42889536 \| \| 0.2117 \| 1.7199 \| 35200 \| 0.2285 \| 43134208 \| \| 0.2199 \| 1.7297 \| 35400 \| 0.2273 \| 43377824 \| \| 0.1868 \| 1.7395 \| 35600 \| 0.2285 \| 43623232 \| \| 0.2633 \| 1.7492 \| 35800 \| 0.2272 \| 43872416 \| \| 0.2165 \| 1.7590 \| 36000 \| 0.2280 \| 44117632 \| \| 0.2574 \| 1.7688 \| 36200 \| 0.2271 \| 44363488 \| \| 0.2696 \| 1.7785 \| 36400 \| 0.2268 \| 44608000 \| \| 0.2385 \| 1.7883 \| 36600 \| 0.2271 \| 44852672 \| \| 0.2374 \| 1.7981 \| 36800 \| 0.2269 \| 45098144 \| \| 0.216 \| 1.8079 \| 37000 \| 0.2276 \| 45342912 \| \| 0.1787 \| 1.8176 \| 37200 \| 0.2269 \| 45590720 \| \| 0.1936 \| 1.8274 \| 37400 \| 0.2271 \| 45835200 \| \| 0.2439 \| 1.8372 \| 37600 \| 0.2272 \| 46079328 \| \| 0.2397 \| 1.8469 \| 37800 \| 0.2272 \| 46322496 \| \| 0.2493 \| 1.8567 \| 38000 \| 0.2268 \| 46565536 \| \| 0.2368 \| 1.8665 \| 38200 \| 0.2268 \| 46809376 \| \| 0.2552 \| 1.8763 \| 38400 \| 0.2269 \| 47052352 \| \| 0.2151 \| 1.8860 \| 38600 \| 0.2269 \| 47298816 \| \| 0.188 \| 1.8958 \| 38800 \| 0.2270 \| 47547712 \| \| 0.2345 \| 1.9056 \| 39000 \| 0.2272 \| 47794048 \| \| 0.2166 \| 1.9153 \| 39200 \| 0.2270 \| 48039872 \| \| 0.2361 \| 1.9251 \| 39400 \| 0.2271 \| 48286368 \| \| 0.2186 \| 1.9349 \| 39600 \| 0.2271 \| 48530880 \| \| 0.2108 \| 1.9447 \| 39800 \| 0.2270 \| 48774656 \| \| 0.2436 \| 1.9544 \| 40000 \| 0.2270 \| 49022016 \| ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
mlfoundations-dev/b1_science_top_2_10k	mlfoundations-dev	"2025-04-19T11:16:34Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T04:45:07Z"	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b1_science_top_2_10k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # b1_science_top_2_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b1_science_top_2_10k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
rehatr/chan	rehatr	"2025-04-19T11:15:50Z"	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-04-19T10:49:53Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: chan --- # Chan <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `chan` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "chan", "lora_weights": "https://huggingface.co/rehatr/chan/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('rehatr/chan', weight_name='lora.safetensors') image = pipeline('chan').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/rehatr/chan/discussions) to add images that show off what you’ve made with this LoRA.
aleegis/9d59f849-08fd-4dd8-9b12-765a10cae01d	aleegis	"2025-04-19T11:15:22Z"	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:upstage/SOLAR-10.7B-Instruct-v1.0", "base_model:adapter:upstage/SOLAR-10.7B-Instruct-v1.0", "license:cc-by-nc-4.0", "region:us" ]	null	"2025-04-19T09:15:38Z"	--- library_name: peft license: cc-by-nc-4.0 base_model: upstage/SOLAR-10.7B-Instruct-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: 9d59f849-08fd-4dd8-9b12-765a10cae01d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: upstage/SOLAR-10.7B-Instruct-v1.0 bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - fc41e3171106b27b_train_data.json ds_type: json format: custom path: /workspace/input_data/fc41e3171106b27b_train_data.json type: field_instruction: source field_output: good-translation format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: aleegis/9d59f849-08fd-4dd8-9b12-765a10cae01d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: null lora_alpha: 32 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true loraplus_lr_embedding: 1.0e-06 loraplus_lr_ratio: 16 lr_scheduler: cosine max_grad_norm: 1 max_steps: 1500 micro_batch_size: 8 mlflow_experiment_name: /tmp/fc41e3171106b27b_train_data.json model_type: AutoModelForCausalLM num_epochs: 200 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null save_total_limit: 10 saves_per_epoch: 0 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.0 wandb_entity: null wandb_mode: online wandb_name: ec623392-2dd6-4ede-83e3-b5ca8a66c621 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: ec623392-2dd6-4ede-83e3-b5ca8a66c621 warmup_steps: 100 weight_decay: 0 xformers_attention: null ``` </details><br> # 9d59f849-08fd-4dd8-9b12-765a10cae01d This model is a fine-tuned version of [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1500 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
jyc0325/Qwen2.5-1.5B-Instruct-SFT-code	jyc0325	"2025-04-19T11:13:28Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:open-r1/codeforces-cots", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T00:39:44Z"	--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: open-r1/codeforces-cots library_name: transformers model_name: Qwen2.5-1.5B-Instruct-SFT-code tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-SFT-code This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/codeforces-cots](https://huggingface.co/datasets/open-r1/codeforces-cots) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jyc0325/Qwen2.5-1.5B-Instruct-SFT-code", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/davidcho2356-purdue-university/huggingface/runs/xuwfbac1) This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
dekos2606/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-beaked_aquatic_dove	dekos2606	"2025-04-19T11:13:06Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am beaked aquatic dove", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T11:11:51Z"	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-beaked_aquatic_dove tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am beaked aquatic dove - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-beaked_aquatic_dove This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dekos2606/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-beaked_aquatic_dove", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ushnaiffath/gita-text-generation-gpt2	ushnaiffath	"2025-04-19T11:11:51Z"	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T11:11:29Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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TOMFORD79/Cake_7	TOMFORD79	"2025-04-19T11:09:59Z"	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	"2025-04-19T10:56:37Z"	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
TOMFORD79/Cake_6	TOMFORD79	"2025-04-19T11:09:43Z"	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	"2025-04-19T10:56:31Z"	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf	RichardErkhov	"2025-04-19T11:07:22Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T09:32:59Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3 - GGUF - Model creator: https://huggingface.co/yjwon/ - Original model: https://huggingface.co/yjwon/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q2_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q2_K.gguf) \| Q2_K \| 2.54GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_XS.gguf) \| IQ3_XS \| 2.82GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_S.gguf) \| IQ3_S \| 2.97GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ4_XS.gguf) \| IQ4_XS \| 3.68GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_1.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_0.gguf) \| Q5_0 \| 4.66GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K_S.gguf) \| Q5_K_S \| 4.66GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_1.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q6_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q6_K.gguf) \| Q6_K \| 5.54GB \| \| [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q8_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Raniahossam33/qwen2.5-7b-instruct-ditto-Tunisia-food-sap1-custom	Raniahossam33	"2025-04-19T11:03:17Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-03-21T00:55:05Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dzanbek/cfd34c94-17a4-4d2b-a13b-ec0e25df2cab	dzanbek	"2025-04-19T11:02:45Z"	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-04-19T10:58:12Z"	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: cfd34c94-17a4-4d2b-a13b-ec0e25df2cab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Llama-3.2-1B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1f97354293835a5f_train_data.json ds_type: json format: custom path: /workspace/input_data/1f97354293835a5f_train_data.json type: field_instruction: prompt field_output: init_response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/cfd34c94-17a4-4d2b-a13b-ec0e25df2cab hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/1f97354293835a5f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 40576177-a321-4699-bf15-9122727da99d wandb_project: 01-31 wandb_run: your_name wandb_runid: 40576177-a321-4699-bf15-9122727da99d warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # cfd34c94-17a4-4d2b-a13b-ec0e25df2cab This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0288 \| 150 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nicksedov/rubert-tiny2-classifier	nicksedov	"2025-04-19T10:58:24Z"	21	0	null	[ "safetensors", "bert", "text-classification", "ru", "base_model:cointegrated/rubert-tiny2", "base_model:finetune:cointegrated/rubert-tiny2", "license:mit", "region:us" ]	text-classification	"2025-04-09T17:47:48Z"	--- license: mit language: - ru base_model: - cointegrated/rubert-tiny2 pipeline_tag: text-classification --- Модель бинарной классификации текстов на русском языке. Получает на вход текст запроса и определяет, содержится ли в нем просьба сгенерировать изображение.
itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF	itlwas	"2025-04-19T10:58:01Z"	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "hiber-multi", "safetensors", "Llama3.1", "multilingual-llm", "instruction-tuning", "flash-attention2", "quantization", "llama-cpp", "gguf-my-repo", "text-generation", "en", "zh", "es", "fr", "de", "ja", "ko", "ru", "base_model:Hibernates/Hiber-Multi-10B-Instruct", "base_model:quantized:Hibernates/Hiber-Multi-10B-Instruct", "license:llama3.1", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2025-04-19T10:57:35Z"	--- base_model: Hibernates/Hiber-Multi-10B-Instruct language: - en - zh - es - fr - de - ja - ko - ru library_name: transformers license: llama3.1 pipeline_tag: text-generation tags: - text-generation-inference - hiber-multi - safetensors - Llama3.1 - multilingual-llm - instruction-tuning - flash-attention2 - quantization - llama-cpp - gguf-my-repo --- # itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Hibernates/Hiber-Multi-10B-Instruct`](https://huggingface.co/Hibernates/Hiber-Multi-10B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Hibernates/Hiber-Multi-10B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF --hf-file hiber-multi-10b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF --hf-file hiber-multi-10b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF --hf-file hiber-multi-10b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF --hf-file hiber-multi-10b-instruct-q4_k_m.gguf -c 2048 ```
unsloth/DeepSeek-V3-0324-BF16	unsloth	"2025-04-19T10:57:32Z"	113	2	transformers	[ "transformers", "safetensors", "deepseek_v3", "text-generation", "deepseek", "unsloth", "conversational", "custom_code", "en", "arxiv:2412.19437", "base_model:deepseek-ai/DeepSeek-V3-0324", "base_model:quantized:deepseek-ai/DeepSeek-V3-0324", "license:mit", "autotrain_compatible", "endpoints_compatible", "fp8", "region:us" ]	text-generation	"2025-03-25T00:34:56Z"	--- base_model: deepseek-ai/DeepSeek-V3-0324 language: - en library_name: transformers license: mit tags: - deepseek_v3 - deepseek - unsloth - transformers --- # DeepSeek-V3-0324 <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V3-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> ## Features DeepSeek-V3-0324 demonstrates notable improvements over its predecessor, DeepSeek-V3, in several key aspects. ![Model Performance](figures/0324_comparison.png) ### Reasoning Capabilities - Significant improvements in benchmark performance: - MMLU-Pro: 75.9 → 81.2 (+5.3) - GPQA: 59.1 → 68.4 (+9.3) - AIME: 39.6 → 59.4 (+19.8) - LiveCodeBench: 39.2 → 49.2 (+10.0) ### Front-End Web Development - Improved the executability of the code - More aesthetically pleasing web pages and game front-ends ### Chinese Writing Proficiency - Enhanced style and content quality: - Aligned with the R1 writing style - Better quality in medium-to-long-form writing - Feature Enhancements - Improved multi-turn interactive rewriting - Optimized translation quality and letter writing ### Chinese Search Capabilities - Enhanced report analysis requests with more detailed outputs ### Function Calling Improvements - Increased accuracy in Function Calling, fixing issues from previous V3 versions --- ## Usage Recommendations ### System Prompt In the official DeepSeek web/app, we use the same system prompt with a specific date. ``` 该助手为DeepSeek Chat，由深度求索公司创造。今天是{current date}。 ``` For example, ``` 该助手为DeepSeek Chat，由深度求索公司创造。今天是3月24日，星期一。 ``` ### Temperature In our web and application environments, the temperature parameter $T_{model}$ is set to 0.3. Because many users use the default temperature 1.0 in API call, we have implemented an API temperature $T_{api}$ mapping mechanism that adjusts the input API temperature value of 1.0 to the most suitable model temperature setting of 0.3. $$ T_{model} = T_{api} \times 0.3 \quad (0 \leq T_{api} \leq 1) $$ $$ T_{model} = T_{api} - 0.7 \quad (1 < T_{api} \leq 2) $$ Thus, if you call V3 via API, temperature 1.0 equals to the model temperature 0.3. ### Prompts for File Uploading and Web Search For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments. ``` file_template = \ """[file name]: {file_name} [file content begin] {file_content} [file content end] {question}""" ``` For Web Search, {search_results}, {cur_date}, and {question} are arguments. For Chinese query, we use the prompt: ``` search_answer_zh_template = \ '''# 以下内容是基于用户发送的消息的搜索结果: {search_results} 在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。在回答时，请注意以下几点： - 今天是{cur_date}。 - 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。 - 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。 - 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。 - 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。 - 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。 - 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。 - 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。 - 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。 # 用户消息为： {question}''' ``` For English query, we use the prompt: ``` search_answer_en_template = \ '''# The following contents are the search results related to the user's message: {search_results} In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer. When responding, please keep the following points in mind: - Today is {cur_date}. - Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question. - For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary. - For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough. - If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content. - For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content. - Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability. - Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage. - Unless the user requests otherwise, your response should be in the same language as the user's question. # The user's message is: {question}''' ``` ## How to Run Locally The model structure of DeepSeek-V3-0324 is exactly the same as DeepSeek-V3. Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running this model locally. This model supports features such as function calling, JSON output, and FIM completion. For instructions on how to construct prompts to use these features, please refer to [DeepSeek-V2.5](https://huggingface.co/deepseek-ai/DeepSeek-V2.5#function-calling) repo. NOTE: Hugging Face's Transformers has not been directly supported yet. ## License This repository and the model weights are licensed under the [MIT License](LICENSE). ## Citation ``` @misc{deepseekai2024deepseekv3technicalreport, title={DeepSeek-V3 Technical Report}, author={DeepSeek-AI}, year={2024}, eprint={2412.19437}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.19437}, } ``` ## Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
unsloth/DeepSeek-V3-bf16	unsloth	"2025-04-19T10:55:22Z"	505	16	transformers	[ "transformers", "safetensors", "deepseek_v3", "text-generation", "deepseek", "unsloth", "conversational", "custom_code", "en", "arxiv:2412.19437", "base_model:deepseek-ai/DeepSeek-V3", "base_model:quantized:deepseek-ai/DeepSeek-V3", "license:mit", "autotrain_compatible", "endpoints_compatible", "fp8", "region:us" ]	text-generation	"2025-01-06T09:27:41Z"	--- base_model: deepseek-ai/DeepSeek-V3 language: - en library_name: transformers license: mit tags: - deepseek_v3 - deepseek - unsloth - transformers --- ## *See [our collection](https://huggingface.co/collections/unsloth/deepseek-v3-all-versions-677cf5cfd7df8b7815fc723c) for versions of Deepseek V3 including GGUF, bf16 and original formats.* # Finetune Llama 3.3, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth! We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) # unsloth/DeepSeek-V3-bf16 For more details on the model, please go to Deepseek's original [model card](https://huggingface.co/deepseek-ai/DeepSeek-V3) ## ✨ Finetune for Free All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. \| Unsloth supports \| Free Notebooks \| Performance \| Memory use \| \|-----------------\|--------------------------------------------------------------------------------------------------------------------------\|-------------\|----------\| \| Llama-3.2 (3B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) \| 2.4x faster \| 58% less \| \| Llama-3.2 (11B vision) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) \| 2x faster \| 60% less \| \| Qwen2 VL (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb) \| 1.8x faster \| 60% less \| \| Qwen2.5 (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) \| 2x faster \| 60% less \| \| Llama-3.1 (8B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb) \| 2.4x faster \| 58% less \| \| Phi-3.5 (mini) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb) \| 2x faster \| 50% less \| \| Gemma 2 (9B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb) \| 2.4x faster \| 58% less \| \| Mistral (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) \| 2.2x faster \| 62% less \| [<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="200"/>](https://docs.unsloth.ai) - This [Llama 3.2 conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_(7B)-Text_Completion.ipynb) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. ## Special Thanks A huge thank you to the Deepseek team for creating and releasing these models. ## Model Information We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. ## 2. Model Summary --- Architecture: Innovative Load Balancing Strategy and Training Objective - On top of the efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which minimizes the performance degradation that arises from encouraging load balancing. - We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model performance. It can also be used for speculative decoding for inference acceleration. --- Pre-Training: Towards Ultimate Training Efficiency - We design an FP8 mixed precision training framework and, for the first time, validate the feasibility and effectiveness of FP8 training on an extremely large-scale model. - Through co-design of algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, nearly achieving full computation-communication overlap. This significantly enhances our training efficiency and reduces the training costs, enabling us to further scale up the model size without additional overhead. - At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the currently strongest open-source base model. The subsequent training stages after pre-training require only 0.1M GPU hours. --- Post-Training: Knowledge Distillation from DeepSeek-R1 - We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and notably improves its reasoning performance. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3. --- ## 3. Model Downloads <div align="center"> \| Model \| #Total Params \| #Activated Params \| Context Length \| Download \| \| :------------: \| :------------: \| :------------: \| :------------: \| :------------: \| \| DeepSeek-V3-Base \| 671B \| 37B \| 128K \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base) \| \| DeepSeek-V3 \| 671B \| 37B \| 128K \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3) \| </div> NOTE: The total size of DeepSeek-V3 models on HuggingFace is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: [How_to Run_Locally](#6-how-to-run-locally). For developers looking to dive deeper, we recommend exploring [README_WEIGHTS.md](./README_WEIGHTS.md) for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is currently under active development within the community, and we welcome your contributions and feedback. ## 4. Evaluation Results ### Base Model #### Standard Benchmarks <div align="center"> \| \| Benchmark (Metric) \| # Shots \| DeepSeek-V2 \| Qwen2.5 72B \| LLaMA3.1 405B \| DeepSeek-V3 \| \|---\|-------------------\|----------\|--------\|-------------\|---------------\|---------\| \| \| Architecture \| - \| MoE \| Dense \| Dense \| MoE \| \| \| # Activated Params \| - \| 21B \| 72B \| 405B \| 37B \| \| \| # Total Params \| - \| 236B \| 72B \| 405B \| 671B \| \| English \| Pile-test (BPB) \| - \| 0.606 \| 0.638 \| 0.542 \| 0.548 \| \| \| BBH (EM) \| 3-shot \| 78.8 \| 79.8 \| 82.9 \| 87.5 \| \| \| MMLU (Acc.) \| 5-shot \| 78.4 \| 85.0 \| 84.4 \| 87.1 \| \| \| MMLU-Redux (Acc.) \| 5-shot \| 75.6 \| 83.2 \| 81.3 \| 86.2 \| \| \| MMLU-Pro (Acc.) \| 5-shot \| 51.4 \| 58.3 \| 52.8 \| 64.4 \| \| \| DROP (F1) \| 3-shot \| 80.4 \| 80.6 \| 86.0 \| 89.0 \| \| \| ARC-Easy (Acc.) \| 25-shot \| 97.6 \| 98.4 \| 98.4 \| 98.9 \| \| \| ARC-Challenge (Acc.) \| 25-shot \| 92.2 \| 94.5 \| 95.3 \| 95.3 \| \| \| HellaSwag (Acc.) \| 10-shot \| 87.1 \| 84.8 \| 89.2 \| 88.9 \| \| \| PIQA (Acc.) \| 0-shot \| 83.9 \| 82.6 \| 85.9 \| 84.7 \| \| \| WinoGrande (Acc.) \| 5-shot \| 86.3 \| 82.3 \| 85.2 \| 84.9 \| \| \| RACE-Middle (Acc.) \| 5-shot \| 73.1 \| 68.1 \| 74.2 \| 67.1 \| \| \| RACE-High (Acc.) \| 5-shot \| 52.6 \| 50.3 \| 56.8 \| 51.3 \| \| \| TriviaQA (EM) \| 5-shot \| 80.0 \| 71.9 \| 82.7 \| 82.9 \| \| \| NaturalQuestions (EM) \| 5-shot \| 38.6 \| 33.2 \| 41.5 \| 40.0 \| \| \| AGIEval (Acc.) \| 0-shot \| 57.5 \| 75.8 \| 60.6 \| 79.6 \| \| Code \| HumanEval (Pass@1) \| 0-shot \| 43.3 \| 53.0 \| 54.9 \| 65.2 \| \| \| MBPP (Pass@1) \| 3-shot \| 65.0 \| 72.6 \| 68.4 \| 75.4 \| \| \| LiveCodeBench-Base (Pass@1) \| 3-shot \| 11.6 \| 12.9 \| 15.5 \| 19.4 \| \| \| CRUXEval-I (Acc.) \| 2-shot \| 52.5 \| 59.1 \| 58.5 \| 67.3 \| \| \| CRUXEval-O (Acc.) \| 2-shot \| 49.8 \| 59.9 \| 59.9 \| 69.8 \| \| Math \| GSM8K (EM) \| 8-shot \| 81.6 \| 88.3 \| 83.5 \| 89.3 \| \| \| MATH (EM) \| 4-shot \| 43.4 \| 54.4 \| 49.0 \| 61.6 \| \| \| MGSM (EM) \| 8-shot \| 63.6 \| 76.2 \| 69.9 \| 79.8 \| \| \| CMath (EM) \| 3-shot \| 78.7 \| 84.5 \| 77.3 \| 90.7 \| \| Chinese \| CLUEWSC (EM) \| 5-shot \| 82.0 \| 82.5 \| 83.0 \| 82.7 \| \| \| C-Eval (Acc.) \| 5-shot \| 81.4 \| 89.2 \| 72.5 \| 90.1 \| \| \| CMMLU (Acc.) \| 5-shot \| 84.0 \| 89.5 \| 73.7 \| 88.8 \| \| \| CMRC (EM) \| 1-shot \| 77.4 \| 75.8 \| 76.0 \| 76.3 \| \| \| C3 (Acc.) \| 0-shot \| 77.4 \| 76.7 \| 79.7 \| 78.6 \| \| \| CCPM (Acc.) \| 0-shot \| 93.0 \| 88.5 \| 78.6 \| 92.0 \| \| Multilingual \| MMMLU-non-English (Acc.) \| 5-shot \| 64.0 \| 74.8 \| 73.8 \| 79.4 \| </div> Note: Best results are shown in bold. Scores with a gap not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 achieves the best performance on most benchmarks, especially on math and code tasks. For more evaluation details, please check our paper. #### Context Window <p align="center"> <img width="80%" src="figures/niah.png"> </p> Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-V3 performs well across all context window lengths up to 128K. ### Chat Model #### Standard Benchmarks (Models larger than 67B) <div align="center"> \| \| Benchmark (Metric) \| DeepSeek V2-0506 \| DeepSeek V2.5-0905 \| Qwen2.5 72B-Inst. \| Llama3.1 405B-Inst. \| Claude-3.5-Sonnet-1022 \| GPT-4o 0513 \| DeepSeek V3 \| \|---\|---------------------\|---------------------\|----------------------\|---------------------\|----------------------\|---------------------------\|----------------\|----------------\| \| \| Architecture \| MoE \| MoE \| Dense \| Dense \| - \| - \| MoE \| \| \| # Activated Params \| 21B \| 21B \| 72B \| 405B \| - \| - \| 37B \| \| \| # Total Params \| 236B \| 236B \| 72B \| 405B \| - \| - \| 671B \| \| English \| MMLU (EM) \| 78.2 \| 80.6 \| 85.3 \| 88.6 \| 88.3 \| 87.2 \| 88.5 \| \| \| MMLU-Redux (EM) \| 77.9 \| 80.3 \| 85.6 \| 86.2 \| 88.9 \| 88.0 \| 89.1 \| \| \| MMLU-Pro (EM) \| 58.5 \| 66.2 \| 71.6 \| 73.3 \| 78.0 \| 72.6 \| 75.9 \| \| \| DROP (3-shot F1) \| 83.0 \| 87.8 \| 76.7 \| 88.7 \| 88.3 \| 83.7 \| 91.6 \| \| \| IF-Eval (Prompt Strict) \| 57.7 \| 80.6 \| 84.1 \| 86.0 \| 86.5 \| 84.3 \| 86.1 \| \| \| GPQA-Diamond (Pass@1) \| 35.3 \| 41.3 \| 49.0 \| 51.1 \| 65.0 \| 49.9 \| 59.1 \| \| \| SimpleQA (Correct) \| 9.0 \| 10.2 \| 9.1 \| 17.1 \| 28.4 \| 38.2 \| 24.9 \| \| \| FRAMES (Acc.) \| 66.9 \| 65.4 \| 69.8 \| 70.0 \| 72.5 \| 80.5 \| 73.3 \| \| \| LongBench v2 (Acc.) \| 31.6 \| 35.4 \| 39.4 \| 36.1 \| 41.0 \| 48.1 \| 48.7 \| \| Code \| HumanEval-Mul (Pass@1) \| 69.3 \| 77.4 \| 77.3 \| 77.2 \| 81.7 \| 80.5 \| 82.6 \| \| \| LiveCodeBench (Pass@1-COT) \| 18.8 \| 29.2 \| 31.1 \| 28.4 \| 36.3 \| 33.4 \| 40.5 \| \| \| LiveCodeBench (Pass@1) \| 20.3 \| 28.4 \| 28.7 \| 30.1 \| 32.8 \| 34.2 \| 37.6 \| \| \| Codeforces (Percentile) \| 17.5 \| 35.6 \| 24.8 \| 25.3 \| 20.3 \| 23.6 \| 51.6 \| \| \| SWE Verified (Resolved) \| - \| 22.6 \| 23.8 \| 24.5 \| 50.8 \| 38.8 \| 42.0 \| \| \| Aider-Edit (Acc.) \| 60.3 \| 71.6 \| 65.4 \| 63.9 \| 84.2 \| 72.9 \| 79.7 \| \| \| Aider-Polyglot (Acc.) \| - \| 18.2 \| 7.6 \| 5.8 \| 45.3 \| 16.0 \| 49.6 \| \| Math \| AIME 2024 (Pass@1) \| 4.6 \| 16.7 \| 23.3 \| 23.3 \| 16.0 \| 9.3 \| 39.2 \| \| \| MATH-500 (EM) \| 56.3 \| 74.7 \| 80.0 \| 73.8 \| 78.3 \| 74.6 \| 90.2 \| \| \| CNMO 2024 (Pass@1) \| 2.8 \| 10.8 \| 15.9 \| 6.8 \| 13.1 \| 10.8 \| 43.2 \| \| Chinese \| CLUEWSC (EM) \| 89.9 \| 90.4 \| 91.4 \| 84.7 \| 85.4 \| 87.9 \| 90.9 \| \| \| C-Eval (EM) \| 78.6 \| 79.5 \| 86.1 \| 61.5 \| 76.7 \| 76.0 \| 86.5 \| \| \| C-SimpleQA (Correct) \| 48.5 \| 54.1 \| 48.4 \| 50.4 \| 51.3 \| 59.3 \| 64.8 \| Note: All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature settings to derive robust final results. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance against frontier closed-source models. </div> #### Open Ended Generation Evaluation <div align="center"> \| Model \| Arena-Hard \| AlpacaEval 2.0 \| \|-------\|------------\|----------------\| \| DeepSeek-V2.5-0905 \| 76.2 \| 50.5 \| \| Qwen2.5-72B-Instruct \| 81.2 \| 49.1 \| \| LLaMA-3.1 405B \| 69.3 \| 40.5 \| \| GPT-4o-0513 \| 80.4 \| 51.1 \| \| Claude-Sonnet-3.5-1022 \| 85.2 \| 52.0 \| \| DeepSeek-V3 \| 85.5 \| 70.0 \| Note: English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric. </div> ## 5. Chat Website & API Platform You can chat with DeepSeek-V3 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in) We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 6. How to Run Locally DeepSeek-V3 can be deployed locally using the following hardware and open-source community software: 1. DeepSeek-Infer Demo: We provide a simple and lightweight demo for FP8 and BF16 inference. 2. SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes. 3. LMDeploy: Enables efficient FP8 and BF16 inference for local and cloud deployment. 4. TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon. 5. vLLM: Support DeekSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism. 6. AMD GPU: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes. 7. Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend devices. Since FP8 training is natively adopted in our framework, we only provide FP8 weights. If you require BF16 weights for experimentation, you can use the provided conversion script to perform the transformation. Here is an example of converting FP8 weights to BF16: ```shell cd inference python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-hf-path /path/to/bf16_weights ``` NOTE: Huggingface's Transformers has not been directly supported yet. ### 6.1 Inference with DeepSeek-Infer Demo (example only) #### Model Weights & Demo Code Preparation First, clone our DeepSeek-V3 GitHub repository: ```shell git clone https://github.com/deepseek-ai/DeepSeek-V3.git ``` Navigate to the `inference` folder and install dependencies listed in `requirements.txt`. ```shell cd DeepSeek-V3/inference pip install -r requirements.txt ``` Download the model weights from HuggingFace, and put them into `/path/to/DeepSeek-V3` folder. #### Model Weights Conversion Convert HuggingFace model weights to a specific format: ```shell python convert.py --hf-ckpt-path /path/to/DeepSeek-V3 --save-path /path/to/DeepSeek-V3-Demo --n-experts 256 --model-parallel 16 ``` #### Run Then you can chat with DeepSeek-V3: ```shell torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-addr $ADDR --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200 ``` Or batch inference on a given file: ```shell torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-addr $ADDR --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --input-file $FILE ``` ### 6.2 Inference with SGLang (recommended) [SGLang](https://github.com/sgl-project/sglang) currently supports MLA optimizations, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering state-of-the-art latency and throughput performance among open-source frameworks. Notably, [SGLang v0.4.1](https://github.com/sgl-project/sglang/releases/tag/v0.4.1) fully supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly versatile and robust solution. Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3 ### 6.3 Inference with LMDeploy (recommended) [LMDeploy](https://github.com/InternLM/lmdeploy), a flexible and high-performance inference and serving framework tailored for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online deployment capabilities, seamlessly integrating with PyTorch-based workflows. For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: https://github.com/InternLM/lmdeploy/issues/2960 ### 6.4 Inference with TRT-LLM (recommended) [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3. ### 6.5 Inference with vLLM (recommended) [vLLM](https://github.com/vllm-project/vllm) v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard techniques, vLLM offers _pipeline parallelism_ allowing you to run this model on multiple machines connected by networks. For detailed guidance, please refer to the [vLLM instructions](https://docs.vllm.ai/en/latest/serving/distributed_serving.html). Please feel free to follow [the enhancement plan](https://github.com/vllm-project/vllm/issues/11539) as well. ### 6.6 Recommended Inference Functionality with AMD GPUs In collaboration with the AMD team, we have achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For detailed guidance, please refer to the [SGLang instructions](#63-inference-with-lmdeploy-recommended). ### 6.7 Recommended Inference Functionality with Huawei Ascend NPUs The [MindIE](https://www.hiascend.com/en/software/mindie) framework from the Huawei Ascend community has successfully adapted the BF16 version of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the [instructions here](https://modelers.cn/models/MindIE/deepseekv3). ## 7. License This code repository is licensed under [the MIT License](LICENSE-CODE). The use of DeepSeek-V3 Base/Chat models is subject to [the Model License](LICENSE-MODEL). DeepSeek-V3 series (including Base and Chat) supports commercial use. ## 8. Citation ``` @misc{deepseekai2024deepseekv3technicalreport, title={DeepSeek-V3 Technical Report}, author={DeepSeek-AI and Aixin Liu and Bei Feng and Bing Xue and Bingxuan Wang and Bochao Wu and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Daya Guo and Dejian Yang and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Haowei Zhang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Li and Hui Qu and J. L. Cai and Jian Liang and Jianzhong Guo and Jiaqi Ni and Jiashi Li and Jiawei Wang and Jin Chen and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and Junxiao Song and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Lei Xu and Leyi Xia and Liang Zhao and Litong Wang and Liyue Zhang and Meng Li and Miaojun Wang and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Mingming Li and Ning Tian and Panpan Huang and Peiyi Wang and Peng Zhang and Qiancheng Wang and Qihao Zhu and Qinyu Chen and Qiushi Du and R. J. Chen and R. L. Jin and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and Runxin Xu and Ruoyu Zhang and Ruyi Chen and S. S. Li and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shaoqing Wu and Shengfeng Ye and Shengfeng Ye and Shirong Ma and Shiyu Wang and Shuang Zhou and Shuiping Yu and Shunfeng Zhou and Shuting Pan and T. Wang and Tao Yun and Tian Pei and Tianyu Sun and W. L. Xiao and Wangding Zeng and Wanjia Zhao and Wei An and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and X. Q. Li and Xiangyue Jin and Xianzu Wang and Xiao Bi and Xiaodong Liu and Xiaohan Wang and Xiaojin Shen and Xiaokang Chen and Xiaokang Zhang and Xiaosha Chen and Xiaotao Nie and Xiaowen Sun and Xiaoxiang Wang and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xingkai Yu and Xinnan Song and Xinxia Shan and Xinyi Zhou and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and Y. K. Li and Y. Q. Wang and Y. X. Wei and Y. X. Zhu and Yang Zhang and Yanhong Xu and Yanhong Xu and Yanping Huang and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Li and Yaohui Wang and Yi Yu and Yi Zheng and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Ying Tang and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yu Wu and Yuan Ou and Yuchen Zhu and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yukun Zha and Yunfan Xiong and Yunxian Ma and Yuting Yan and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Z. F. Wu and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhen Huang and Zhen Zhang and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhibin Gou and Zhicheng Ma and Zhigang Yan and Zhihong Shao and Zhipeng Xu and Zhiyu Wu and Zhongyu Zhang and Zhuoshu Li and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Ziyi Gao and Zizheng Pan}, year={2024}, eprint={2412.19437}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.19437}, } ``` ## 9. Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF	itlwas	"2025-04-19T10:51:33Z"	0	0	transformers	[ "transformers", "gguf", "nvidia", "llama-3", "pytorch", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:nvidia/Nemotron-Mini-4B-Instruct", "base_model:quantized:nvidia/Nemotron-Mini-4B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2025-04-19T10:51:15Z"	--- base_model: nvidia/Nemotron-Mini-4B-Instruct language: - en library_name: transformers license: other license_name: nvidia-open-model-license license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf pipeline_tag: text-generation tags: - nvidia - llama-3 - pytorch - llama-cpp - gguf-my-repo --- # itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`nvidia/Nemotron-Mini-4B-Instruct`](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-mini-4b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-mini-4b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-mini-4b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-mini-4b-instruct-q4_k_m.gguf -c 2048 ```
amarkale/irnx_ironman_suit_mac_42	amarkale	"2025-04-19T10:48:57Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-04-18T16:03:00Z"	--- license: apache-2.0 ---
RobotsMali/stt-bm-quartznet15x5-V0	RobotsMali	"2025-04-19T10:47:18Z"	75	1	nemo	[ "nemo", "automatic-speech-recognition", "speech", "audio", "CTC", "QuartzNet", "pytorch", "Bambara", "NeMo", "bm", "dataset:RobotsMali/bam-asr-early", "license:cc-by-4.0", "model-index", "region:us" ]	automatic-speech-recognition	"2025-02-07T04:07:11Z"	--- language: - bm library_name: nemo datasets: - RobotsMali/bam-asr-early thumbnail: null tags: - automatic-speech-recognition - speech - audio - CTC - QuartzNet - pytorch - Bambara - NeMo license: cc-by-4.0 base_model: stt_fr_quartznet15x5 model-index: - name: stt-bm-quartznet15x5 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: bam-asr-early type: RobotsMali/bam-asr-early split: test args: language: bm metrics: - name: Test WER type: wer value: 46.5 metrics: - wer pipeline_tag: automatic-speech-recognition --- # QuartzNet 15x5 CTC Bambara <style> img { display: inline; } </style> [![Model architecture](https://img.shields.io/badge/Model_Arch-QuartzNet-lightgrey#model-badge)](#model-architecture) \| [![Model size](https://img.shields.io/badge/Params-19M-lightgrey#model-badge)](#model-architecture) \| [![Language](https://img.shields.io/badge/Language-bm-lightgrey#model-badge)](#datasets) `stt-bm-quartznet15x5-V0` is a fine-tuned version of NVIDIA’s [`stt_fr_quartznet15x5`](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_quartznet15x5) optimized for Bambara ASR. This model cannot write Punctuations and Capitalizations, it utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the training set of bam-asr-all dataset. The model was fine-tuned using NVIDIA NeMo and is trained with CTC (Connectionist Temporal Classification) Loss. ## 🚨 Important Note This model, along with its associated resources, is part of an ongoing research effort, improvements and refinements are expected in future versions. Users should be aware that: - The model may not generalize very well accross all speaking conditions and dialects. - Community feedback is welcome, and contributions are encouraged to refine the model further. ## NVIDIA NeMo: Training To fine-tune or use the model, install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend installing it after setting up the latest PyTorch version. ```bash pip install nemo_toolkit['asr'] ``` ## How to Use This Model ### Load Model with NeMo ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="RobotsMali/stt-bm-quartznet15x5") ``` ### Transcribe Audio ```python # Assuming you have a test audio file named sample_audio.wav asr_model.transcribe(['sample_audio.wav']) ``` ### Input This model accepts 16 kHz mono-channel audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given speech sample. ## Model Architecture QuartzNet is a convolutional architecture, which consists of 1D time-channel separable convolutions optimized for speech recognition. More information on QuartzNet can be found here: [QuartzNet Model](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/models.html#quartznet). ## Training The NeMo toolkit was used to fine-tune this model for 25939 steps over the `stt_fr_quartznet15x5` model. This model is trained with this [base config](https://github.com/RobotsMali-AI/bambara-asr/blob/main/configs/quartznet-20m-config-v2.yaml). The full training configurations, scripts, and experimental logs are available here: 🔗 [Bambara-ASR Experiments](https://github.com/RobotsMali-AI/bambara-asr) ## Dataset This model was fine-tuned on the [bam-asr-early](https://huggingface.co/datasets/RobotsMali/bam-asr-early) dataset, which consists of 37 hours of transcribed Bambara speech data. The dataset is primarily derived from Jeli-ASR dataset (~87%). ## Performance The performance of Automatic Speech Recognition models is measured using Word Error Rate (WER%). \|Version\|Tokenizer\|Vocabulary Size\|bam-asr-all (test set)\| \|---------\|-----------------------\|-----------------\|---------\| \| V2 \| Character-wise \| 45 \| 46.5 \| These are greedy WER numbers without external LM. ## License This model is released under the CC-BY-4.0 license. By using this model, you agree to the terms of the license. --- More details are available in the Experimental Technical Report: 📄 [Draft Technical Report - Weights & Biases](https://wandb.ai/yacoudiarra-wl/bam-asr-nemo-training/reports/Draft-Technical-Report-V1--VmlldzoxMTIyOTMzOA). Feel free to open a discussion on Hugging Face or [file an issue](https://github.com/RobotsMali-AI/bambara-asr/issues) on GitHub if you have any contributions. ---
itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF	itlwas	"2025-04-19T10:44:20Z"	0	0	nemo	[ "nemo", "gguf", "llama-cpp", "gguf-my-repo", "en", "hi", "base_model:nvidia/Nemotron-4-Mini-Hindi-4B-Instruct", "base_model:quantized:nvidia/Nemotron-4-Mini-Hindi-4B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T10:44:07Z"	--- base_model: nvidia/Nemotron-4-Mini-Hindi-4B-Instruct language: - en - hi library_name: nemo license: other license_name: nvidia-open-model-license license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf tags: - llama-cpp - gguf-my-repo --- # itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`nvidia/Nemotron-4-Mini-Hindi-4B-Instruct`](https://huggingface.co/nvidia/Nemotron-4-Mini-Hindi-4B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/Nemotron-4-Mini-Hindi-4B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-4-mini-hindi-4b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-4-mini-hindi-4b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-4-mini-hindi-4b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-4-mini-hindi-4b-instruct-q4_k_m.gguf -c 2048 ```
RobotsMali/soloni-114m-tdt-ctc-V0	RobotsMali	"2025-04-19T10:42:31Z"	23	2	nemo	[ "nemo", "automatic-speech-recognition", "speech", "audio", "Transducer", "TDT", "FastConformer", "Conformer", "pytorch", "Bambara", "NeMo", "bm", "dataset:RobotsMali/bam-asr-early", "base_model:nvidia/parakeet-tdt_ctc-110m", "base_model:finetune:nvidia/parakeet-tdt_ctc-110m", "license:cc-by-4.0", "model-index", "region:us" ]	automatic-speech-recognition	"2025-02-07T04:04:28Z"	--- language: - bm library_name: nemo datasets: - RobotsMali/bam-asr-early thumbnail: null tags: - automatic-speech-recognition - speech - audio - Transducer - TDT - FastConformer - Conformer - pytorch - Bambara - NeMo license: cc-by-4.0 base_model: nvidia/parakeet-tdt_ctc-110m model-index: - name: soloni-114m-tdt-ctc results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: bam-asr-early type: RobotsMali/bam-asr-early split: test args: language: bm metrics: - name: Test WER (TDT) type: wer value: 66.7 - name: Test WER (CTC) type: wer value: 40.6 metrics: - wer pipeline_tag: automatic-speech-recognition --- # Soloni TDT-CTC 114M Bambara <style> img { display: inline; } </style> [![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--TDT-lightgrey#model-badge)](#model-architecture) \| [![Model size](https://img.shields.io/badge/Params-114M-lightgrey#model-badge)](#model-architecture) \| [![Language](https://img.shields.io/badge/Language-bm-lightgrey#model-badge)](#datasets) `soloni-114m-tdt-ctc-V0` is a fine tuned version of nvidia's [`parakeet-tdt_ctc-110m`](https://huggingface.co/nvidia/parakeet-tdt_ctc-110m) that transcribes bambara language speech. Unlike its base model, this model cannot write Punctuations and Capitalizations since these were absent from its training. The model was fine-tuned using NVIDIA NeMo and supports both TDT (Token-and-Duration Transducer) and CTC (Connectionist Temporal Classification) decoding. ## 🚨 Important Note Update (February 17th): We observed a significantly lower WER (\~36%) for the TDT branch when using an external WER calculation method that relies solely on the predicted and reference transcriptions. However, the WER values reported in this model card are derived from the standard NeMo workflow using PyTorch Lightning's trainer, where the TDT branch yielded higher WER scores (\~66%). Differences may arise due to variations in post-processing, alignment handling, or evaluation methodologies. This model, along with its associated resources, is part of an ongoing research effort, improvements and refinements are expected in future versions. Users should be aware that: - The model may not generalize very well accross all speaking conditions and dialects. - Community feedback is welcome, and contributions are encouraged to refine the model further. ## NVIDIA NeMo: Training To fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version. ```bash pip install nemo_toolkit['asr'] ``` ## How to Use This Model Note that this model has been released for research purposes primarily. ### Load Model with NeMo ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name="RobotsMali/soloni-114m-tdt-ctc") ``` ### Transcribe Audio ```python # Assuming you have a test audio file named sample_audio.wav asr_model.transcribe(['sample_audio.wav']) ``` Note that the decoding strategy for the TDT decoder use CUDA Graphs by default but not all GPUs and versions of cuda support that parameter. If you run into a `RuntimeError: CUDA error: invalid argument` you should set that argument to false in the decoding strategy before calling asr_model.transcribe() ```python decoding_cfg = asr_model.cfg.decoding # Disable CUDA Graphs decoding_cfg.greedy.use_cuda_graph_decoder = False # Then change the decoding strategy asr_model.change_decoding_strategy(decoding_cfg=decoding_cfg) ``` ### Input This model accepts 16000 Hz mono-channel audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture This model uses a Hybrid FastConformer-TDT-CTC architecture. FastConformer is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. You may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer). ## Training The NeMo toolkit was used for finetuning this model for 16,296 steps over `parakeet-tdt_ctc-110m` model. This model is trained with this [base config](https://github.com/RobotsMali-AI/bambara-asr/blob/main/configs/parakeet-110m-config-v6.yaml). The full training configurations, scripts, and experimental logs are available here: 🔗 [Bambara-ASR Experiments](https://github.com/RobotsMali-AI/bambara-asr) The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). ## Dataset This model was fine-tuned on the [bam-asr-early](https://huggingface.co/datasets/RobotsMali/bam-asr-early) dataset, which consists of 37 hours of transcribed Bambara speech data. The dataset is primarily derived from Jeli-ASR dataset (~87%). ## Performance The performance of Automatic Speech Recognition models is measured using Word Error Rate. Since this model has two decoders operating independently, each decoder is evaluated independently too. The following table summarizes the performance of the available models in this collection with the Transducer decoder. Performances of the ASR models are reported in terms of Word Error Rate (WER%). \|Decoder (Version)\|Tokenizer\|Vocabulary Size\|bam-asr-all (test set)\| \|---------\|-----------------------\|-----------------\|---------\| \| CTC (V6) \| BPE \| 1024 \| 40.6 \| \| TDT (V6) \| BPE \| 1024 \| 66.7 \| These are greedy WER numbers without external LM. By default the main decoder branch is the TDT branch, if you would like to switch to the CTC decoder simply run this block of code before calling the .transcribe method ```python # Retrieve the CTC decoding config ctc_decoding_cfg = asr_model.cfg.aux_ctc.decoding # Then change the decoding strategy asr_model.change_decoding_strategy(decoder_type='ctc', decoding_cfg=ctc_decoding_cfg) # Transcribe with the CTC decoder asr_model.transcribe(['sample_audio.wav']) ``` ## License This model is released under the CC-BY-4.0 license. By using this model, you agree to the terms of the license. --- More details are available in the Experimental Technical Report: 📄 [Draft Technical Report - Weights & Biases](https://wandb.ai/yacoudiarra-wl/bam-asr-nemo-training/reports/Draft-Technical-Report-V1--VmlldzoxMTIyOTMzOA). Feel free to open a discussion on Hugging Face or [file an issue](https://github.com/RobotsMali-AI/bambara-asr/issues) on github if you have any contributions ---
ishvets/Sarah	ishvets	"2025-04-19T10:40:42Z"	0	0	null	[ "license:other", "region:us" ]	null	"2025-04-19T09:30:42Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
Raniahossam33/qwen2.5-7b-instruct-ditto-Syria-topic-sap1-custom	Raniahossam33	"2025-04-19T10:37:45Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-03-19T19:51:28Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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aliyuksal/mistral-mailwizz-merged	aliyuksal	"2025-04-19T10:37:12Z"	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T10:23:22Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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heyIamUmair/llama3-3b-merged-legal	heyIamUmair	"2025-04-19T10:36:06Z"	0	0	null	[ "safetensors", "llama", "legal", "pakistan", "merged", "instruction-tuned", "base_model:unsloth/Llama-3.2-3B-Instruct", "base_model:finetune:unsloth/Llama-3.2-3B-Instruct", "license:apache-2.0", "region:us" ]	null	"2025-04-19T10:31:00Z"	--- license: apache-2.0 tags: - llama - legal - pakistan - merged - instruction-tuned model_type: causal-lm base_model: unsloth/Llama-3.2-3B-Instruct inference: true --- # 🧠 LLaMA 3.2 3B – Legal Chatbot (Merged) This is a merged model combining `unsloth/Llama-3.2-3B-Instruct` with LoRA adapters fine-tuned on Pakistani law, including family, criminal, and property law. ✅ Merged ✅ Inference API compatible ✅ No Unsloth or adapter loading needed
Bouquets/SecGPT-1.5B-Q4_K_M-GGUF	Bouquets	"2025-04-19T10:35:48Z"	0	0	transformers	[ "transformers", "gguf", "cybersecurity", "security", "network-security", "llama-cpp", "gguf-my-repo", "zh", "en", "base_model:clouditera/SecGPT-1.5B", "base_model:quantized:clouditera/SecGPT-1.5B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T10:35:40Z"	--- base_model: clouditera/SecGPT-1.5B language: - zh - en library_name: transformers license: apache-2.0 tags: - cybersecurity - security - network-security - llama-cpp - gguf-my-repo --- # Bouquets/SecGPT-1.5B-Q4_K_M-GGUF This model was converted to GGUF format from [`clouditera/SecGPT-1.5B`](https://huggingface.co/clouditera/SecGPT-1.5B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/clouditera/SecGPT-1.5B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Bouquets/SecGPT-1.5B-Q4_K_M-GGUF --hf-file secgpt-1.5b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Bouquets/SecGPT-1.5B-Q4_K_M-GGUF --hf-file secgpt-1.5b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Bouquets/SecGPT-1.5B-Q4_K_M-GGUF --hf-file secgpt-1.5b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Bouquets/SecGPT-1.5B-Q4_K_M-GGUF --hf-file secgpt-1.5b-q4_k_m.gguf -c 2048 ```
itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF	itlwas	"2025-04-19T10:35:08Z"	0	0	transformers	[ "transformers", "gguf", "nvidia", "llama-3", "pytorch", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:nvidia/Llama-3.1-Nemotron-Nano-8B-v1", "base_model:quantized:nvidia/Llama-3.1-Nemotron-Nano-8B-v1", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2025-04-19T10:34:44Z"	--- base_model: nvidia/Llama-3.1-Nemotron-Nano-8B-v1 language: - en library_name: transformers license: other license_name: nvidia-open-model-license license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ pipeline_tag: text-generation tags: - nvidia - llama-3 - pytorch - llama-cpp - gguf-my-repo --- # itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF This model was converted to GGUF format from [`nvidia/Llama-3.1-Nemotron-Nano-8B-v1`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q4_k_m.gguf -c 2048 ```
iTroned/weight_test_early_fusion_sentiment_False_hate_speech_False_extra_layer_True	iTroned	"2025-04-19T10:33:56Z"	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	"2025-04-19T09:39:26Z"	--- library_name: transformers tags: - generated_from_trainer model-index: - name: weight_test_early_fusion_sentiment_False_hate_speech_False_extra_layer_True results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/moufl4td) # weight_test_early_fusion_sentiment_False_hate_speech_False_extra_layer_True This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7058 - Accuracy Offensive: 0.7980 - F1 Offensive: 0.7770 - Accuracy Targeted: 0.7727 - F1 Targeted: 0.5053 - Accuracy Stance: 0.7247 - F1 Stance: 0.3955 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy Offensive \| F1 Offensive \| Accuracy Targeted \| F1 Targeted \| Accuracy Stance \| F1 Stance \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------------:\|:------------:\|:-----------------:\|:-----------:\|:---------------:\|:---------:\| \| 0.8406 \| 1.0 \| 1324 \| 0.7136 \| 0.7938 \| 0.7702 \| 0.7659 \| 0.4982 \| 0.6952 \| 0.2286 \| \| 0.6996 \| 2.0 \| 2648 \| 0.7058 \| 0.7980 \| 0.7770 \| 0.7727 \| 0.5053 \| 0.7247 \| 0.3955 \| \| 0.6313 \| 3.0 \| 3972 \| 0.8419 \| 0.7874 \| 0.7714 \| 0.7632 \| 0.5038 \| 0.6798 \| 0.3803 \| \| 0.5086 \| 4.0 \| 5296 \| 1.2924 \| 0.7949 \| 0.7662 \| 0.7704 \| 0.5001 \| 0.7356 \| 0.3709 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.6.0+cu124 - Datasets 3.0.1 - Tokenizers 0.21.1
yamatazen/StarrySky-12B-Q4_K_M-GGUF	yamatazen	"2025-04-19T10:31:54Z"	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:yamatazen/StarrySky-12B", "base_model:quantized:yamatazen/StarrySky-12B", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T10:31:21Z"	--- base_model: yamatazen/StarrySky-12B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # yamatazen/StarrySky-12B-Q4_K_M-GGUF This model was converted to GGUF format from [`yamatazen/StarrySky-12B`](https://huggingface.co/yamatazen/StarrySky-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/yamatazen/StarrySky-12B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo yamatazen/StarrySky-12B-Q4_K_M-GGUF --hf-file starrysky-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo yamatazen/StarrySky-12B-Q4_K_M-GGUF --hf-file starrysky-12b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo yamatazen/StarrySky-12B-Q4_K_M-GGUF --hf-file starrysky-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo yamatazen/StarrySky-12B-Q4_K_M-GGUF --hf-file starrysky-12b-q4_k_m.gguf -c 2048 ```
adarsh3601/my_gemma3_4b_pt	adarsh3601	"2025-04-19T10:28:51Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it", "base_model:finetune:unsloth/gemma-3-4b-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-04-19T10:28:37Z"	--- base_model: unsloth/gemma-3-4b-it tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: adarsh3601 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ngtranAI1/RawMomentum	ngtranAI1	"2025-04-19T10:27:08Z"	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-04-19T10:27:03Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF	itlwas	"2025-04-19T10:27:04Z"	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct", "base_model:quantized:nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T10:26:42Z"	--- base_model: nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct language: - en library_name: transformers license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -c 2048 ```
CrimsonZockt/PaigeBueckers-FLUXLORA	CrimsonZockt	"2025-04-19T10:25:15Z"	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	"2025-04-19T10:24:44Z"	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- photoshoot of Paige Bueckers, female, woman, solo, black tanktop, professional headshot. output: url: images/photoshoot of Paige Bueckers, female, woman, so....png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Paige Bueckers --- # PaigeBueckers <Gallery /> ## Model description This is a LORA Model that i have train on Weights.gg ## Trigger words You should use `Paige Bueckers` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/CrimsonZockt/PaigeBueckers-FLUXLORA/tree/main) them in the Files & versions tab.
mradermacher/Pathos-Eta-LLaMa-70B-GGUF	mradermacher	"2025-04-19T10:23:22Z"	108	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:TareksGraveyard/Pathos-Eta-LLaMa-70B", "base_model:quantized:TareksGraveyard/Pathos-Eta-LLaMa-70B", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-02-20T03:18:43Z"	--- base_model: TareksGraveyard/Pathos-Eta-LLaMa-70B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/TareksGraveyard/Pathos-Eta-LLaMa-70B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF	mradermacher	"2025-04-19T10:23:22Z"	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final", "base_model:quantized:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T09:42:12Z"	--- base_model: Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
ahmadtalha/whisper-small-dv	ahmadtalha	"2025-04-19T10:20:40Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2025-04-19T09:03:11Z"	--- library_name: transformers language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 13.683624856556664 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Dv - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1749 - Wer Ortho: 63.2844 - Wer: 13.6836 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.1201 \| 1.6287 \| 500 \| 0.1749 \| 63.2844 \| 13.6836 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
mlfoundations-dev/b1_code_top_8_3k	mlfoundations-dev	"2025-04-19T10:19:51Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T05:18:44Z"	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b1_code_top_8_3k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # b1_code_top_8_3k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b1_code_top_8_3k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
MinaMila/phi3_LoRa_Adult_ep5_22	MinaMila	"2025-04-19T10:16:37Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-04-19T10:16:34Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
dgambettaphd/M_gmm2_gen8_run0_W_doc1000_synt64_MPP	dgambettaphd	"2025-04-19T10:12:42Z"	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-04-19T10:12:05Z"	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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railispeople/nalmis	railispeople	"2025-04-19T10:11:05Z"	0	0	null	[ "license:other", "region:us" ]	null	"2025-04-19T09:37:15Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
thanhhau097/cm9o18aaq009ags6rcdmtp9ks	thanhhau097	"2025-04-19T10:06:16Z"	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-04-19T09:46:48Z"	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers license: other instance_prompt: a photo of sks fashion model widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux DreamBooth LoRA - thanhhau097/cm9o18aaq009ags6rcdmtp9ks <Gallery /> ## Model description These are thanhhau097/cm9o18aaq009ags6rcdmtp9ks DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `a photo of sks fashion model` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](thanhhau097/cm9o18aaq009ags6rcdmtp9ks/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('thanhhau097/cm9o18aaq009ags6rcdmtp9ks', weight_name='pytorch_lora_weights.safetensors') image = pipeline('a photo of sks fashion model').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
TommyClas/phaseseg_models	TommyClas	"2025-04-19T10:06:10Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "segformer", "image-segmentation", "vision", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	"2025-04-19T02:57:18Z"	--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - image-segmentation - vision - generated_from_trainer model-index: - name: phaseseg_models results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phaseseg_models This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the TommyClas/phase_seg dataset. It achieves the following results on the evaluation set: - Loss: 0.0372 - Mean Iou: 0.9744 - Mean Accuracy: 0.9872 - Overall Accuracy: 0.9869 - Accuracy 背景: nan - Accuracy 未水化水泥颗粒: 0.9806 - Accuracy 孔隙: 0.9893 - Accuracy 氢氧化钙: 0.9901 - Accuracy 其他水化物: 0.9887 - Iou 背景: nan - Iou 未水化水泥颗粒: 0.9730 - Iou 孔隙: 0.9695 - Iou 氢氧化钙: 0.9767 - Iou 其他水化物: 0.9782 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy 背景 \| Accuracy 未水化水泥颗粒 \| Accuracy 孔隙 \| Accuracy 氢氧化钙 \| Accuracy 其他水化物 \| Iou 背景 \| Iou 未水化水泥颗粒 \| Iou 孔隙 \| Iou 氢氧化钙 \| Iou 其他水化物 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------:\|:----------------:\|:-----------:\|:-------------:\|:--------------:\|:------:\|:-----------:\|:------:\|:--------:\|:---------:\| \| No log \| 1.0 \| 50 \| 0.2994 \| 0.7375 \| 0.9586 \| 0.9580 \| nan \| 0.9557 \| 0.9290 \| 0.9696 \| 0.9802 \| 0.0 \| 0.9224 \| 0.9066 \| 0.9195 \| 0.9392 \| \| 0.4501 \| 2.0 \| 100 \| 0.1558 \| 0.7645 \| 0.9767 \| 0.9766 \| nan \| 0.9802 \| 0.9580 \| 0.9758 \| 0.9929 \| 0.0 \| 0.9585 \| 0.9504 \| 0.9524 \| 0.9609 \| \| 0.4501 \| 3.0 \| 150 \| 0.1193 \| 0.7715 \| 0.9814 \| 0.9812 \| nan \| 0.9797 \| 0.9718 \| 0.9829 \| 0.9912 \| 0.0 \| 0.9661 \| 0.9628 \| 0.9607 \| 0.9680 \| \| 0.0949 \| 4.0 \| 200 \| 0.0898 \| 0.7745 \| 0.9835 \| 0.9834 \| nan \| 0.9844 \| 0.9751 \| 0.9842 \| 0.9902 \| 0.0 \| 0.9702 \| 0.9667 \| 0.9655 \| 0.9699 \| \| 0.0949 \| 5.0 \| 250 \| 0.0766 \| 0.7762 \| 0.9848 \| 0.9848 \| nan \| 0.9848 \| 0.9799 \| 0.9842 \| 0.9905 \| 0.0 \| 0.9729 \| 0.9696 \| 0.9674 \| 0.9713 \| \| 0.0584 \| 6.0 \| 300 \| 0.0624 \| 0.7771 \| 0.9856 \| 0.9855 \| nan \| 0.9865 \| 0.9802 \| 0.9852 \| 0.9905 \| 0.0 \| 0.9747 \| 0.9704 \| 0.9684 \| 0.9723 \| \| 0.0584 \| 7.0 \| 350 \| 0.0628 \| 0.7777 \| 0.9859 \| 0.9858 \| nan \| 0.9845 \| 0.9817 \| 0.9865 \| 0.9907 \| 0.0 \| 0.9743 \| 0.9717 \| 0.9695 \| 0.9731 \| \| 0.0441 \| 8.0 \| 400 \| 0.0575 \| 0.7784 \| 0.9863 \| 0.9863 \| nan \| 0.9852 \| 0.9841 \| 0.9846 \| 0.9914 \| 0.0 \| 0.9750 \| 0.9732 \| 0.9709 \| 0.9732 \| \| 0.0441 \| 9.0 \| 450 \| 0.0500 \| 0.7788 \| 0.9867 \| 0.9866 \| nan \| 0.9855 \| 0.9847 \| 0.9839 \| 0.9925 \| 0.0 \| 0.9762 \| 0.9738 \| 0.9706 \| 0.9734 \| \| 0.0363 \| 10.0 \| 500 \| 0.0496 \| 0.7795 \| 0.9870 \| 0.9869 \| nan \| 0.9841 \| 0.9859 \| 0.9875 \| 0.9905 \| 0.0 \| 0.9753 \| 0.9745 \| 0.9726 \| 0.9751 \| \| 0.0363 \| 11.0 \| 550 \| 0.0458 \| 0.7798 \| 0.9873 \| 0.9872 \| nan \| 0.9844 \| 0.9863 \| 0.9875 \| 0.9910 \| 0.0 \| 0.9758 \| 0.9749 \| 0.9731 \| 0.9755 \| \| 0.0315 \| 12.0 \| 600 \| 0.0423 \| 0.7802 \| 0.9875 \| 0.9875 \| nan \| 0.9872 \| 0.9845 \| 0.9895 \| 0.9891 \| 0.0 \| 0.9771 \| 0.9750 \| 0.9731 \| 0.9757 \| \| 0.0315 \| 13.0 \| 650 \| 0.0437 \| 0.7800 \| 0.9874 \| 0.9873 \| nan \| 0.9851 \| 0.9848 \| 0.9891 \| 0.9908 \| 0.0 \| 0.9762 \| 0.9749 \| 0.9731 \| 0.9760 \| \| 0.0278 \| 14.0 \| 700 \| 0.0390 \| 0.7805 \| 0.9878 \| 0.9877 \| nan \| 0.9862 \| 0.9859 \| 0.9874 \| 0.9916 \| 0.0 \| 0.9772 \| 0.9753 \| 0.9738 \| 0.9762 \| \| 0.0278 \| 15.0 \| 750 \| 0.0404 \| 0.7799 \| 0.9874 \| 0.9873 \| nan \| 0.9834 \| 0.9872 \| 0.9896 \| 0.9896 \| 0.0 \| 0.9753 \| 0.9738 \| 0.9740 \| 0.9764 \| \| 0.0255 \| 16.0 \| 800 \| 0.0422 \| 0.7789 \| 0.9868 \| 0.9866 \| nan \| 0.9809 \| 0.9872 \| 0.9878 \| 0.9911 \| 0.0 \| 0.9725 \| 0.9709 \| 0.9745 \| 0.9765 \| \| 0.0255 \| 17.0 \| 850 \| 0.0387 \| 0.7794 \| 0.9871 \| 0.9869 \| nan \| 0.9831 \| 0.9858 \| 0.9900 \| 0.9895 \| 0.0 \| 0.9742 \| 0.9720 \| 0.9739 \| 0.9767 \| \| 0.0235 \| 18.0 \| 900 \| 0.0395 \| 0.7791 \| 0.9869 \| 0.9867 \| nan \| 0.9810 \| 0.9882 \| 0.9881 \| 0.9903 \| 0.0 \| 0.9725 \| 0.9706 \| 0.9751 \| 0.9770 \| \| 0.0235 \| 19.0 \| 950 \| 0.0364 \| 0.7790 \| 0.9868 \| 0.9866 \| nan \| 0.9809 \| 0.9886 \| 0.9867 \| 0.9911 \| 0.0 \| 0.9723 \| 0.9706 \| 0.9752 \| 0.9769 \| \| 0.0221 \| 20.0 \| 1000 \| 0.0394 \| 0.7785 \| 0.9865 \| 0.9863 \| nan \| 0.9801 \| 0.9870 \| 0.9887 \| 0.9904 \| 0.0 \| 0.9713 \| 0.9691 \| 0.9751 \| 0.9769 \| \| 0.0221 \| 21.0 \| 1050 \| 0.0374 \| 0.7787 \| 0.9866 \| 0.9864 \| nan \| 0.9812 \| 0.9873 \| 0.9871 \| 0.9910 \| 0.0 \| 0.9720 \| 0.9697 \| 0.9750 \| 0.9768 \| \| 0.021 \| 22.0 \| 1100 \| 0.0364 \| 0.7787 \| 0.9867 \| 0.9865 \| nan \| 0.9804 \| 0.9874 \| 0.9884 \| 0.9906 \| 0.0 \| 0.9718 \| 0.9695 \| 0.9753 \| 0.9771 \| \| 0.021 \| 23.0 \| 1150 \| 0.0375 \| 0.7784 \| 0.9865 \| 0.9863 \| nan \| 0.9792 \| 0.9883 \| 0.9888 \| 0.9897 \| 0.0 \| 0.9708 \| 0.9687 \| 0.9754 \| 0.9774 \| \| 0.0199 \| 24.0 \| 1200 \| 0.0371 \| 0.7782 \| 0.9864 \| 0.9861 \| nan \| 0.9792 \| 0.9871 \| 0.9878 \| 0.9913 \| 0.0 \| 0.9709 \| 0.9684 \| 0.9749 \| 0.9768 \| \| 0.0199 \| 25.0 \| 1250 \| 0.0393 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9788 \| 0.9885 \| 0.9890 \| 0.9897 \| 0.0 \| 0.9707 \| 0.9683 \| 0.9754 \| 0.9776 \| \| 0.0191 \| 26.0 \| 1300 \| 0.0387 \| 0.7783 \| 0.9865 \| 0.9862 \| nan \| 0.9791 \| 0.9878 \| 0.9904 \| 0.9887 \| 0.0 \| 0.9709 \| 0.9683 \| 0.9750 \| 0.9775 \| \| 0.0191 \| 27.0 \| 1350 \| 0.0384 \| 0.7785 \| 0.9865 \| 0.9863 \| nan \| 0.9794 \| 0.9880 \| 0.9897 \| 0.9890 \| 0.0 \| 0.9711 \| 0.9685 \| 0.9754 \| 0.9775 \| \| 0.0188 \| 28.0 \| 1400 \| 0.0383 \| 0.7783 \| 0.9865 \| 0.9862 \| nan \| 0.9779 \| 0.9893 \| 0.9884 \| 0.9903 \| 0.0 \| 0.9705 \| 0.9682 \| 0.9754 \| 0.9776 \| \| 0.0188 \| 29.0 \| 1450 \| 0.0377 \| 0.7784 \| 0.9864 \| 0.9862 \| nan \| 0.9785 \| 0.9902 \| 0.9890 \| 0.9880 \| 0.0 \| 0.9703 \| 0.9680 \| 0.9759 \| 0.9775 \| \| 0.018 \| 30.0 \| 1500 \| 0.0378 \| 0.9732 \| 0.9866 \| 0.9863 \| nan \| 0.9794 \| 0.9885 \| 0.9888 \| 0.9895 \| nan \| 0.9710 \| 0.9683 \| 0.9757 \| 0.9777 \| \| 0.018 \| 31.0 \| 1550 \| 0.0379 \| 0.9730 \| 0.9865 \| 0.9862 \| nan \| 0.9794 \| 0.9875 \| 0.9901 \| 0.9890 \| nan \| 0.9710 \| 0.9681 \| 0.9753 \| 0.9776 \| \| 0.0175 \| 32.0 \| 1600 \| 0.0381 \| 0.9730 \| 0.9865 \| 0.9862 \| nan \| 0.9792 \| 0.9884 \| 0.9894 \| 0.9889 \| nan \| 0.9708 \| 0.9682 \| 0.9755 \| 0.9775 \| \| 0.0175 \| 33.0 \| 1650 \| 0.0394 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9783 \| 0.9896 \| 0.9894 \| 0.9886 \| 0.0 \| 0.9705 \| 0.9679 \| 0.9758 \| 0.9777 \| \| 0.0171 \| 34.0 \| 1700 \| 0.0390 \| 0.7784 \| 0.9865 \| 0.9863 \| nan \| 0.9800 \| 0.9871 \| 0.9902 \| 0.9887 \| 0.0 \| 0.9712 \| 0.9682 \| 0.9753 \| 0.9775 \| \| 0.0171 \| 35.0 \| 1750 \| 0.0385 \| 0.9729 \| 0.9865 \| 0.9862 \| nan \| 0.9790 \| 0.9878 \| 0.9892 \| 0.9899 \| nan \| 0.9710 \| 0.9680 \| 0.9754 \| 0.9774 \| \| 0.0166 \| 36.0 \| 1800 \| 0.0384 \| 0.9731 \| 0.9865 \| 0.9863 \| nan \| 0.9791 \| 0.9884 \| 0.9889 \| 0.9897 \| nan \| 0.9711 \| 0.9682 \| 0.9756 \| 0.9775 \| \| 0.0166 \| 37.0 \| 1850 \| 0.0389 \| 0.9730 \| 0.9865 \| 0.9862 \| nan \| 0.9794 \| 0.9875 \| 0.9891 \| 0.9898 \| nan \| 0.9711 \| 0.9680 \| 0.9754 \| 0.9775 \| \| 0.0162 \| 38.0 \| 1900 \| 0.0375 \| 0.9731 \| 0.9865 \| 0.9863 \| nan \| 0.9797 \| 0.9879 \| 0.9901 \| 0.9884 \| nan \| 0.9711 \| 0.9681 \| 0.9755 \| 0.9777 \| \| 0.0162 \| 39.0 \| 1950 \| 0.0389 \| 0.9731 \| 0.9866 \| 0.9863 \| nan \| 0.9786 \| 0.9891 \| 0.9891 \| 0.9894 \| nan \| 0.9709 \| 0.9681 \| 0.9759 \| 0.9776 \| \| 0.0158 \| 40.0 \| 2000 \| 0.0396 \| 0.9730 \| 0.9865 \| 0.9862 \| nan \| 0.9783 \| 0.9897 \| 0.9894 \| 0.9886 \| nan \| 0.9705 \| 0.9678 \| 0.9761 \| 0.9777 \| \| 0.0158 \| 41.0 \| 2050 \| 0.0397 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9788 \| 0.9889 \| 0.9887 \| 0.9895 \| 0.0 \| 0.9708 \| 0.9679 \| 0.9759 \| 0.9773 \| \| 0.0156 \| 42.0 \| 2100 \| 0.0401 \| 0.9730 \| 0.9865 \| 0.9862 \| nan \| 0.9782 \| 0.9889 \| 0.9890 \| 0.9898 \| nan \| 0.9707 \| 0.9678 \| 0.9758 \| 0.9775 \| \| 0.0156 \| 43.0 \| 2150 \| 0.0399 \| 0.9730 \| 0.9865 \| 0.9862 \| nan \| 0.9789 \| 0.9886 \| 0.9896 \| 0.9889 \| nan \| 0.9708 \| 0.9678 \| 0.9757 \| 0.9777 \| \| 0.0154 \| 44.0 \| 2200 \| 0.0407 \| 0.9728 \| 0.9864 \| 0.9861 \| nan \| 0.9781 \| 0.9900 \| 0.9884 \| 0.9891 \| nan \| 0.9702 \| 0.9673 \| 0.9762 \| 0.9776 \| \| 0.0154 \| 45.0 \| 2250 \| 0.0405 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9785 \| 0.9901 \| 0.9896 \| 0.9877 \| 0.0 \| 0.9706 \| 0.9675 \| 0.9761 \| 0.9776 \| \| 0.0151 \| 46.0 \| 2300 \| 0.0411 \| 0.7782 \| 0.9864 \| 0.9861 \| nan \| 0.9784 \| 0.9903 \| 0.9901 \| 0.9866 \| 0.0 \| 0.9704 \| 0.9673 \| 0.9758 \| 0.9775 \| \| 0.0151 \| 47.0 \| 2350 \| 0.0394 \| 0.9732 \| 0.9866 \| 0.9863 \| nan \| 0.9790 \| 0.9896 \| 0.9890 \| 0.9886 \| nan \| 0.9709 \| 0.9681 \| 0.9759 \| 0.9777 \| \| 0.015 \| 48.0 \| 2400 \| 0.0405 \| 0.7784 \| 0.9865 \| 0.9863 \| nan \| 0.9787 \| 0.9885 \| 0.9892 \| 0.9898 \| 0.0 \| 0.9708 \| 0.9677 \| 0.9757 \| 0.9780 \| \| 0.015 \| 49.0 \| 2450 \| 0.0399 \| 0.7786 \| 0.9866 \| 0.9863 \| nan \| 0.9787 \| 0.9905 \| 0.9882 \| 0.9888 \| 0.0 \| 0.9707 \| 0.9678 \| 0.9764 \| 0.9779 \| \| 0.0149 \| 50.0 \| 2500 \| 0.0410 \| 0.7783 \| 0.9864 \| 0.9861 \| nan \| 0.9781 \| 0.9895 \| 0.9889 \| 0.9891 \| 0.0 \| 0.9705 \| 0.9673 \| 0.9761 \| 0.9776 \| \| 0.0149 \| 51.0 \| 2550 \| 0.0405 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9793 \| 0.9898 \| 0.9895 \| 0.9872 \| 0.0 \| 0.9707 \| 0.9676 \| 0.9763 \| 0.9776 \| \| 0.0145 \| 52.0 \| 2600 \| 0.0402 \| 0.7785 \| 0.9866 \| 0.9863 \| nan \| 0.9788 \| 0.9895 \| 0.9893 \| 0.9887 \| 0.0 \| 0.9710 \| 0.9678 \| 0.9760 \| 0.9778 \| \| 0.0145 \| 53.0 \| 2650 \| 0.0401 \| 0.7786 \| 0.9866 \| 0.9864 \| nan \| 0.9791 \| 0.9889 \| 0.9898 \| 0.9887 \| 0.0 \| 0.9710 \| 0.9680 \| 0.9761 \| 0.9780 \| \| 0.0144 \| 54.0 \| 2700 \| 0.0392 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9795 \| 0.9888 \| 0.9887 \| 0.9896 \| 0.0 \| 0.9714 \| 0.9682 \| 0.9761 \| 0.9777 \| \| 0.0144 \| 55.0 \| 2750 \| 0.0409 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9787 \| 0.9886 \| 0.9895 \| 0.9891 \| 0.0 \| 0.9706 \| 0.9675 \| 0.9760 \| 0.9777 \| \| 0.0141 \| 56.0 \| 2800 \| 0.0410 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9779 \| 0.9897 \| 0.9897 \| 0.9887 \| 0.0 \| 0.9707 \| 0.9675 \| 0.9759 \| 0.9778 \| \| 0.0141 \| 57.0 \| 2850 \| 0.0412 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9780 \| 0.9898 \| 0.9891 \| 0.9891 \| 0.0 \| 0.9707 \| 0.9676 \| 0.9761 \| 0.9776 \| \| 0.014 \| 58.0 \| 2900 \| 0.0403 \| 0.9732 \| 0.9866 \| 0.9863 \| nan \| 0.9794 \| 0.9889 \| 0.9889 \| 0.9891 \| nan \| 0.9713 \| 0.9680 \| 0.9761 \| 0.9775 \| \| 0.014 \| 59.0 \| 2950 \| 0.0404 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9787 \| 0.9899 \| 0.9889 \| 0.9892 \| 0.0 \| 0.9711 \| 0.9680 \| 0.9763 \| 0.9779 \| \| 0.0139 \| 60.0 \| 3000 \| 0.0412 \| 0.7783 \| 0.9865 \| 0.9862 \| nan \| 0.9786 \| 0.9893 \| 0.9900 \| 0.9879 \| 0.0 \| 0.9708 \| 0.9675 \| 0.9758 \| 0.9775 \| \| 0.0139 \| 61.0 \| 3050 \| 0.0410 \| 0.7785 \| 0.9866 \| 0.9863 \| nan \| 0.9789 \| 0.9893 \| 0.9901 \| 0.9879 \| 0.0 \| 0.9708 \| 0.9676 \| 0.9762 \| 0.9780 \| \| 0.0138 \| 62.0 \| 3100 \| 0.0413 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9778 \| 0.9896 \| 0.9893 \| 0.9894 \| 0.0 \| 0.9705 \| 0.9675 \| 0.9763 \| 0.9779 \| \| 0.0138 \| 63.0 \| 3150 \| 0.0400 \| 0.7786 \| 0.9866 \| 0.9863 \| nan \| 0.9794 \| 0.9887 \| 0.9908 \| 0.9874 \| 0.0 \| 0.9715 \| 0.9681 \| 0.9757 \| 0.9776 \| \| 0.0138 \| 64.0 \| 3200 \| 0.0401 \| 0.7786 \| 0.9866 \| 0.9864 \| nan \| 0.9800 \| 0.9888 \| 0.9904 \| 0.9873 \| 0.0 \| 0.9715 \| 0.9682 \| 0.9758 \| 0.9776 \| \| 0.0138 \| 65.0 \| 3250 \| 0.0414 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9788 \| 0.9888 \| 0.9905 \| 0.9879 \| 0.0 \| 0.9708 \| 0.9675 \| 0.9759 \| 0.9776 \| \| 0.0136 \| 66.0 \| 3300 \| 0.0397 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9796 \| 0.9895 \| 0.9897 \| 0.9880 \| 0.0 \| 0.9714 \| 0.9683 \| 0.9763 \| 0.9776 \| \| 0.0136 \| 67.0 \| 3350 \| 0.0417 \| 0.7783 \| 0.9864 \| 0.9861 \| nan \| 0.9777 \| 0.9894 \| 0.9903 \| 0.9884 \| 0.0 \| 0.9702 \| 0.9671 \| 0.9761 \| 0.9779 \| \| 0.0135 \| 68.0 \| 3400 \| 0.0409 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9790 \| 0.9898 \| 0.9908 \| 0.9862 \| 0.0 \| 0.9711 \| 0.9678 \| 0.9758 \| 0.9773 \| \| 0.0135 \| 69.0 \| 3450 \| 0.0399 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9796 \| 0.9896 \| 0.9887 \| 0.9888 \| 0.0 \| 0.9714 \| 0.9681 \| 0.9764 \| 0.9778 \| \| 0.0133 \| 70.0 \| 3500 \| 0.0407 \| 0.7785 \| 0.9865 \| 0.9863 \| nan \| 0.9792 \| 0.9903 \| 0.9901 \| 0.9866 \| 0.0 \| 0.9713 \| 0.9676 \| 0.9761 \| 0.9775 \| \| 0.0133 \| 71.0 \| 3550 \| 0.0407 \| 0.7786 \| 0.9866 \| 0.9864 \| nan \| 0.9787 \| 0.9896 \| 0.9892 \| 0.9890 \| 0.0 \| 0.9712 \| 0.9679 \| 0.9761 \| 0.9778 \| \| 0.0131 \| 72.0 \| 3600 \| 0.0394 \| 0.7789 \| 0.9868 \| 0.9865 \| nan \| 0.9790 \| 0.9899 \| 0.9895 \| 0.9887 \| 0.0 \| 0.9714 \| 0.9681 \| 0.9766 \| 0.9781 \| \| 0.0131 \| 73.0 \| 3650 \| 0.0410 \| 0.7785 \| 0.9865 \| 0.9863 \| nan \| 0.9796 \| 0.9897 \| 0.9903 \| 0.9865 \| 0.0 \| 0.9713 \| 0.9678 \| 0.9759 \| 0.9774 \| \| 0.0132 \| 74.0 \| 3700 \| 0.0412 \| 0.7785 \| 0.9866 \| 0.9863 \| nan \| 0.9791 \| 0.9900 \| 0.9901 \| 0.9871 \| 0.0 \| 0.9713 \| 0.9678 \| 0.9761 \| 0.9774 \| \| 0.0132 \| 75.0 \| 3750 \| 0.0412 \| 0.7786 \| 0.9866 \| 0.9863 \| nan \| 0.9785 \| 0.9902 \| 0.9898 \| 0.9879 \| 0.0 \| 0.9711 \| 0.9676 \| 0.9763 \| 0.9779 \| \| 0.0131 \| 76.0 \| 3800 \| 0.0396 \| 0.7786 \| 0.9866 \| 0.9864 \| nan \| 0.9798 \| 0.9893 \| 0.9904 \| 0.9870 \| 0.0 \| 0.9716 \| 0.9682 \| 0.9760 \| 0.9775 \| \| 0.0131 \| 77.0 \| 3850 \| 0.0418 \| 0.7784 \| 0.9865 \| 0.9862 \| nan \| 0.9789 \| 0.9896 \| 0.9905 \| 0.9871 \| 0.0 \| 0.9711 \| 0.9676 \| 0.9760 \| 0.9775 \| \| 0.013 \| 78.0 \| 3900 \| 0.0396 \| 0.7786 \| 0.9866 \| 0.9864 \| nan \| 0.9787 \| 0.9899 \| 0.9906 \| 0.9872 \| 0.0 \| 0.9713 \| 0.9678 \| 0.9760 \| 0.9779 \| \| 0.013 \| 79.0 \| 3950 \| 0.0398 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9794 \| 0.9898 \| 0.9905 \| 0.9869 \| 0.0 \| 0.9715 \| 0.9680 \| 0.9762 \| 0.9777 \| \| 0.0128 \| 80.0 \| 4000 \| 0.0402 \| 0.7788 \| 0.9867 \| 0.9865 \| nan \| 0.9789 \| 0.9898 \| 0.9896 \| 0.9885 \| 0.0 \| 0.9714 \| 0.9680 \| 0.9765 \| 0.9779 \| \| 0.0128 \| 81.0 \| 4050 \| 0.0404 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9787 \| 0.9903 \| 0.9902 \| 0.9874 \| 0.0 \| 0.9713 \| 0.9677 \| 0.9763 \| 0.9779 \| \| 0.0127 \| 82.0 \| 4100 \| 0.0397 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9794 \| 0.9896 \| 0.9901 \| 0.9877 \| 0.0 \| 0.9716 \| 0.9681 \| 0.9762 \| 0.9778 \| \| 0.0127 \| 83.0 \| 4150 \| 0.0411 \| 0.7786 \| 0.9866 \| 0.9863 \| nan \| 0.9786 \| 0.9898 \| 0.9899 \| 0.9881 \| 0.0 \| 0.9712 \| 0.9677 \| 0.9763 \| 0.9778 \| \| 0.0127 \| 84.0 \| 4200 \| 0.0406 \| 0.7788 \| 0.9867 \| 0.9865 \| nan \| 0.9787 \| 0.9903 \| 0.9890 \| 0.9889 \| 0.0 \| 0.9713 \| 0.9680 \| 0.9766 \| 0.9781 \| \| 0.0127 \| 85.0 \| 4250 \| 0.0413 \| 0.7786 \| 0.9866 \| 0.9864 \| nan \| 0.9787 \| 0.9900 \| 0.9888 \| 0.9891 \| 0.0 \| 0.9711 \| 0.9677 \| 0.9764 \| 0.9779 \| \| 0.0126 \| 86.0 \| 4300 \| 0.0400 \| 0.7788 \| 0.9867 \| 0.9865 \| nan \| 0.9792 \| 0.9904 \| 0.9895 \| 0.9878 \| 0.0 \| 0.9715 \| 0.9681 \| 0.9765 \| 0.9778 \| \| 0.0126 \| 87.0 \| 4350 \| 0.0397 \| 0.7788 \| 0.9868 \| 0.9865 \| nan \| 0.9789 \| 0.9898 \| 0.9898 \| 0.9885 \| 0.0 \| 0.9715 \| 0.9682 \| 0.9765 \| 0.9780 \| \| 0.0125 \| 88.0 \| 4400 \| 0.0398 \| 0.7788 \| 0.9868 \| 0.9865 \| nan \| 0.9791 \| 0.9903 \| 0.9894 \| 0.9883 \| 0.0 \| 0.9716 \| 0.9681 \| 0.9767 \| 0.9779 \| \| 0.0125 \| 89.0 \| 4450 \| 0.0400 \| 0.7787 \| 0.9867 \| 0.9865 \| nan \| 0.9795 \| 0.9898 \| 0.9902 \| 0.9872 \| 0.0 \| 0.9716 \| 0.9682 \| 0.9763 \| 0.9776 \| \| 0.0125 \| 90.0 \| 4500 \| 0.0397 \| 0.7788 \| 0.9867 \| 0.9865 \| nan \| 0.9788 \| 0.9902 \| 0.9893 \| 0.9887 \| 0.0 \| 0.9716 \| 0.9680 \| 0.9765 \| 0.9779 \| \| 0.0125 \| 91.0 \| 4550 \| 0.0400 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9790 \| 0.9901 \| 0.9903 \| 0.9875 \| 0.0 \| 0.9715 \| 0.9680 \| 0.9762 \| 0.9779 \| \| 0.0125 \| 92.0 \| 4600 \| 0.0392 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9790 \| 0.9903 \| 0.9898 \| 0.9878 \| 0.0 \| 0.9716 \| 0.9680 \| 0.9765 \| 0.9777 \| \| 0.0125 \| 93.0 \| 4650 \| 0.0403 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9791 \| 0.9900 \| 0.9905 \| 0.9873 \| 0.0 \| 0.9716 \| 0.9681 \| 0.9763 \| 0.9777 \| \| 0.0123 \| 94.0 \| 4700 \| 0.0396 \| 0.7789 \| 0.9868 \| 0.9865 \| nan \| 0.9797 \| 0.9898 \| 0.9903 \| 0.9874 \| 0.0 \| 0.9718 \| 0.9684 \| 0.9764 \| 0.9778 \| \| 0.0123 \| 95.0 \| 4750 \| 0.0405 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9790 \| 0.9901 \| 0.9903 \| 0.9874 \| 0.0 \| 0.9715 \| 0.9679 \| 0.9764 \| 0.9778 \| \| 0.0122 \| 96.0 \| 4800 \| 0.0394 \| 0.7789 \| 0.9868 \| 0.9865 \| nan \| 0.9793 \| 0.9896 \| 0.9898 \| 0.9884 \| 0.0 \| 0.9717 \| 0.9682 \| 0.9764 \| 0.9780 \| \| 0.0122 \| 97.0 \| 4850 \| 0.0396 \| 0.7789 \| 0.9868 \| 0.9865 \| nan \| 0.9790 \| 0.9900 \| 0.9900 \| 0.9882 \| 0.0 \| 0.9716 \| 0.9681 \| 0.9766 \| 0.9780 \| \| 0.0122 \| 98.0 \| 4900 \| 0.0399 \| 0.7788 \| 0.9867 \| 0.9865 \| nan \| 0.9797 \| 0.9900 \| 0.9904 \| 0.9870 \| 0.0 \| 0.9718 \| 0.9682 \| 0.9764 \| 0.9776 \| \| 0.0122 \| 99.0 \| 4950 \| 0.0394 \| 0.7789 \| 0.9868 \| 0.9865 \| nan \| 0.9793 \| 0.9896 \| 0.9897 \| 0.9885 \| 0.0 \| 0.9717 \| 0.9682 \| 0.9766 \| 0.9780 \| \| 0.0122 \| 100.0 \| 5000 \| 0.0383 \| 0.7790 \| 0.9868 \| 0.9866 \| nan \| 0.9804 \| 0.9899 \| 0.9895 \| 0.9876 \| 0.0 \| 0.9720 \| 0.9686 \| 0.9767 \| 0.9777 \| \| 0.0122 \| 101.0 \| 5050 \| 0.0399 \| 0.7788 \| 0.9867 \| 0.9865 \| nan \| 0.9794 \| 0.9904 \| 0.9895 \| 0.9877 \| 0.0 \| 0.9716 \| 0.9680 \| 0.9766 \| 0.9779 \| \| 0.0121 \| 102.0 \| 5100 \| 0.0392 \| 0.7790 \| 0.9868 \| 0.9866 \| nan \| 0.9796 \| 0.9898 \| 0.9889 \| 0.9890 \| 0.0 \| 0.9718 \| 0.9685 \| 0.9767 \| 0.9779 \| \| 0.0121 \| 103.0 \| 5150 \| 0.0393 \| 0.7788 \| 0.9867 \| 0.9865 \| nan \| 0.9788 \| 0.9901 \| 0.9900 \| 0.9881 \| 0.0 \| 0.9715 \| 0.9679 \| 0.9765 \| 0.9781 \| \| 0.012 \| 104.0 \| 5200 \| 0.0400 \| 0.7788 \| 0.9867 \| 0.9865 \| nan \| 0.9790 \| 0.9894 \| 0.9904 \| 0.9881 \| 0.0 \| 0.9716 \| 0.9682 \| 0.9763 \| 0.9779 \| \| 0.012 \| 105.0 \| 5250 \| 0.0393 \| 0.7789 \| 0.9868 \| 0.9865 \| nan \| 0.9796 \| 0.9894 \| 0.9904 \| 0.9878 \| 0.0 \| 0.9718 \| 0.9683 \| 0.9764 \| 0.9780 \| \| 0.012 \| 106.0 \| 5300 \| 0.0390 \| 0.7789 \| 0.9868 \| 0.9866 \| nan \| 0.9794 \| 0.9900 \| 0.9890 \| 0.9888 \| 0.0 \| 0.9719 \| 0.9683 \| 0.9766 \| 0.9780 \| \| 0.012 \| 107.0 \| 5350 \| 0.0383 \| 0.7790 \| 0.9868 \| 0.9866 \| nan \| 0.9801 \| 0.9899 \| 0.9903 \| 0.9870 \| 0.0 \| 0.9721 \| 0.9684 \| 0.9765 \| 0.9779 \| \| 0.0119 \| 108.0 \| 5400 \| 0.0380 \| 0.7792 \| 0.9870 \| 0.9868 \| nan \| 0.9807 \| 0.9892 \| 0.9897 \| 0.9883 \| 0.0 \| 0.9724 \| 0.9690 \| 0.9768 \| 0.9780 \| \| 0.0119 \| 109.0 \| 5450 \| 0.0400 \| 0.7787 \| 0.9867 \| 0.9864 \| nan \| 0.9786 \| 0.9902 \| 0.9902 \| 0.9876 \| 0.0 \| 0.9714 \| 0.9677 \| 0.9764 \| 0.9778 \| \| 0.0119 \| 110.0 \| 5500 \| 0.0385 \| 0.7791 \| 0.9869 \| 0.9867 \| nan \| 0.9801 \| 0.9894 \| 0.9891 \| 0.9889 \| 0.0 \| 0.9721 \| 0.9686 \| 0.9768 \| 0.9780 \| \| 0.0119 \| 111.0 \| 5550 \| 0.0385 \| 0.7790 \| 0.9869 \| 0.9866 \| nan \| 0.9798 \| 0.9896 \| 0.9902 \| 0.9879 \| 0.0 \| 0.9719 \| 0.9685 \| 0.9767 \| 0.9781 \| \| 0.0118 \| 112.0 \| 5600 \| 0.0377 \| 0.7791 \| 0.9869 \| 0.9867 \| nan \| 0.9798 \| 0.9891 \| 0.9897 \| 0.9891 \| 0.0 \| 0.9722 \| 0.9687 \| 0.9766 \| 0.9782 \| \| 0.0118 \| 113.0 \| 5650 \| 0.0388 \| 0.7790 \| 0.9869 \| 0.9866 \| nan \| 0.9794 \| 0.9899 \| 0.9904 \| 0.9878 \| 0.0 \| 0.9719 \| 0.9683 \| 0.9767 \| 0.9781 \| \| 0.0118 \| 114.0 \| 5700 \| 0.0391 \| 0.7789 \| 0.9868 \| 0.9866 \| nan \| 0.9797 \| 0.9891 \| 0.9906 \| 0.9880 \| 0.0 \| 0.9719 \| 0.9683 \| 0.9763 \| 0.9781 \| \| 0.0118 \| 115.0 \| 5750 \| 0.0390 \| 0.7789 \| 0.9868 \| 0.9866 \| nan \| 0.9796 \| 0.9902 \| 0.9899 \| 0.9876 \| 0.0 \| 0.9719 \| 0.9683 \| 0.9766 \| 0.9779 \| \| 0.0118 \| 116.0 \| 5800 \| 0.0390 \| 0.7789 \| 0.9868 \| 0.9866 \| nan \| 0.9795 \| 0.9899 \| 0.9896 \| 0.9882 \| 0.0 \| 0.9718 \| 0.9682 \| 0.9767 \| 0.9779 \| \| 0.0118 \| 117.0 \| 5850 \| 0.0394 \| 0.7788 \| 0.9867 \| 0.9865 \| nan \| 0.9791 \| 0.9899 \| 0.9896 \| 0.9883 \| 0.0 \| 0.9717 \| 0.9679 \| 0.9765 \| 0.9778 \| \| 0.0117 \| 118.0 \| 5900 \| 0.0386 \| 0.7789 \| 0.9868 \| 0.9866 \| nan \| 0.9796 \| 0.9898 \| 0.9900 \| 0.9879 \| 0.0 \| 0.9719 \| 0.9682 \| 0.9766 \| 0.9779 \| \| 0.0117 \| 119.0 \| 5950 \| 0.0386 \| 0.7791 \| 0.9869 \| 0.9867 \| nan \| 0.9800 \| 0.9895 \| 0.9896 \| 0.9885 \| 0.0 \| 0.9721 \| 0.9686 \| 0.9767 \| 0.9781 \| \| 0.0117 \| 120.0 \| 6000 \| 0.0388 \| 0.7790 \| 0.9869 \| 0.9866 \| nan \| 0.9796 \| 0.9899 \| 0.9902 \| 0.9878 \| 0.0 \| 0.9719 \| 0.9684 \| 0.9767 \| 0.9781 \| \| 0.0117 \| 121.0 \| 6050 \| 0.0389 \| 0.7790 \| 0.9868 \| 0.9866 \| nan \| 0.9800 \| 0.9896 \| 0.9894 \| 0.9883 \| 0.0 \| 0.9721 \| 0.9684 \| 0.9767 \| 0.9778 \| \| 0.0116 \| 122.0 \| 6100 \| 0.0384 \| 0.7790 \| 0.9869 \| 0.9866 \| nan \| 0.9796 \| 0.9896 \| 0.9897 \| 0.9886 \| 0.0 \| 0.9720 \| 0.9684 \| 0.9767 \| 0.9780 \| \| 0.0116 \| 123.0 \| 6150 \| 0.0386 \| 0.7789 \| 0.9868 \| 0.9865 \| nan \| 0.9793 \| 0.9899 \| 0.9901 \| 0.9879 \| 0.0 \| 0.9718 \| 0.9680 \| 0.9765 \| 0.9781 \| \| 0.0115 \| 124.0 \| 6200 \| 0.0383 \| 0.7792 \| 0.9870 \| 0.9867 \| nan \| 0.9802 \| 0.9890 \| 0.9900 \| 0.9888 \| 0.0 \| 0.9722 \| 0.9688 \| 0.9767 \| 0.9781 \| \| 0.0115 \| 125.0 \| 6250 \| 0.0381 \| 0.7790 \| 0.9869 \| 0.9866 \| nan \| 0.9796 \| 0.9892 \| 0.9900 \| 0.9888 \| 0.0 \| 0.9721 \| 0.9685 \| 0.9766 \| 0.9780 \| \| 0.0115 \| 126.0 \| 6300 \| 0.0383 \| 0.7791 \| 0.9869 \| 0.9867 \| nan \| 0.9797 \| 0.9894 \| 0.9894 \| 0.9893 \| 0.0 \| 0.9720 \| 0.9686 \| 0.9767 \| 0.9782 \| \| 0.0115 \| 127.0 \| 6350 \| 0.0384 \| 0.7790 \| 0.9869 \| 0.9866 \| nan \| 0.9797 \| 0.9895 \| 0.9901 \| 0.9881 \| 0.0 \| 0.9719 \| 0.9684 \| 0.9766 \| 0.9781 \| \| 0.0115 \| 128.0 \| 6400 \| 0.0377 \| 0.7792 \| 0.9870 \| 0.9867 \| nan \| 0.9801 \| 0.9891 \| 0.9896 \| 0.9891 \| 0.0 \| 0.9722 \| 0.9688 \| 0.9767 \| 0.9781 \| \| 0.0115 \| 129.0 \| 6450 \| 0.0383 \| 0.7791 \| 0.9869 \| 0.9867 \| nan \| 0.9800 \| 0.9898 \| 0.9899 \| 0.9880 \| 0.0 \| 0.9721 \| 0.9685 \| 0.9768 \| 0.9782 \| \| 0.0115 \| 130.0 \| 6500 \| 0.0377 \| 0.7791 \| 0.9870 \| 0.9867 \| nan \| 0.9797 \| 0.9895 \| 0.9901 \| 0.9885 \| 0.0 \| 0.9723 \| 0.9687 \| 0.9767 \| 0.9781 \| \| 0.0115 \| 131.0 \| 6550 \| 0.0380 \| 0.7791 \| 0.9869 \| 0.9867 \| nan \| 0.9800 \| 0.9891 \| 0.9897 \| 0.9890 \| 0.0 \| 0.9722 \| 0.9687 \| 0.9767 \| 0.9780 \| \| 0.0114 \| 132.0 \| 6600 \| 0.0377 \| 0.7792 \| 0.9870 \| 0.9868 \| nan \| 0.9799 \| 0.9893 \| 0.9901 \| 0.9887 \| 0.0 \| 0.9724 \| 0.9689 \| 0.9766 \| 0.9782 \| \| 0.0114 \| 133.0 \| 6650 \| 0.0378 \| 0.7792 \| 0.9870 \| 0.9867 \| nan \| 0.9801 \| 0.9899 \| 0.9897 \| 0.9882 \| 0.0 \| 0.9722 \| 0.9687 \| 0.9769 \| 0.9782 \| \| 0.0114 \| 134.0 \| 6700 \| 0.0379 \| 0.7791 \| 0.9869 \| 0.9867 \| nan \| 0.9801 \| 0.9896 \| 0.9902 \| 0.9879 \| 0.0 \| 0.9723 \| 0.9688 \| 0.9767 \| 0.9780 \| \| 0.0114 \| 135.0 \| 6750 \| 0.0374 \| 0.7793 \| 0.9870 \| 0.9868 \| nan \| 0.9803 \| 0.9894 \| 0.9899 \| 0.9884 \| 0.0 \| 0.9724 \| 0.9690 \| 0.9768 \| 0.9782 \| \| 0.0113 \| 136.0 \| 6800 \| 0.0386 \| 0.7790 \| 0.9869 \| 0.9866 \| nan \| 0.9796 \| 0.9897 \| 0.9903 \| 0.9878 \| 0.0 \| 0.9720 \| 0.9683 \| 0.9766 \| 0.9781 \| \| 0.0113 \| 137.0 \| 6850 \| 0.0378 \| 0.9739 \| 0.9870 \| 0.9867 \| nan \| 0.9802 \| 0.9895 \| 0.9900 \| 0.9880 \| nan \| 0.9724 \| 0.9688 \| 0.9766 \| 0.9779 \| \| 0.0114 \| 138.0 \| 6900 \| 0.0378 \| 0.9740 \| 0.9870 \| 0.9868 \| nan \| 0.9800 \| 0.9895 \| 0.9893 \| 0.9893 \| nan \| 0.9722 \| 0.9688 \| 0.9769 \| 0.9783 \| \| 0.0114 \| 139.0 \| 6950 \| 0.0380 \| 0.7791 \| 0.9869 \| 0.9867 \| nan \| 0.9797 \| 0.9896 \| 0.9897 \| 0.9888 \| 0.0 \| 0.9722 \| 0.9685 \| 0.9767 \| 0.9782 \| \| 0.0113 \| 140.0 \| 7000 \| 0.0374 \| 0.7793 \| 0.9871 \| 0.9868 \| nan \| 0.9803 \| 0.9893 \| 0.9899 \| 0.9887 \| 0.0 \| 0.9725 \| 0.9690 \| 0.9768 \| 0.9783 \| \| 0.0113 \| 141.0 \| 7050 \| 0.0378 \| 0.7792 \| 0.9870 \| 0.9868 \| nan \| 0.9801 \| 0.9894 \| 0.9900 \| 0.9886 \| 0.0 \| 0.9724 \| 0.9689 \| 0.9767 \| 0.9781 \| \| 0.0112 \| 142.0 \| 7100 \| 0.0380 \| 0.9740 \| 0.9870 \| 0.9868 \| nan \| 0.9801 \| 0.9899 \| 0.9897 \| 0.9882 \| nan \| 0.9724 \| 0.9687 \| 0.9768 \| 0.9782 \| \| 0.0112 \| 143.0 \| 7150 \| 0.0380 \| 0.9740 \| 0.9870 \| 0.9868 \| nan \| 0.9800 \| 0.9897 \| 0.9899 \| 0.9883 \| nan \| 0.9724 \| 0.9688 \| 0.9768 \| 0.9781 \| \| 0.0112 \| 144.0 \| 7200 \| 0.0378 \| 0.9741 \| 0.9870 \| 0.9868 \| nan \| 0.9802 \| 0.9896 \| 0.9897 \| 0.9887 \| nan \| 0.9725 \| 0.9690 \| 0.9768 \| 0.9781 \| \| 0.0112 \| 145.0 \| 7250 \| 0.0376 \| 0.7793 \| 0.9870 \| 0.9868 \| nan \| 0.9806 \| 0.9892 \| 0.9903 \| 0.9880 \| 0.0 \| 0.9726 \| 0.9690 \| 0.9767 \| 0.9782 \| \| 0.0112 \| 146.0 \| 7300 \| 0.0380 \| 0.7792 \| 0.9870 \| 0.9867 \| nan \| 0.9801 \| 0.9899 \| 0.9898 \| 0.9880 \| 0.0 \| 0.9724 \| 0.9687 \| 0.9767 \| 0.9780 \| \| 0.0112 \| 147.0 \| 7350 \| 0.0381 \| 0.9740 \| 0.9870 \| 0.9867 \| nan \| 0.9800 \| 0.9900 \| 0.9899 \| 0.9880 \| nan \| 0.9723 \| 0.9687 \| 0.9767 \| 0.9781 \| \| 0.0111 \| 148.0 \| 7400 \| 0.0374 \| 0.9742 \| 0.9871 \| 0.9868 \| nan \| 0.9805 \| 0.9895 \| 0.9900 \| 0.9883 \| nan \| 0.9726 \| 0.9690 \| 0.9768 \| 0.9782 \| \| 0.0111 \| 149.0 \| 7450 \| 0.0378 \| 0.9740 \| 0.9870 \| 0.9868 \| nan \| 0.9801 \| 0.9897 \| 0.9902 \| 0.9879 \| nan \| 0.9724 \| 0.9687 \| 0.9767 \| 0.9781 \| \| 0.0112 \| 150.0 \| 7500 \| 0.0377 \| 0.9741 \| 0.9870 \| 0.9868 \| nan \| 0.9800 \| 0.9892 \| 0.9897 \| 0.9891 \| nan \| 0.9725 \| 0.9690 \| 0.9767 \| 0.9781 \| \| 0.0112 \| 151.0 \| 7550 \| 0.0377 \| 0.9742 \| 0.9871 \| 0.9868 \| nan \| 0.9802 \| 0.9893 \| 0.9895 \| 0.9893 \| nan \| 0.9725 \| 0.9691 \| 0.9768 \| 0.9782 \| \| 0.0111 \| 152.0 \| 7600 \| 0.0374 \| 0.9741 \| 0.9870 \| 0.9868 \| nan \| 0.9804 \| 0.9898 \| 0.9898 \| 0.9883 \| nan \| 0.9726 \| 0.9690 \| 0.9768 \| 0.9782 \| \| 0.0111 \| 153.0 \| 7650 \| 0.0380 \| 0.9740 \| 0.9870 \| 0.9868 \| nan \| 0.9800 \| 0.9898 \| 0.9897 \| 0.9884 \| nan \| 0.9725 \| 0.9688 \| 0.9767 \| 0.9781 \| \| 0.0111 \| 154.0 \| 7700 \| 0.0373 \| 0.9742 \| 0.9871 \| 0.9869 \| nan \| 0.9805 \| 0.9891 \| 0.9901 \| 0.9887 \| nan \| 0.9727 \| 0.9692 \| 0.9767 \| 0.9782 \| \| 0.0111 \| 155.0 \| 7750 \| 0.0375 \| 0.9742 \| 0.9871 \| 0.9868 \| nan \| 0.9804 \| 0.9896 \| 0.9893 \| 0.9891 \| nan \| 0.9727 \| 0.9692 \| 0.9768 \| 0.9781 \| \| 0.0111 \| 156.0 \| 7800 \| 0.0378 \| 0.9741 \| 0.9870 \| 0.9868 \| nan \| 0.9801 \| 0.9898 \| 0.9897 \| 0.9886 \| nan \| 0.9727 \| 0.9689 \| 0.9767 \| 0.9781 \| \| 0.0111 \| 157.0 \| 7850 \| 0.0376 \| 0.9742 \| 0.9871 \| 0.9868 \| nan \| 0.9805 \| 0.9891 \| 0.9902 \| 0.9885 \| nan \| 0.9727 \| 0.9691 \| 0.9766 \| 0.9782 \| \| 0.0111 \| 158.0 \| 7900 \| 0.0375 \| 0.9742 \| 0.9871 \| 0.9868 \| nan \| 0.9804 \| 0.9893 \| 0.9899 \| 0.9887 \| nan \| 0.9727 \| 0.9691 \| 0.9767 \| 0.9782 \| \| 0.0111 \| 159.0 \| 7950 \| 0.0372 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9805 \| 0.9892 \| 0.9904 \| 0.9884 \| nan \| 0.9728 \| 0.9693 \| 0.9766 \| 0.9783 \| \| 0.0111 \| 160.0 \| 8000 \| 0.0367 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9809 \| 0.9896 \| 0.9898 \| 0.9882 \| nan \| 0.9730 \| 0.9693 \| 0.9768 \| 0.9782 \| \| 0.0111 \| 161.0 \| 8050 \| 0.0370 \| 0.9744 \| 0.9871 \| 0.9869 \| nan \| 0.9808 \| 0.9898 \| 0.9894 \| 0.9886 \| nan \| 0.9728 \| 0.9693 \| 0.9770 \| 0.9783 \| \| 0.0111 \| 162.0 \| 8100 \| 0.0371 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9806 \| 0.9892 \| 0.9901 \| 0.9885 \| nan \| 0.9729 \| 0.9694 \| 0.9767 \| 0.9782 \| \| 0.0111 \| 163.0 \| 8150 \| 0.0372 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9807 \| 0.9894 \| 0.9901 \| 0.9882 \| nan \| 0.9729 \| 0.9694 \| 0.9767 \| 0.9781 \| \| 0.011 \| 164.0 \| 8200 \| 0.0373 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9805 \| 0.9896 \| 0.9894 \| 0.9889 \| nan \| 0.9728 \| 0.9693 \| 0.9768 \| 0.9781 \| \| 0.011 \| 165.0 \| 8250 \| 0.0371 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9808 \| 0.9897 \| 0.9898 \| 0.9882 \| nan \| 0.9729 \| 0.9694 \| 0.9768 \| 0.9783 \| \| 0.011 \| 166.0 \| 8300 \| 0.0372 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9806 \| 0.9897 \| 0.9898 \| 0.9884 \| nan \| 0.9729 \| 0.9693 \| 0.9768 \| 0.9781 \| \| 0.011 \| 167.0 \| 8350 \| 0.0373 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9804 \| 0.9896 \| 0.9900 \| 0.9885 \| nan \| 0.9728 \| 0.9692 \| 0.9768 \| 0.9783 \| \| 0.011 \| 168.0 \| 8400 \| 0.0369 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9807 \| 0.9895 \| 0.9899 \| 0.9885 \| nan \| 0.9731 \| 0.9695 \| 0.9767 \| 0.9782 \| \| 0.011 \| 169.0 \| 8450 \| 0.0375 \| 0.9742 \| 0.9871 \| 0.9869 \| nan \| 0.9802 \| 0.9897 \| 0.9898 \| 0.9886 \| nan \| 0.9727 \| 0.9691 \| 0.9768 \| 0.9782 \| \| 0.0109 \| 170.0 \| 8500 \| 0.0363 \| 0.9746 \| 0.9873 \| 0.9871 \| nan \| 0.9814 \| 0.9892 \| 0.9894 \| 0.9891 \| nan \| 0.9734 \| 0.9699 \| 0.9769 \| 0.9782 \| \| 0.0109 \| 171.0 \| 8550 \| 0.0371 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9805 \| 0.9895 \| 0.9900 \| 0.9885 \| nan \| 0.9729 \| 0.9693 \| 0.9767 \| 0.9782 \| \| 0.011 \| 172.0 \| 8600 \| 0.0371 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9807 \| 0.9896 \| 0.9898 \| 0.9885 \| nan \| 0.9729 \| 0.9693 \| 0.9768 \| 0.9782 \| \| 0.011 \| 173.0 \| 8650 \| 0.0373 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9807 \| 0.9893 \| 0.9901 \| 0.9885 \| nan \| 0.9728 \| 0.9694 \| 0.9767 \| 0.9783 \| \| 0.0109 \| 174.0 \| 8700 \| 0.0372 \| 0.9744 \| 0.9872 \| 0.9869 \| nan \| 0.9806 \| 0.9894 \| 0.9898 \| 0.9889 \| nan \| 0.9729 \| 0.9694 \| 0.9768 \| 0.9783 \| \| 0.0109 \| 175.0 \| 8750 \| 0.0373 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9806 \| 0.9895 \| 0.9899 \| 0.9885 \| nan \| 0.9729 \| 0.9694 \| 0.9768 \| 0.9782 \| \| 0.0109 \| 176.0 \| 8800 \| 0.0371 \| 0.9744 \| 0.9872 \| 0.9869 \| nan \| 0.9808 \| 0.9894 \| 0.9898 \| 0.9886 \| nan \| 0.9730 \| 0.9694 \| 0.9768 \| 0.9782 \| \| 0.0109 \| 177.0 \| 8850 \| 0.0370 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9808 \| 0.9897 \| 0.9896 \| 0.9886 \| nan \| 0.9730 \| 0.9695 \| 0.9768 \| 0.9782 \| \| 0.0109 \| 178.0 \| 8900 \| 0.0373 \| 0.9744 \| 0.9872 \| 0.9869 \| nan \| 0.9808 \| 0.9895 \| 0.9899 \| 0.9885 \| nan \| 0.9729 \| 0.9694 \| 0.9768 \| 0.9783 \| \| 0.0109 \| 179.0 \| 8950 \| 0.0372 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9806 \| 0.9894 \| 0.9897 \| 0.9888 \| nan \| 0.9729 \| 0.9694 \| 0.9768 \| 0.9782 \| \| 0.0109 \| 180.0 \| 9000 \| 0.0368 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9811 \| 0.9894 \| 0.9897 \| 0.9885 \| nan \| 0.9731 \| 0.9696 \| 0.9768 \| 0.9781 \| \| 0.0109 \| 181.0 \| 9050 \| 0.0371 \| 0.9744 \| 0.9872 \| 0.9869 \| nan \| 0.9807 \| 0.9894 \| 0.9900 \| 0.9886 \| nan \| 0.9730 \| 0.9694 \| 0.9768 \| 0.9783 \| \| 0.0109 \| 182.0 \| 9100 \| 0.0370 \| 0.9744 \| 0.9872 \| 0.9869 \| nan \| 0.9808 \| 0.9894 \| 0.9898 \| 0.9887 \| nan \| 0.9730 \| 0.9695 \| 0.9768 \| 0.9782 \| \| 0.0109 \| 183.0 \| 9150 \| 0.0368 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9810 \| 0.9892 \| 0.9901 \| 0.9885 \| nan \| 0.9732 \| 0.9697 \| 0.9767 \| 0.9782 \| \| 0.0108 \| 184.0 \| 9200 \| 0.0373 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9805 \| 0.9896 \| 0.9897 \| 0.9887 \| nan \| 0.9729 \| 0.9693 \| 0.9767 \| 0.9782 \| \| 0.0108 \| 185.0 \| 9250 \| 0.0371 \| 0.9743 \| 0.9872 \| 0.9869 \| nan \| 0.9806 \| 0.9895 \| 0.9900 \| 0.9885 \| nan \| 0.9730 \| 0.9694 \| 0.9767 \| 0.9783 \| \| 0.0108 \| 186.0 \| 9300 \| 0.0371 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9808 \| 0.9897 \| 0.9896 \| 0.9886 \| nan \| 0.9731 \| 0.9695 \| 0.9769 \| 0.9782 \| \| 0.0108 \| 187.0 \| 9350 \| 0.0371 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9808 \| 0.9895 \| 0.9899 \| 0.9886 \| nan \| 0.9731 \| 0.9695 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 188.0 \| 9400 \| 0.0370 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9808 \| 0.9893 \| 0.9900 \| 0.9886 \| nan \| 0.9730 \| 0.9695 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 189.0 \| 9450 \| 0.0371 \| 0.9743 \| 0.9872 \| 0.9869 \| nan \| 0.9807 \| 0.9895 \| 0.9901 \| 0.9883 \| nan \| 0.9730 \| 0.9694 \| 0.9767 \| 0.9782 \| \| 0.0108 \| 190.0 \| 9500 \| 0.0370 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9808 \| 0.9896 \| 0.9899 \| 0.9885 \| nan \| 0.9731 \| 0.9695 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 191.0 \| 9550 \| 0.0373 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9806 \| 0.9895 \| 0.9899 \| 0.9886 \| nan \| 0.9729 \| 0.9693 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 192.0 \| 9600 \| 0.0371 \| 0.9744 \| 0.9872 \| 0.9869 \| nan \| 0.9807 \| 0.9894 \| 0.9899 \| 0.9887 \| nan \| 0.9730 \| 0.9695 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 193.0 \| 9650 \| 0.0374 \| 0.9743 \| 0.9871 \| 0.9869 \| nan \| 0.9805 \| 0.9898 \| 0.9897 \| 0.9886 \| nan \| 0.9729 \| 0.9693 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 194.0 \| 9700 \| 0.0371 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9807 \| 0.9896 \| 0.9899 \| 0.9885 \| nan \| 0.9730 \| 0.9695 \| 0.9768 \| 0.9783 \| \| 0.0108 \| 195.0 \| 9750 \| 0.0370 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9809 \| 0.9896 \| 0.9898 \| 0.9885 \| nan \| 0.9731 \| 0.9696 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 196.0 \| 9800 \| 0.0370 \| 0.9745 \| 0.9872 \| 0.9870 \| nan \| 0.9810 \| 0.9894 \| 0.9898 \| 0.9887 \| nan \| 0.9732 \| 0.9697 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 197.0 \| 9850 \| 0.0371 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9809 \| 0.9896 \| 0.9897 \| 0.9886 \| nan \| 0.9731 \| 0.9695 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 198.0 \| 9900 \| 0.0371 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9809 \| 0.9896 \| 0.9897 \| 0.9886 \| nan \| 0.9731 \| 0.9695 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 199.0 \| 9950 \| 0.0371 \| 0.9744 \| 0.9872 \| 0.9870 \| nan \| 0.9809 \| 0.9895 \| 0.9898 \| 0.9886 \| nan \| 0.9731 \| 0.9696 \| 0.9768 \| 0.9782 \| \| 0.0108 \| 200.0 \| 10000 \| 0.0372 \| 0.9744 \| 0.9872 \| 0.9869 \| nan \| 0.9806 \| 0.9893 \| 0.9901 \| 0.9887 \| nan \| 0.9730 \| 0.9695 \| 0.9767 \| 0.9782 \| ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Sachleen/fintune_new	Sachleen	"2025-04-19T10:05:33Z"	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/orpheus-3b-0.1-pretrained", "base_model:adapter:unsloth/orpheus-3b-0.1-pretrained", "region:us" ]	null	"2025-04-19T10:01:11Z"	--- base_model: unsloth/orpheus-3b-0.1-pretrained library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
mlfoundations-dev/b1_code_top_16	mlfoundations-dev	"2025-04-19T10:05:00Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T03:15:19Z"	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b1_code_top_16 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # b1_code_top_16 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b1_code_top_16 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.0.2 - Tokenizers 0.20.3
hanzogak/comradeshipLU	hanzogak	"2025-04-19T09:51:27Z"	0	0	null	[ "anime", "merge", "text-to-image", "base_model:OnomaAIResearch/Illustrious-Lumina-v0.03", "base_model:finetune:OnomaAIResearch/Illustrious-Lumina-v0.03", "license:apache-2.0", "region:us" ]	text-to-image	"2025-04-19T09:36:59Z"	--- license: apache-2.0 base_model: - OnomaAIResearch/Illustrious-Lumina-v0.03 pipeline_tag: text-to-image tags: - anime - merge --- Comradeship LU ============= 전우애는 계속되어야 한다. ## comradeshipLU-v1T2 This is a merged anime model based on Illustrious-Lumina-v0.03. This is a test merged model. Illustrious-Lumina-v0.03 + ((LeX-Lumina - Lumina-Image-2.0) × 0.6) = Comradeship LU v1T2
linoyts/dog-hidream-lora	linoyts	"2025-04-19T09:50:12Z"	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "hidream", "hidream-diffusers", "template:sd-lora", "base_model:HiDream-ai/HiDream-I1-Full", "base_model:adapter:HiDream-ai/HiDream-I1-Full", "license:mit", "region:us" ]	text-to-image	"2025-04-19T06:42:42Z"	--- base_model: HiDream-ai/HiDream-I1-Full library_name: diffusers license: mit instance_prompt: a photo of sks dog widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - hidream - hidream-diffusers - template:sd-lora - text-to-image - diffusers-training - diffusers - lora - hidream - hidream-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # HiDream Image DreamBooth LoRA - linoyts/dog-hidream-lora <Gallery /> ## Model description These are linoyts/dog-hidream-lora DreamBooth LoRA weights for HiDream-ai/HiDream-I1-Full. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [HiDream Image diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_hidream.md). ## Trigger words You should use `a photo of sks dog` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](linoyts/dog-hidream-lora/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py >>> import torch >>> from transformers import PreTrainedTokenizerFast, LlamaForCausalLM >>> from diffusers import UniPCMultistepScheduler, HiDreamImagePipeline >>> scheduler = UniPCMultistepScheduler( ... flow_shift=3.0, prediction_type="flow_prediction", use_flow_sigmas=True ... ) >>> tokenizer_4 = PreTrainedTokenizerFast.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") >>> text_encoder_4 = LlamaForCausalLM.from_pretrained( ... "meta-llama/Meta-Llama-3.1-8B-Instruct", ... output_hidden_states=True, ... output_attentions=True, ... torch_dtype=torch.bfloat16, ... ) >>> pipe = HiDreamImagePipeline.from_pretrained( ... "HiDream-ai/HiDream-I1-Full", ... scheduler=scheduler, ... tokenizer_4=tokenizer_4, ... text_encoder_4=text_encoder_4, ... torch_dtype=torch.bfloat16, ... ) >>> pipe.enable_model_cpu_offload() >>> pipe.load_lora_weights(f"linoyts/dog-hidream-lora") >>> image = pipe(f"a photo of sks dog").images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
LlamaQwenDeepSeek/Qwen2.5-1.5B-scierc	LlamaQwenDeepSeek	"2025-04-19T09:49:23Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-04-19T09:47:40Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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TareksTesting/Alkahest-V6-LLaMa-70B	TareksTesting	"2025-04-19T09:47:32Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "base_model:TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B", "base_model:merge:TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B", "base_model:TareksLab/Malediction-V2-LLaMa-70B", "base_model:merge:TareksLab/Malediction-V2-LLaMa-70B", "base_model:TareksLab/Stylizer-V2b-LLaMa-70B", "base_model:merge:TareksLab/Stylizer-V2b-LLaMa-70B", "base_model:TareksLab/Wordsmith-V9-LLaMa-70B", "base_model:merge:TareksLab/Wordsmith-V9-LLaMa-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T09:12:45Z"	--- base_model: - TareksLab/Malediction-V2-LLaMa-70B - TareksLab/Wordsmith-V9-LLaMa-70B - TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B - TareksLab/Stylizer-V2b-LLaMa-70B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [TareksLab/Stylizer-V2b-LLaMa-70B](https://huggingface.co/TareksLab/Stylizer-V2b-LLaMa-70B) as a base. ### Models Merged The following models were included in the merge: * [TareksLab/Malediction-V2-LLaMa-70B](https://huggingface.co/TareksLab/Malediction-V2-LLaMa-70B) * [TareksLab/Wordsmith-V9-LLaMa-70B](https://huggingface.co/TareksLab/Wordsmith-V9-LLaMa-70B) * [TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B](https://huggingface.co/TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TareksLab/Wordsmith-V9-LLaMa-70B parameters: weight: 0.25 density: 0.5 - model: TareksLab/Malediction-V2-LLaMa-70B parameters: weight: 0.25 density: 0.5 - model: TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B parameters: weight: 0.25 density: 0.5 - model: TareksLab/Stylizer-V2b-LLaMa-70B parameters: weight: 0.25 density: 0.5 merge_method: dare_ties base_model: TareksLab/Stylizer-V2b-LLaMa-70B parameters: normalize: false out_dtype: bfloat16 chat_template: llama3 tokenizer: source: base ```
sunnnil/Smanhwa	sunnnil	"2025-04-19T09:46:29Z"	0	0	null	[ "license:other", "region:us" ]	null	"2025-04-18T10:20:39Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
dazzlinggopi/gopikPEFT_expo	dazzlinggopi	"2025-04-19T09:46:14Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "dataset:samsum", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2025-04-19T09:44:32Z"	--- library_name: transformers license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer datasets: - samsum model-index: - name: gopikPEFT_expo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gopikPEFT_expo This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 0.2669 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9187 \| 1.0 \| 19 \| 0.2777 \| \| 0.1883 \| 2.0 \| 38 \| 0.2669 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
mlfoundations-dev/b1_science_top_8_10k	mlfoundations-dev	"2025-04-19T09:41:16Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T04:45:18Z"	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b1_science_top_8_10k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # b1_science_top_8_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b1_science_top_8_10k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
raveas/klue-roberta-base-klue-sts	raveas	"2025-04-19T09:37:22Z"	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	"2025-04-19T09:37:05Z"	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 657 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Hartunka/tiny_bert_km_20_v2	Hartunka	"2025-04-19T09:28:12Z"	0	0	null	[ "safetensors", "distilbert", "generated_from_trainer", "dataset:Hartunka/processed_wikitext-103-raw-v1-km-20_v2", "model-index", "region:us" ]	null	"2025-04-13T16:24:27Z"	--- tags: - generated_from_trainer datasets: - Hartunka/processed_wikitext-103-raw-v1-km-20_v2 metrics: - accuracy model-index: - name: tiny_bert_km_20_v2 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: Hartunka/processed_wikitext-103-raw-v1-km-20_v2 type: Hartunka/processed_wikitext-103-raw-v1-km-20_v2 metrics: - name: Accuracy type: accuracy value: 0.15406566084647116 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tiny_bert_km_20_v2 This model is a fine-tuned version of [](https://huggingface.co/) on the Hartunka/processed_wikitext-103-raw-v1-km-20_v2 dataset. It achieves the following results on the evaluation set: - Loss: 6.5066 - Accuracy: 0.1541 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 96 - eval_batch_size: 96 - seed: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\| \| 6.6514 \| 4.1982 \| 10000 \| 6.7571 \| 0.1485 \| \| 6.326 \| 8.3963 \| 20000 \| 6.5497 \| 0.1526 \| \| 6.1671 \| 12.5945 \| 30000 \| 6.6530 \| 0.1544 \| \| 6.0706 \| 16.7926 \| 40000 \| 6.6305 \| 0.1514 \| \| 6.0123 \| 20.9908 \| 50000 \| 6.7075 \| 0.1511 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.19.1
sony-fashion-photography/riq	sony-fashion-photography	"2025-04-19T09:27:14Z"	5	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-04-07T11:10:21Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Liza --- # Riq <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Liza` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Liza", "lora_weights": "https://huggingface.co/sony-fashion-photography/riq/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('sony-fashion-photography/riq', weight_name='lora.safetensors') image = pipeline('Liza').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/sony-fashion-photography/riq/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF	mradermacher	"2025-04-19T09:26:22Z"	207	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.x_70b_SmarTracks_v1.30_flat", "base_model:quantized:Nexesenex/Llama_3.x_70b_SmarTracks_v1.30_flat", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-01T09:32:37Z"	--- base_model: Nexesenex/Llama_3.x_70b_SmarTracks_v1.30_flat language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Nexesenex/Llama_3.x_70b_SmarTracks_v1.30_flat <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
rbelanec/train_sst2_1744902628	rbelanec	"2025-04-19T09:23:18Z"	0	0	peft	[ "peft", "safetensors", "llama-factory", "lntuning", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]	null	"2025-04-18T23:52:50Z"	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - llama-factory - lntuning - generated_from_trainer model-index: - name: train_sst2_1744902628 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # train_sst2_1744902628 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0728 - Num Input Tokens Seen: 33458560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:-----------------:\| \| 0.1539 \| 0.0528 \| 200 \| 0.1842 \| 166688 \| \| 0.1944 \| 0.1056 \| 400 \| 0.1516 \| 334048 \| \| 0.0708 \| 0.1584 \| 600 \| 0.1392 \| 500448 \| \| 0.098 \| 0.2112 \| 800 \| 0.1304 \| 667872 \| \| 0.1267 \| 0.2640 \| 1000 \| 0.1269 \| 834848 \| \| 0.1179 \| 0.3167 \| 1200 \| 0.1228 \| 1002816 \| \| 0.0794 \| 0.3695 \| 1400 \| 0.1210 \| 1169088 \| \| 0.2142 \| 0.4223 \| 1600 \| 0.1180 \| 1337088 \| \| 0.1079 \| 0.4751 \| 1800 \| 0.1159 \| 1505536 \| \| 0.1278 \| 0.5279 \| 2000 \| 0.1137 \| 1673024 \| \| 0.1395 \| 0.5807 \| 2200 \| 0.1122 \| 1842304 \| \| 0.1163 \| 0.6335 \| 2400 \| 0.1105 \| 2007328 \| \| 0.0823 \| 0.6863 \| 2600 \| 0.1093 \| 2174880 \| \| 0.1669 \| 0.7391 \| 2800 \| 0.1076 \| 2341280 \| \| 0.1197 \| 0.7919 \| 3000 \| 0.1067 \| 2509440 \| \| 0.1089 \| 0.8447 \| 3200 \| 0.1046 \| 2674784 \| \| 0.088 \| 0.8975 \| 3400 \| 0.1030 \| 2843680 \| \| 0.1537 \| 0.9502 \| 3600 \| 0.1062 \| 3011904 \| \| 0.0942 \| 1.0029 \| 3800 \| 0.1014 \| 3178064 \| \| 0.1005 \| 1.0557 \| 4000 \| 0.1001 \| 3345904 \| \| 0.0968 \| 1.1085 \| 4200 \| 0.0992 \| 3514608 \| \| 0.0908 \| 1.1613 \| 4400 \| 0.0987 \| 3680560 \| \| 0.0681 \| 1.2141 \| 4600 \| 0.0979 \| 3849328 \| \| 0.1266 \| 1.2669 \| 4800 \| 0.0978 \| 4017200 \| \| 0.08 \| 1.3197 \| 5000 \| 0.0961 \| 4187184 \| \| 0.0749 \| 1.3724 \| 5200 \| 0.0955 \| 4354416 \| \| 0.1066 \| 1.4252 \| 5400 \| 0.0947 \| 4519856 \| \| 0.1032 \| 1.4780 \| 5600 \| 0.0941 \| 4687280 \| \| 0.0682 \| 1.5308 \| 5800 \| 0.0934 \| 4856112 \| \| 0.05 \| 1.5836 \| 6000 \| 0.0938 \| 5022736 \| \| 0.1597 \| 1.6364 \| 6200 \| 0.0919 \| 5188656 \| \| 0.0547 \| 1.6892 \| 6400 \| 0.0913 \| 5356208 \| \| 0.1362 \| 1.7420 \| 6600 \| 0.0909 \| 5523952 \| \| 0.1347 \| 1.7948 \| 6800 \| 0.0901 \| 5690672 \| \| 0.0949 \| 1.8476 \| 7000 \| 0.0896 \| 5857072 \| \| 0.0551 \| 1.9004 \| 7200 \| 0.0889 \| 6024976 \| \| 0.0835 \| 1.9531 \| 7400 \| 0.0890 \| 6191664 \| \| 0.0984 \| 2.0058 \| 7600 \| 0.0882 \| 6357472 \| \| 0.1026 \| 2.0586 \| 7800 \| 0.0882 \| 6525984 \| \| 0.0955 \| 2.1114 \| 8000 \| 0.0871 \| 6692320 \| \| 0.0731 \| 2.1642 \| 8200 \| 0.0870 \| 6860064 \| \| 0.1487 \| 2.2170 \| 8400 \| 0.0870 \| 7026528 \| \| 0.0494 \| 2.2698 \| 8600 \| 0.0868 \| 7192384 \| \| 0.1057 \| 2.3226 \| 8800 \| 0.0859 \| 7358816 \| \| 0.0801 \| 2.3753 \| 9000 \| 0.0857 \| 7526496 \| \| 0.0757 \| 2.4281 \| 9200 \| 0.0852 \| 7696064 \| \| 0.0889 \| 2.4809 \| 9400 \| 0.0851 \| 7863456 \| \| 0.1005 \| 2.5337 \| 9600 \| 0.0845 \| 8031776 \| \| 0.0785 \| 2.5865 \| 9800 \| 0.0843 \| 8199584 \| \| 0.0925 \| 2.6393 \| 10000 \| 0.0836 \| 8366016 \| \| 0.0698 \| 2.6921 \| 10200 \| 0.0839 \| 8531808 \| \| 0.0657 \| 2.7449 \| 10400 \| 0.0830 \| 8702976 \| \| 0.0471 \| 2.7977 \| 10600 \| 0.0829 \| 8870944 \| \| 0.0946 \| 2.8505 \| 10800 \| 0.0830 \| 9039680 \| \| 0.1393 \| 2.9033 \| 11000 \| 0.0829 \| 9206880 \| \| 0.0655 \| 2.9561 \| 11200 \| 0.0823 \| 9372128 \| \| 0.0833 \| 3.0087 \| 11400 \| 0.0818 \| 9538768 \| \| 0.0782 \| 3.0615 \| 11600 \| 0.0815 \| 9705232 \| \| 0.0579 \| 3.1143 \| 11800 \| 0.0814 \| 9871632 \| \| 0.0707 \| 3.1671 \| 12000 \| 0.0814 \| 10039472 \| \| 0.0276 \| 3.2199 \| 12200 \| 0.0809 \| 10206320 \| \| 0.0665 \| 3.2727 \| 12400 \| 0.0807 \| 10376240 \| \| 0.0816 \| 3.3255 \| 12600 \| 0.0807 \| 10544464 \| \| 0.0344 \| 3.3782 \| 12800 \| 0.0801 \| 10712240 \| \| 0.044 \| 3.4310 \| 13000 \| 0.0801 \| 10879120 \| \| 0.0479 \| 3.4838 \| 13200 \| 0.0800 \| 11045072 \| \| 0.0631 \| 3.5366 \| 13400 \| 0.0800 \| 11211312 \| \| 0.0876 \| 3.5894 \| 13600 \| 0.0795 \| 11378128 \| \| 0.0434 \| 3.6422 \| 13800 \| 0.0800 \| 11544592 \| \| 0.1373 \| 3.6950 \| 14000 \| 0.0790 \| 11713040 \| \| 0.1293 \| 3.7478 \| 14200 \| 0.0794 \| 11880432 \| \| 0.115 \| 3.8006 \| 14400 \| 0.0788 \| 12048176 \| \| 0.0573 \| 3.8534 \| 14600 \| 0.0790 \| 12215792 \| \| 0.0487 \| 3.9062 \| 14800 \| 0.0789 \| 12383792 \| \| 0.0535 \| 3.9590 \| 15000 \| 0.0786 \| 12549680 \| \| 0.0956 \| 4.0116 \| 15200 \| 0.0782 \| 12716448 \| \| 0.0484 \| 4.0644 \| 15400 \| 0.0781 \| 12882752 \| \| 0.0618 \| 4.1172 \| 15600 \| 0.0780 \| 13051200 \| \| 0.0581 \| 4.1700 \| 15800 \| 0.0777 \| 13217024 \| \| 0.0639 \| 4.2228 \| 16000 \| 0.0776 \| 13382784 \| \| 0.0619 \| 4.2756 \| 16200 \| 0.0781 \| 13549216 \| \| 0.0358 \| 4.3284 \| 16400 \| 0.0772 \| 13719072 \| \| 0.1304 \| 4.3812 \| 16600 \| 0.0771 \| 13884928 \| \| 0.0876 \| 4.4339 \| 16800 \| 0.0768 \| 14051584 \| \| 0.0492 \| 4.4867 \| 17000 \| 0.0781 \| 14220704 \| \| 0.045 \| 4.5395 \| 17200 \| 0.0766 \| 14387008 \| \| 0.1129 \| 4.5923 \| 17400 \| 0.0768 \| 14555808 \| \| 0.0602 \| 4.6451 \| 17600 \| 0.0766 \| 14723456 \| \| 0.1302 \| 4.6979 \| 17800 \| 0.0764 \| 14890880 \| \| 0.0634 \| 4.7507 \| 18000 \| 0.0769 \| 15059744 \| \| 0.0675 \| 4.8035 \| 18200 \| 0.0763 \| 15224512 \| \| 0.12 \| 4.8563 \| 18400 \| 0.0761 \| 15392960 \| \| 0.0468 \| 4.9091 \| 18600 \| 0.0759 \| 15561696 \| \| 0.0968 \| 4.9619 \| 18800 \| 0.0763 \| 15728800 \| \| 0.0793 \| 5.0145 \| 19000 \| 0.0758 \| 15897552 \| \| 0.0726 \| 5.0673 \| 19200 \| 0.0757 \| 16064688 \| \| 0.0647 \| 5.1201 \| 19400 \| 0.0754 \| 16231120 \| \| 0.0802 \| 5.1729 \| 19600 \| 0.0755 \| 16397744 \| \| 0.1297 \| 5.2257 \| 19800 \| 0.0753 \| 16564176 \| \| 0.0624 \| 5.2785 \| 20000 \| 0.0752 \| 16731600 \| \| 0.0329 \| 5.3313 \| 20200 \| 0.0756 \| 16898064 \| \| 0.0914 \| 5.3841 \| 20400 \| 0.0753 \| 17064080 \| \| 0.0583 \| 5.4368 \| 20600 \| 0.0751 \| 17231888 \| \| 0.0622 \| 5.4896 \| 20800 \| 0.0750 \| 17399184 \| \| 0.0676 \| 5.5424 \| 21000 \| 0.0758 \| 17566160 \| \| 0.0667 \| 5.5952 \| 21200 \| 0.0748 \| 17732304 \| \| 0.0507 \| 5.6480 \| 21400 \| 0.0750 \| 17900880 \| \| 0.0453 \| 5.7008 \| 21600 \| 0.0747 \| 18070192 \| \| 0.0833 \| 5.7536 \| 21800 \| 0.0748 \| 18237168 \| \| 0.0535 \| 5.8064 \| 22000 \| 0.0748 \| 18403856 \| \| 0.1257 \| 5.8592 \| 22200 \| 0.0745 \| 18571248 \| \| 0.0289 \| 5.9120 \| 22400 \| 0.0747 \| 18738672 \| \| 0.0504 \| 5.9648 \| 22600 \| 0.0747 \| 18905744 \| \| 0.0855 \| 6.0174 \| 22800 \| 0.0744 \| 19073440 \| \| 0.0699 \| 6.0702 \| 23000 \| 0.0744 \| 19241920 \| \| 0.1241 \| 6.1230 \| 23200 \| 0.0745 \| 19409408 \| \| 0.077 \| 6.1758 \| 23400 \| 0.0751 \| 19577024 \| \| 0.0498 \| 6.2286 \| 23600 \| 0.0741 \| 19744608 \| \| 0.0814 \| 6.2814 \| 23800 \| 0.0742 \| 19911488 \| \| 0.0741 \| 6.3342 \| 24000 \| 0.0741 \| 20078944 \| \| 0.0561 \| 6.3870 \| 24200 \| 0.0740 \| 20244928 \| \| 0.0998 \| 6.4398 \| 24400 \| 0.0741 \| 20411232 \| \| 0.0599 \| 6.4925 \| 24600 \| 0.0740 \| 20578080 \| \| 0.0745 \| 6.5453 \| 24800 \| 0.0737 \| 20746592 \| \| 0.1089 \| 6.5981 \| 25000 \| 0.0741 \| 20913344 \| \| 0.0357 \| 6.6509 \| 25200 \| 0.0738 \| 21081952 \| \| 0.0983 \| 6.7037 \| 25400 \| 0.0739 \| 21248384 \| \| 0.0928 \| 6.7565 \| 25600 \| 0.0738 \| 21415872 \| \| 0.0561 \| 6.8093 \| 25800 \| 0.0740 \| 21584000 \| \| 0.1221 \| 6.8621 \| 26000 \| 0.0736 \| 21751168 \| \| 0.0501 \| 6.9149 \| 26200 \| 0.0737 \| 21918816 \| \| 0.0735 \| 6.9677 \| 26400 \| 0.0735 \| 22084384 \| \| 0.073 \| 7.0203 \| 26600 \| 0.0737 \| 22251776 \| \| 0.0831 \| 7.0731 \| 26800 \| 0.0736 \| 22418080 \| \| 0.0576 \| 7.1259 \| 27000 \| 0.0735 \| 22587392 \| \| 0.0622 \| 7.1787 \| 27200 \| 0.0736 \| 22753056 \| \| 0.0731 \| 7.2315 \| 27400 \| 0.0738 \| 22920768 \| \| 0.0822 \| 7.2843 \| 27600 \| 0.0734 \| 23087296 \| \| 0.0392 \| 7.3371 \| 27800 \| 0.0735 \| 23254400 \| \| 0.0331 \| 7.3899 \| 28000 \| 0.0733 \| 23422752 \| \| 0.0444 \| 7.4427 \| 28200 \| 0.0734 \| 23588352 \| \| 0.0614 \| 7.4954 \| 28400 \| 0.0737 \| 23755840 \| \| 0.0677 \| 7.5482 \| 28600 \| 0.0734 \| 23923680 \| \| 0.0489 \| 7.6010 \| 28800 \| 0.0734 \| 24091168 \| \| 0.0393 \| 7.6538 \| 29000 \| 0.0735 \| 24258016 \| \| 0.0912 \| 7.7066 \| 29200 \| 0.0733 \| 24427808 \| \| 0.0217 \| 7.7594 \| 29400 \| 0.0734 \| 24596288 \| \| 0.0513 \| 7.8122 \| 29600 \| 0.0730 \| 24764192 \| \| 0.0658 \| 7.8650 \| 29800 \| 0.0732 \| 24932000 \| \| 0.0394 \| 7.9178 \| 30000 \| 0.0731 \| 25100224 \| \| 0.0558 \| 7.9706 \| 30200 \| 0.0735 \| 25267808 \| \| 0.0496 \| 8.0232 \| 30400 \| 0.0733 \| 25433440 \| \| 0.0734 \| 8.0760 \| 30600 \| 0.0733 \| 25600672 \| \| 0.0612 \| 8.1288 \| 30800 \| 0.0731 \| 25769408 \| \| 0.0413 \| 8.1816 \| 31000 \| 0.0728 \| 25936160 \| \| 0.0626 \| 8.2344 \| 31200 \| 0.0731 \| 26103744 \| \| 0.0785 \| 8.2872 \| 31400 \| 0.0731 \| 26270560 \| \| 0.0716 \| 8.3400 \| 31600 \| 0.0732 \| 26437536 \| \| 0.0325 \| 8.3928 \| 31800 \| 0.0732 \| 26604480 \| \| 0.0484 \| 8.4456 \| 32000 \| 0.0732 \| 26771680 \| \| 0.1124 \| 8.4984 \| 32200 \| 0.0730 \| 26940256 \| \| 0.0738 \| 8.5511 \| 32400 \| 0.0730 \| 27107680 \| \| 0.0793 \| 8.6039 \| 32600 \| 0.0731 \| 27274048 \| \| 0.1138 \| 8.6567 \| 32800 \| 0.0731 \| 27440544 \| \| 0.1 \| 8.7095 \| 33000 \| 0.0731 \| 27608000 \| \| 0.03 \| 8.7623 \| 33200 \| 0.0732 \| 27776704 \| \| 0.0178 \| 8.8151 \| 33400 \| 0.0728 \| 27942752 \| \| 0.0735 \| 8.8679 \| 33600 \| 0.0729 \| 28108864 \| \| 0.032 \| 8.9207 \| 33800 \| 0.0728 \| 28275296 \| \| 0.0549 \| 8.9735 \| 34000 \| 0.0730 \| 28443520 \| \| 0.0322 \| 9.0261 \| 34200 \| 0.0730 \| 28609776 \| \| 0.0633 \| 9.0789 \| 34400 \| 0.0729 \| 28777712 \| \| 0.1346 \| 9.1317 \| 34600 \| 0.0729 \| 28944144 \| \| 0.0417 \| 9.1845 \| 34800 \| 0.0732 \| 29111152 \| \| 0.0991 \| 9.2373 \| 35000 \| 0.0731 \| 29278000 \| \| 0.043 \| 9.2901 \| 35200 \| 0.0729 \| 29443792 \| \| 0.0353 \| 9.3429 \| 35400 \| 0.0729 \| 29609072 \| \| 0.1 \| 9.3957 \| 35600 \| 0.0730 \| 29776592 \| \| 0.0774 \| 9.4485 \| 35800 \| 0.0730 \| 29941616 \| \| 0.0649 \| 9.5013 \| 36000 \| 0.0729 \| 30110160 \| \| 0.0702 \| 9.5540 \| 36200 \| 0.0730 \| 30277744 \| \| 0.1259 \| 9.6068 \| 36400 \| 0.0729 \| 30447152 \| \| 0.0281 \| 9.6596 \| 36600 \| 0.0729 \| 30612976 \| \| 0.0457 \| 9.7124 \| 36800 \| 0.0730 \| 30780240 \| \| 0.0235 \| 9.7652 \| 37000 \| 0.0728 \| 30948048 \| \| 0.0478 \| 9.8180 \| 37200 \| 0.0730 \| 31116368 \| \| 0.0338 \| 9.8708 \| 37400 \| 0.0729 \| 31283888 \| \| 0.0387 \| 9.9236 \| 37600 \| 0.0730 \| 31452560 \| \| 0.0603 \| 9.9764 \| 37800 \| 0.0728 \| 31620720 \| \| 0.1036 \| 10.0290 \| 38000 \| 0.0730 \| 31786016 \| \| 0.0954 \| 10.0818 \| 38200 \| 0.0728 \| 31952768 \| \| 0.0774 \| 10.1346 \| 38400 \| 0.0728 \| 32120320 \| \| 0.0159 \| 10.1874 \| 38600 \| 0.0728 \| 32287584 \| \| 0.0459 \| 10.2402 \| 38800 \| 0.0730 \| 32455072 \| \| 0.0525 \| 10.2930 \| 39000 \| 0.0729 \| 32621184 \| \| 0.0389 \| 10.3458 \| 39200 \| 0.0730 \| 32788960 \| \| 0.0456 \| 10.3986 \| 39400 \| 0.0729 \| 32955776 \| \| 0.0359 \| 10.4514 \| 39600 \| 0.0728 \| 33122816 \| \| 0.0964 \| 10.5042 \| 39800 \| 0.0729 \| 33291072 \| \| 0.0656 \| 10.5569 \| 40000 \| 0.0728 \| 33458560 \| ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
MercuraTech/v4_articles_single_base	MercuraTech	"2025-04-19T09:19:44Z"	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-04-19T04:28:45Z"	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: v4_articles_single_base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # v4_articles_single_base This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7167 - Accuracy: 0.4397 - F1: 0.4350 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 9.9789 \| 0.1895 \| 500 \| 9.9718 \| 0.0001 \| 0.0000 \| \| 9.8369 \| 0.3791 \| 1000 \| 9.7965 \| 0.0094 \| 0.0002 \| \| 9.5592 \| 0.5686 \| 1500 \| 9.5071 \| 0.0094 \| 0.0002 \| \| 9.237 \| 0.7582 \| 2000 \| 9.1586 \| 0.0094 \| 0.0002 \| \| 8.8972 \| 0.9477 \| 2500 \| 8.8566 \| 0.0094 \| 0.0002 \| \| 8.6564 \| 1.1372 \| 3000 \| 8.6478 \| 0.0160 \| 0.0019 \| \| 8.4797 \| 1.3268 \| 3500 \| 8.4819 \| 0.0246 \| 0.0046 \| \| 8.3484 \| 1.5163 \| 4000 \| 8.3282 \| 0.0317 \| 0.0064 \| \| 8.2146 \| 1.7058 \| 4500 \| 8.1572 \| 0.0340 \| 0.0068 \| \| 8.0264 \| 1.8954 \| 5000 \| 7.9514 \| 0.0455 \| 0.0112 \| \| 7.7606 \| 2.0849 \| 5500 \| 7.7213 \| 0.0583 \| 0.0177 \| \| 7.4704 \| 2.2745 \| 6000 \| 7.4514 \| 0.0838 \| 0.0287 \| \| 7.2206 \| 2.4640 \| 6500 \| 7.1248 \| 0.1120 \| 0.0452 \| \| 6.8785 \| 2.6535 \| 7000 \| 6.7901 \| 0.1367 \| 0.0610 \| \| 6.5434 \| 2.8431 \| 7500 \| 6.4326 \| 0.1625 \| 0.0772 \| \| 6.1765 \| 3.0326 \| 8000 \| 6.0874 \| 0.1800 \| 0.0911 \| \| 5.8391 \| 3.2221 \| 8500 \| 5.7638 \| 0.2015 \| 0.1086 \| \| 5.4916 \| 3.4117 \| 9000 \| 5.4800 \| 0.2161 \| 0.1224 \| \| 5.3123 \| 3.6012 \| 9500 \| 5.2343 \| 0.2268 \| 0.1327 \| \| 5.0068 \| 3.7908 \| 10000 \| 5.0158 \| 0.2416 \| 0.1460 \| \| 4.8917 \| 3.9803 \| 10500 \| 4.8260 \| 0.2518 \| 0.1572 \| \| 4.5999 \| 4.1698 \| 11000 \| 4.6644 \| 0.2668 \| 0.1714 \| \| 4.5399 \| 4.3594 \| 11500 \| 4.5127 \| 0.2752 \| 0.1824 \| \| 4.2681 \| 4.5489 \| 12000 \| 4.3847 \| 0.2841 \| 0.1909 \| \| 4.2411 \| 4.7384 \| 12500 \| 4.2655 \| 0.2917 \| 0.1999 \| \| 4.0436 \| 4.9280 \| 13000 \| 4.1556 \| 0.2996 \| 0.2096 \| \| 3.8549 \| 5.1175 \| 13500 \| 4.0580 \| 0.3090 \| 0.2198 \| \| 3.8365 \| 5.3071 \| 14000 \| 3.9771 \| 0.3157 \| 0.2266 \| \| 3.7002 \| 5.4966 \| 14500 \| 3.8831 \| 0.3225 \| 0.2368 \| \| 3.6145 \| 5.6861 \| 15000 \| 3.8118 \| 0.3313 \| 0.2461 \| \| 3.5779 \| 5.8757 \| 15500 \| 3.7317 \| 0.3384 \| 0.2570 \| \| 3.4283 \| 6.0652 \| 16000 \| 3.6797 \| 0.3390 \| 0.2603 \| \| 3.3538 \| 6.2547 \| 16500 \| 3.6148 \| 0.3463 \| 0.2692 \| \| 3.3319 \| 6.4443 \| 17000 \| 3.5629 \| 0.3511 \| 0.2749 \| \| 3.226 \| 6.6338 \| 17500 \| 3.5067 \| 0.3564 \| 0.2814 \| \| 3.2061 \| 6.8234 \| 18000 \| 3.4567 \| 0.3604 \| 0.2869 \| \| 3.1053 \| 7.0129 \| 18500 \| 3.4042 \| 0.3675 \| 0.2957 \| \| 3.0195 \| 7.2024 \| 19000 \| 3.3702 \| 0.3704 \| 0.3014 \| \| 2.9741 \| 7.3920 \| 19500 \| 3.3274 \| 0.3755 \| 0.3074 \| \| 2.9456 \| 7.5815 \| 20000 \| 3.2985 \| 0.3761 \| 0.3086 \| \| 2.9216 \| 7.7710 \| 20500 \| 3.2658 \| 0.3772 \| 0.3119 \| \| 2.8645 \| 7.9606 \| 21000 \| 3.2231 \| 0.3847 \| 0.3226 \| \| 2.7615 \| 8.1501 \| 21500 \| 3.2023 \| 0.3899 \| 0.3265 \| \| 2.7581 \| 8.3397 \| 22000 \| 3.1769 \| 0.3878 \| 0.3273 \| \| 2.7612 \| 8.5292 \| 22500 \| 3.1357 \| 0.3936 \| 0.3370 \| \| 2.656 \| 8.7187 \| 23000 \| 3.1208 \| 0.3893 \| 0.3372 \| \| 2.6204 \| 8.9083 \| 23500 \| 3.0876 \| 0.3973 \| 0.3440 \| \| 2.5629 \| 9.0978 \| 24000 \| 3.0708 \| 0.3957 \| 0.3435 \| \| 2.5407 \| 9.2873 \| 24500 \| 3.0475 \| 0.4014 \| 0.3564 \| \| 2.501 \| 9.4769 \| 25000 \| 3.0425 \| 0.4007 \| 0.3512 \| \| 2.4615 \| 9.6664 \| 25500 \| 3.0077 \| 0.4064 \| 0.3592 \| \| 2.4667 \| 9.8560 \| 26000 \| 2.9950 \| 0.4061 \| 0.3634 \| \| 2.3594 \| 10.0455 \| 26500 \| 2.9875 \| 0.4048 \| 0.3661 \| \| 2.3613 \| 10.2350 \| 27000 \| 2.9587 \| 0.4056 \| 0.3727 \| \| 2.3253 \| 10.4246 \| 27500 \| 2.9467 \| 0.4110 \| 0.3751 \| \| 2.332 \| 10.6141 \| 28000 \| 2.9342 \| 0.4114 \| 0.3734 \| \| 2.2866 \| 10.8036 \| 28500 \| 2.9034 \| 0.4138 \| 0.3830 \| \| 2.2932 \| 10.9932 \| 29000 \| 2.8993 \| 0.4117 \| 0.3822 \| \| 2.2165 \| 11.1827 \| 29500 \| 2.8904 \| 0.4199 \| 0.3865 \| \| 2.1911 \| 11.3723 \| 30000 \| 2.8893 \| 0.4145 \| 0.3858 \| \| 2.1368 \| 11.5618 \| 30500 \| 2.8658 \| 0.4212 \| 0.3951 \| \| 2.147 \| 11.7513 \| 31000 \| 2.8640 \| 0.4144 \| 0.3911 \| \| 2.0725 \| 11.9409 \| 31500 \| 2.8407 \| 0.4203 \| 0.3978 \| \| 2.071 \| 12.1304 \| 32000 \| 2.8350 \| 0.4237 \| 0.4005 \| \| 2.0455 \| 12.3199 \| 32500 \| 2.8318 \| 0.4233 \| 0.3999 \| \| 2.02 \| 12.5095 \| 33000 \| 2.8176 \| 0.4256 \| 0.4033 \| \| 2.0375 \| 12.6990 \| 33500 \| 2.8144 \| 0.4264 \| 0.4063 \| \| 1.9853 \| 12.8886 \| 34000 \| 2.7982 \| 0.4290 \| 0.4075 \| \| 1.9396 \| 13.0781 \| 34500 \| 2.7921 \| 0.4271 \| 0.4120 \| \| 1.9214 \| 13.2676 \| 35000 \| 2.7846 \| 0.4261 \| 0.4100 \| \| 1.9103 \| 13.4572 \| 35500 \| 2.7845 \| 0.4246 \| 0.4099 \| \| 1.9422 \| 13.6467 \| 36000 \| 2.7822 \| 0.4285 \| 0.4112 \| \| 1.9098 \| 13.8362 \| 36500 \| 2.7708 \| 0.4290 \| 0.4130 \| \| 1.8087 \| 14.0258 \| 37000 \| 2.7687 \| 0.4320 \| 0.4177 \| \| 1.7799 \| 14.2153 \| 37500 \| 2.7529 \| 0.4326 \| 0.4176 \| \| 1.7517 \| 14.4049 \| 38000 \| 2.7543 \| 0.4345 \| 0.4218 \| \| 1.8091 \| 14.5944 \| 38500 \| 2.7533 \| 0.4347 \| 0.4215 \| \| 1.8129 \| 14.7839 \| 39000 \| 2.7444 \| 0.4330 \| 0.4230 \| \| 1.777 \| 14.9735 \| 39500 \| 2.7382 \| 0.4370 \| 0.4284 \| \| 1.6449 \| 15.1630 \| 40000 \| 2.7459 \| 0.4344 \| 0.4240 \| \| 1.7006 \| 15.3525 \| 40500 \| 2.7225 \| 0.4375 \| 0.4312 \| \| 1.7103 \| 15.5421 \| 41000 \| 2.7314 \| 0.4402 \| 0.4308 \| \| 1.7152 \| 15.7316 \| 41500 \| 2.7247 \| 0.4401 \| 0.4331 \| \| 1.7274 \| 15.9212 \| 42000 \| 2.7218 \| 0.4388 \| 0.4310 \| \| 1.6366 \| 16.1107 \| 42500 \| 2.7167 \| 0.4397 \| 0.4350 \| \| 1.6787 \| 16.3002 \| 43000 \| 2.6995 \| 0.4425 \| 0.4375 \| \| 1.5951 \| 16.4898 \| 43500 \| 2.7195 \| 0.4390 \| 0.4313 \| \| 1.6202 \| 16.6793 \| 44000 \| 2.7076 \| 0.4406 \| 0.4358 \| \| 1.6674 \| 16.8688 \| 44500 \| 2.7015 \| 0.4414 \| 0.4360 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
summerstars/Solara	summerstars	"2025-04-19T09:17:30Z"	2	0	transformers.js	[ "transformers.js", "safetensors", "llama", "text-generation", "onnx", "conversational", "en", "base_model:HuggingFaceTB/SmolLM2-360M-Instruct", "base_model:finetune:HuggingFaceTB/SmolLM2-360M-Instruct", "license:apache-2.0", "region:us" ]	text-generation	"2025-04-19T05:17:38Z"	--- license: apache-2.0 base_model: - HuggingFaceTB/SmolLM2-360M-Instruct language: - en pipeline_tag: text-generation tags: - safetensors - onnx - transformers.js --- # 🌞 Solara — summerstars/Solara ## Created by a High School Student \| Built on Google Colab (T4 GPU) ## 高校生によって開発 \| Google Colab（T4 GPU）で作成 Solara is a lightweight, instruction-tuned language model based on [`HuggingFaceTB/SmolLM2-360M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct). It was developed by a high school student using Google Colab with a T4 GPU. Despite its compact size, Solara delivers quick responses and handles everyday tasks efficiently. Solara（ソララ）は、[`HuggingFaceTB/SmolLM2-360M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) をベースとした軽量な指示応答型言語モデルです。 Google Colab（T4 GPU）を使用して高校生が開発しました。小型ながら、日常のタスクや会話を効率的かつ高速に処理します。 --- ## 📌 Model Details / モデル詳細 - Base Model / ベースモデル: HuggingFaceTB/SmolLM2-360M-Instruct - Parameters / パラメータ数: 360M - Architecture / アーキテクチャ: Decoder-only Transformer / デコーダ専用トランスフォーマー - Languages / 対応言語: English / 英語 - License / ライセンス: Apache 2.0 --- ## 🚀 Use Cases / 主な用途 - Lightweight chatbots / 軽量チャットボット - Inference on CPUs or mobile devices / CPU・モバイル端末での推論 - Educational or hobbyist projects / 教育・趣味用途 - Instruction-following tasks / 指示応答タスク --- ## 🛠️ How to Use / 使用方法 ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "summerstars/Solara" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) prompt = "Please explain black holes in simple terms." inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=128) # Print the result / 結果を表示 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
shibajustfor/802ae234-5e4c-4ba5-8809-0a981f51f7ba	shibajustfor	"2025-04-19T09:11:32Z"	0	0	transformers	[ "transformers", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	"2025-04-19T09:11:18Z"	--- library_name: transformers model_name: shibajustfor/802ae234-5e4c-4ba5-8809-0a981f51f7ba tags: - generated_from_trainer licence: license --- # Model Card for shibajustfor/802ae234-5e4c-4ba5-8809-0a981f51f7ba This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ### Framework versions - TRL: 0.12.0 - Transformers: 4.46.3 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ahmet71cakir/phi4-turbochat-full	ahmet71cakir	"2025-04-19T09:11:28Z"	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-18T18:14:56Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF	mradermacher	"2025-04-19T09:10:26Z"	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:NexesMess/Llama_3.x_70b_Triads_V1", "base_model:quantized:NexesMess/Llama_3.x_70b_Triads_V1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2025-04-18T08:45:09Z"	--- base_model: NexesMess/Llama_3.x_70b_Triads_V1 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/NexesMess/Llama_3.x_70b_Triads_V1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 24.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 34.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 37.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 40.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q4_1.gguf) \| i1-Q4_1 \| 44.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 58.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
umar141/llama3.2_Baro_v2	umar141	"2025-04-19T09:07:17Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-04-19T09:07:06Z"	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: umar141 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
rbelanec/train_qnli_1744902616	rbelanec	"2025-04-19T09:05:59Z"	3	0	peft	[ "peft", "safetensors", "llama-factory", "lntuning", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]	null	"2025-04-18T18:05:23Z"	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - llama-factory - lntuning - generated_from_trainer model-index: - name: train_qnli_1744902616 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # train_qnli_1744902616 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the qnli dataset. It achieves the following results on the evaluation set: - Loss: 0.0513 - Num Input Tokens Seen: 74724160 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:-----------------:\| \| 0.1529 \| 0.0339 \| 200 \| 0.1279 \| 375872 \| \| 0.1128 \| 0.0679 \| 400 \| 0.1065 \| 754656 \| \| 0.0909 \| 0.1018 \| 600 \| 0.1009 \| 1127296 \| \| 0.1101 \| 0.1358 \| 800 \| 0.0961 \| 1500832 \| \| 0.0937 \| 0.1697 \| 1000 \| 0.0936 \| 1870752 \| \| 0.0823 \| 0.2037 \| 1200 \| 0.0897 \| 2248448 \| \| 0.1123 \| 0.2376 \| 1400 \| 0.0871 \| 2622784 \| \| 0.0786 \| 0.2716 \| 1600 \| 0.0847 \| 2995616 \| \| 0.0674 \| 0.3055 \| 1800 \| 0.0829 \| 3370144 \| \| 0.0745 \| 0.3395 \| 2000 \| 0.0811 \| 3747936 \| \| 0.074 \| 0.3734 \| 2200 \| 0.0795 \| 4126560 \| \| 0.0795 \| 0.4073 \| 2400 \| 0.0788 \| 4497920 \| \| 0.068 \| 0.4413 \| 2600 \| 0.0770 \| 4870432 \| \| 0.0948 \| 0.4752 \| 2800 \| 0.0759 \| 5242976 \| \| 0.0751 \| 0.5092 \| 3000 \| 0.0748 \| 5615808 \| \| 0.0722 \| 0.5431 \| 3200 \| 0.0738 \| 5984672 \| \| 0.0915 \| 0.5771 \| 3400 \| 0.0733 \| 6356832 \| \| 0.0585 \| 0.6110 \| 3600 \| 0.0729 \| 6732928 \| \| 0.0577 \| 0.6450 \| 3800 \| 0.0723 \| 7111456 \| \| 0.0511 \| 0.6789 \| 4000 \| 0.0714 \| 7481824 \| \| 0.0664 \| 0.7129 \| 4200 \| 0.0702 \| 7857440 \| \| 0.0703 \| 0.7468 \| 4400 \| 0.0699 \| 8229632 \| \| 0.0676 \| 0.7808 \| 4600 \| 0.0689 \| 8601824 \| \| 0.0665 \| 0.8147 \| 4800 \| 0.0683 \| 8974688 \| \| 0.0851 \| 0.8486 \| 5000 \| 0.0682 \| 9345088 \| \| 0.0874 \| 0.8826 \| 5200 \| 0.0673 \| 9720928 \| \| 0.063 \| 0.9165 \| 5400 \| 0.0667 \| 10090976 \| \| 0.0652 \| 0.9505 \| 5600 \| 0.0681 \| 10461824 \| \| 0.0807 \| 0.9844 \| 5800 \| 0.0660 \| 10837568 \| \| 0.0644 \| 1.0183 \| 6000 \| 0.0662 \| 11211008 \| \| 0.0679 \| 1.0523 \| 6200 \| 0.0649 \| 11582528 \| \| 0.0594 \| 1.0862 \| 6400 \| 0.0647 \| 11958208 \| \| 0.0808 \| 1.1202 \| 6600 \| 0.0645 \| 12334752 \| \| 0.0535 \| 1.1541 \| 6800 \| 0.0650 \| 12710176 \| \| 0.0547 \| 1.1881 \| 7000 \| 0.0650 \| 13083200 \| \| 0.0692 \| 1.2220 \| 7200 \| 0.0632 \| 13458944 \| \| 0.0571 \| 1.2560 \| 7400 \| 0.0629 \| 13836256 \| \| 0.0874 \| 1.2899 \| 7600 \| 0.0630 \| 14209248 \| \| 0.0752 \| 1.3238 \| 7800 \| 0.0626 \| 14585344 \| \| 0.0605 \| 1.3578 \| 8000 \| 0.0623 \| 14955328 \| \| 0.0412 \| 1.3917 \| 8200 \| 0.0616 \| 15331776 \| \| 0.0396 \| 1.4257 \| 8400 \| 0.0615 \| 15706624 \| \| 0.0307 \| 1.4596 \| 8600 \| 0.0619 \| 16075392 \| \| 0.0412 \| 1.4936 \| 8800 \| 0.0610 \| 16445568 \| \| 0.0981 \| 1.5275 \| 9000 \| 0.0609 \| 16819648 \| \| 0.0615 \| 1.5615 \| 9200 \| 0.0605 \| 17191872 \| \| 0.0829 \| 1.5954 \| 9400 \| 0.0611 \| 17561280 \| \| 0.0505 \| 1.6294 \| 9600 \| 0.0598 \| 17936128 \| \| 0.0755 \| 1.6633 \| 9800 \| 0.0598 \| 18307616 \| \| 0.0499 \| 1.6972 \| 10000 \| 0.0593 \| 18683168 \| \| 0.0665 \| 1.7312 \| 10200 \| 0.0595 \| 19053408 \| \| 0.0485 \| 1.7651 \| 10400 \| 0.0591 \| 19427296 \| \| 0.0618 \| 1.7991 \| 10600 \| 0.0589 \| 19802400 \| \| 0.0495 \| 1.8330 \| 10800 \| 0.0586 \| 20173056 \| \| 0.054 \| 1.8670 \| 11000 \| 0.0585 \| 20550720 \| \| 0.0792 \| 1.9009 \| 11200 \| 0.0589 \| 20920224 \| \| 0.0688 \| 1.9349 \| 11400 \| 0.0581 \| 21289344 \| \| 0.0484 \| 1.9688 \| 11600 \| 0.0580 \| 21666048 \| \| 0.0698 \| 2.0027 \| 11800 \| 0.0603 \| 22041760 \| \| 0.0501 \| 2.0367 \| 12000 \| 0.0576 \| 22412256 \| \| 0.0569 \| 2.0706 \| 12200 \| 0.0573 \| 22782848 \| \| 0.048 \| 2.1046 \| 12400 \| 0.0574 \| 23151392 \| \| 0.0453 \| 2.1385 \| 12600 \| 0.0571 \| 23523648 \| \| 0.0656 \| 2.1724 \| 12800 \| 0.0569 \| 23892992 \| \| 0.0461 \| 2.2064 \| 13000 \| 0.0571 \| 24264192 \| \| 0.0471 \| 2.2403 \| 13200 \| 0.0567 \| 24635264 \| \| 0.0702 \| 2.2743 \| 13400 \| 0.0564 \| 25009664 \| \| 0.0558 \| 2.3082 \| 13600 \| 0.0563 \| 25382432 \| \| 0.0769 \| 2.3422 \| 13800 \| 0.0568 \| 25755616 \| \| 0.0487 \| 2.3761 \| 14000 \| 0.0560 \| 26131424 \| \| 0.0775 \| 2.4101 \| 14200 \| 0.0560 \| 26504960 \| \| 0.0526 \| 2.4440 \| 14400 \| 0.0568 \| 26877888 \| \| 0.0483 \| 2.4780 \| 14600 \| 0.0558 \| 27248384 \| \| 0.0695 \| 2.5119 \| 14800 \| 0.0556 \| 27625376 \| \| 0.0663 \| 2.5458 \| 15000 \| 0.0561 \| 28005696 \| \| 0.0542 \| 2.5798 \| 15200 \| 0.0555 \| 28379936 \| \| 0.0754 \| 2.6137 \| 15400 \| 0.0557 \| 28749536 \| \| 0.0406 \| 2.6477 \| 15600 \| 0.0553 \| 29128672 \| \| 0.0476 \| 2.6816 \| 15800 \| 0.0552 \| 29503456 \| \| 0.0725 \| 2.7156 \| 16000 \| 0.0549 \| 29874176 \| \| 0.07 \| 2.7495 \| 16200 \| 0.0549 \| 30251904 \| \| 0.0544 \| 2.7835 \| 16400 \| 0.0552 \| 30626560 \| \| 0.0545 \| 2.8174 \| 16600 \| 0.0547 \| 30999968 \| \| 0.0425 \| 2.8514 \| 16800 \| 0.0546 \| 31376704 \| \| 0.0646 \| 2.8853 \| 17000 \| 0.0562 \| 31749472 \| \| 0.0542 \| 2.9193 \| 17200 \| 0.0546 \| 32128320 \| \| 0.0445 \| 2.9532 \| 17400 \| 0.0547 \| 32501056 \| \| 0.065 \| 2.9871 \| 17600 \| 0.0541 \| 32872640 \| \| 0.0465 \| 3.0210 \| 17800 \| 0.0543 \| 33243744 \| \| 0.0475 \| 3.0550 \| 18000 \| 0.0546 \| 33619808 \| \| 0.0886 \| 3.0889 \| 18200 \| 0.0543 \| 33994048 \| \| 0.0389 \| 3.1229 \| 18400 \| 0.0544 \| 34361920 \| \| 0.0716 \| 3.1568 \| 18600 \| 0.0537 \| 34735392 \| \| 0.065 \| 3.1908 \| 18800 \| 0.0537 \| 35107872 \| \| 0.0658 \| 3.2247 \| 19000 \| 0.0536 \| 35486976 \| \| 0.063 \| 3.2587 \| 19200 \| 0.0539 \| 35862880 \| \| 0.0491 \| 3.2926 \| 19400 \| 0.0536 \| 36237280 \| \| 0.0656 \| 3.3266 \| 19600 \| 0.0535 \| 36614176 \| \| 0.0568 \| 3.3605 \| 19800 \| 0.0534 \| 36987200 \| \| 0.058 \| 3.3944 \| 20000 \| 0.0537 \| 37357312 \| \| 0.0471 \| 3.4284 \| 20200 \| 0.0533 \| 37728448 \| \| 0.0463 \| 3.4623 \| 20400 \| 0.0535 \| 38104736 \| \| 0.0691 \| 3.4963 \| 20600 \| 0.0534 \| 38477696 \| \| 0.0437 \| 3.5302 \| 20800 \| 0.0531 \| 38847808 \| \| 0.0465 \| 3.5642 \| 21000 \| 0.0529 \| 39222464 \| \| 0.0529 \| 3.5981 \| 21200 \| 0.0530 \| 39595392 \| \| 0.0699 \| 3.6321 \| 21400 \| 0.0530 \| 39971968 \| \| 0.063 \| 3.6660 \| 21600 \| 0.0529 \| 40341952 \| \| 0.0664 \| 3.7000 \| 21800 \| 0.0530 \| 40713376 \| \| 0.0464 \| 3.7339 \| 22000 \| 0.0535 \| 41085856 \| \| 0.0474 \| 3.7679 \| 22200 \| 0.0527 \| 41461568 \| \| 0.0436 \| 3.8018 \| 22400 \| 0.0526 \| 41833280 \| \| 0.0458 \| 3.8357 \| 22600 \| 0.0526 \| 42205152 \| \| 0.0419 \| 3.8697 \| 22800 \| 0.0526 \| 42578144 \| \| 0.0587 \| 3.9036 \| 23000 \| 0.0527 \| 42956608 \| \| 0.0522 \| 3.9376 \| 23200 \| 0.0526 \| 43327904 \| \| 0.0315 \| 3.9715 \| 23400 \| 0.0524 \| 43700960 \| \| 0.04 \| 4.0054 \| 23600 \| 0.0524 \| 44077568 \| \| 0.051 \| 4.0394 \| 23800 \| 0.0528 \| 44449632 \| \| 0.0667 \| 4.0733 \| 24000 \| 0.0524 \| 44825184 \| \| 0.0606 \| 4.1073 \| 24200 \| 0.0522 \| 45195872 \| \| 0.0362 \| 4.1412 \| 24400 \| 0.0525 \| 45566816 \| \| 0.0487 \| 4.1752 \| 24600 \| 0.0523 \| 45945824 \| \| 0.0492 \| 4.2091 \| 24800 \| 0.0525 \| 46322304 \| \| 0.0365 \| 4.2431 \| 25000 \| 0.0522 \| 46694976 \| \| 0.0683 \| 4.2770 \| 25200 \| 0.0521 \| 47069472 \| \| 0.0513 \| 4.3109 \| 25400 \| 0.0522 \| 47444064 \| \| 0.0546 \| 4.3449 \| 25600 \| 0.0522 \| 47819744 \| \| 0.0593 \| 4.3788 \| 25800 \| 0.0522 \| 48190912 \| \| 0.0514 \| 4.4128 \| 26000 \| 0.0528 \| 48563040 \| \| 0.0454 \| 4.4467 \| 26200 \| 0.0520 \| 48936320 \| \| 0.0486 \| 4.4807 \| 26400 \| 0.0519 \| 49306944 \| \| 0.0393 \| 4.5146 \| 26600 \| 0.0521 \| 49683712 \| \| 0.0322 \| 4.5486 \| 26800 \| 0.0519 \| 50057824 \| \| 0.042 \| 4.5825 \| 27000 \| 0.0518 \| 50431552 \| \| 0.058 \| 4.6165 \| 27200 \| 0.0518 \| 50808576 \| \| 0.0489 \| 4.6504 \| 27400 \| 0.0518 \| 51182144 \| \| 0.0376 \| 4.6843 \| 27600 \| 0.0517 \| 51554016 \| \| 0.0524 \| 4.7183 \| 27800 \| 0.0518 \| 51925888 \| \| 0.05 \| 4.7522 \| 28000 \| 0.0519 \| 52295168 \| \| 0.0391 \| 4.7862 \| 28200 \| 0.0519 \| 52664096 \| \| 0.038 \| 4.8201 \| 28400 \| 0.0517 \| 53038784 \| \| 0.0566 \| 4.8541 \| 28600 \| 0.0517 \| 53412352 \| \| 0.0506 \| 4.8880 \| 28800 \| 0.0517 \| 53788608 \| \| 0.0616 \| 4.9220 \| 29000 \| 0.0518 \| 54166176 \| \| 0.0675 \| 4.9559 \| 29200 \| 0.0518 \| 54541216 \| \| 0.066 \| 4.9899 \| 29400 \| 0.0517 \| 54916928 \| \| 0.0629 \| 5.0238 \| 29600 \| 0.0516 \| 55288160 \| \| 0.0287 \| 5.0577 \| 29800 \| 0.0516 \| 55662784 \| \| 0.0421 \| 5.0917 \| 30000 \| 0.0519 \| 56034432 \| \| 0.0298 \| 5.1256 \| 30200 \| 0.0518 \| 56405792 \| \| 0.0739 \| 5.1595 \| 30400 \| 0.0516 \| 56777504 \| \| 0.046 \| 5.1935 \| 30600 \| 0.0516 \| 57149760 \| \| 0.0529 \| 5.2274 \| 30800 \| 0.0515 \| 57521536 \| \| 0.0289 \| 5.2614 \| 31000 \| 0.0514 \| 57889408 \| \| 0.0424 \| 5.2953 \| 31200 \| 0.0519 \| 58258624 \| \| 0.0427 \| 5.3293 \| 31400 \| 0.0517 \| 58635520 \| \| 0.0425 \| 5.3632 \| 31600 \| 0.0519 \| 59006592 \| \| 0.0518 \| 5.3972 \| 31800 \| 0.0515 \| 59381312 \| \| 0.0716 \| 5.4311 \| 32000 \| 0.0514 \| 59761568 \| \| 0.059 \| 5.4651 \| 32200 \| 0.0516 \| 60138720 \| \| 0.0601 \| 5.4990 \| 32400 \| 0.0516 \| 60511168 \| \| 0.0695 \| 5.5329 \| 32600 \| 0.0514 \| 60884448 \| \| 0.0269 \| 5.5669 \| 32800 \| 0.0513 \| 61259680 \| \| 0.0423 \| 5.6008 \| 33000 \| 0.0514 \| 61636416 \| \| 0.0843 \| 5.6348 \| 33200 \| 0.0514 \| 62013760 \| \| 0.0657 \| 5.6687 \| 33400 \| 0.0516 \| 62389440 \| \| 0.0834 \| 5.7027 \| 33600 \| 0.0514 \| 62764512 \| \| 0.0725 \| 5.7366 \| 33800 \| 0.0514 \| 63139872 \| \| 0.0354 \| 5.7706 \| 34000 \| 0.0514 \| 63517632 \| \| 0.0817 \| 5.8045 \| 34200 \| 0.0515 \| 63889248 \| \| 0.0493 \| 5.8385 \| 34400 \| 0.0513 \| 64262048 \| \| 0.0603 \| 5.8724 \| 34600 \| 0.0513 \| 64632256 \| \| 0.0322 \| 5.9064 \| 34800 \| 0.0513 \| 65006944 \| \| 0.08 \| 5.9403 \| 35000 \| 0.0513 \| 65382656 \| \| 0.0451 \| 5.9742 \| 35200 \| 0.0514 \| 65756992 \| \| 0.0516 \| 6.0081 \| 35400 \| 0.0513 \| 66125280 \| \| 0.0647 \| 6.0421 \| 35600 \| 0.0513 \| 66493536 \| \| 0.0448 \| 6.0760 \| 35800 \| 0.0514 \| 66867936 \| \| 0.0546 \| 6.1100 \| 36000 \| 0.0515 \| 67243328 \| \| 0.0449 \| 6.1439 \| 36200 \| 0.0516 \| 67616992 \| \| 0.0329 \| 6.1779 \| 36400 \| 0.0516 \| 67995520 \| \| 0.035 \| 6.2118 \| 36600 \| 0.0514 \| 68370624 \| \| 0.0461 \| 6.2458 \| 36800 \| 0.0514 \| 68746880 \| \| 0.0456 \| 6.2797 \| 37000 \| 0.0515 \| 69119328 \| \| 0.0573 \| 6.3137 \| 37200 \| 0.0514 \| 69490336 \| \| 0.0501 \| 6.3476 \| 37400 \| 0.0515 \| 69862688 \| \| 0.0323 \| 6.3816 \| 37600 \| 0.0513 \| 70238592 \| \| 0.0381 \| 6.4155 \| 37800 \| 0.0514 \| 70612608 \| \| 0.054 \| 6.4494 \| 38000 \| 0.0514 \| 70985568 \| \| 0.0242 \| 6.4834 \| 38200 \| 0.0515 \| 71360704 \| \| 0.0399 \| 6.5173 \| 38400 \| 0.0515 \| 71738432 \| \| 0.0286 \| 6.5513 \| 38600 \| 0.0514 \| 72112640 \| \| 0.0532 \| 6.5852 \| 38800 \| 0.0514 \| 72484256 \| \| 0.0447 \| 6.6192 \| 39000 \| 0.0515 \| 72858912 \| \| 0.046 \| 6.6531 \| 39200 \| 0.0513 \| 73232576 \| \| 0.0673 \| 6.6871 \| 39400 \| 0.0516 \| 73604352 \| \| 0.0389 \| 6.7210 \| 39600 \| 0.0514 \| 73975648 \| \| 0.0397 \| 6.7550 \| 39800 \| 0.0514 \| 74349632 \| \| 0.0395 \| 6.7889 \| 40000 \| 0.0514 \| 74724160 \| ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
dzanbek/0039ab53-95ed-4f05-9bd4-15486ae682bc	dzanbek	"2025-04-19T09:05:59Z"	0	0	peft	[ "peft", "safetensors", "opt", "axolotl", "generated_from_trainer", "base_model:facebook/opt-1.3b", "base_model:adapter:facebook/opt-1.3b", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-04-19T08:54:46Z"	--- library_name: peft license: other base_model: facebook/opt-1.3b tags: - axolotl - generated_from_trainer model-index: - name: 0039ab53-95ed-4f05-9bd4-15486ae682bc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: facebook/opt-1.3b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 45af3457229c3363_train_data.json ds_type: json format: custom path: /workspace/input_data/45af3457229c3363_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/0039ab53-95ed-4f05-9bd4-15486ae682bc hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/45af3457229c3363_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 047ce7a0-d57c-486c-ba3f-1f0804c929df wandb_project: 01-31 wandb_run: your_name wandb_runid: 047ce7a0-d57c-486c-ba3f-1f0804c929df warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 0039ab53-95ed-4f05-9bd4-15486ae682bc This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.964 \| 0.0114 \| 150 \| 3.0908 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
John6666/noborumix-illustrious-xl-20-merged-v20-illustrious20-sdxl	John6666	"2025-04-19T09:04:12Z"	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "Illustrious XL v2.0", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-XL-v2.0", "base_model:finetune:OnomaAIResearch/Illustrious-XL-v2.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	"2025-04-19T08:58:45Z"	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - Illustrious XL v2.0 - illustrious base_model: OnomaAIResearch/Illustrious-XL-v2.0 --- Original model is [here](https://civitai.com/models/1439680?modelVersionId=1681362). This model created by [noboru6703](https://civitai.com/user/noboru6703).
mlfoundations-dev/b1_science_top_16_10k	mlfoundations-dev	"2025-04-19T08:51:23Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-19T04:45:08Z"	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b1_science_top_16_10k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # b1_science_top_16_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b1_science_top_16_10k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
vuongpro/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_skilled_owl	vuongpro	"2025-04-19T08:45:50Z"	4	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am scavenging skilled owl", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-14T21:16:54Z"	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_skilled_owl tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am scavenging skilled owl - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_skilled_owl This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vuongpro/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_skilled_owl", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ahmet71cakir/phi4-turbochat	ahmet71cakir	"2025-04-19T08:42:25Z"	21	0	peft	[ "peft", "safetensors", "phi3", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-4-mini-instruct", "base_model:adapter:microsoft/Phi-4-mini-instruct", "license:mit", "region:us" ]	null	"2025-04-18T17:12:05Z"	--- library_name: peft license: mit base_model: microsoft/Phi-4-mini-instruct tags: - generated_from_trainer model-index: - name: phi4-turbochat results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi4-turbochat This model is a fine-tuned version of [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
rbelanec/train_sst2_1744902625	rbelanec	"2025-04-19T08:41:51Z"	0	0	peft	[ "peft", "safetensors", "llama-factory", "ia3", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]	null	"2025-04-18T22:40:11Z"	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - llama-factory - ia3 - generated_from_trainer model-index: - name: train_sst2_1744902625 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # train_sst2_1744902625 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0713 - Num Input Tokens Seen: 33458560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:-----------------:\| \| 0.1318 \| 0.0528 \| 200 \| 0.1586 \| 166688 \| \| 0.1951 \| 0.1056 \| 400 \| 0.1397 \| 334048 \| \| 0.0693 \| 0.1584 \| 600 \| 0.1319 \| 500448 \| \| 0.0969 \| 0.2112 \| 800 \| 0.1235 \| 667872 \| \| 0.1196 \| 0.2640 \| 1000 \| 0.1205 \| 834848 \| \| 0.1125 \| 0.3167 \| 1200 \| 0.1172 \| 1002816 \| \| 0.0759 \| 0.3695 \| 1400 \| 0.1145 \| 1169088 \| \| 0.2051 \| 0.4223 \| 1600 \| 0.1123 \| 1337088 \| \| 0.1022 \| 0.4751 \| 1800 \| 0.1098 \| 1505536 \| \| 0.1273 \| 0.5279 \| 2000 \| 0.1084 \| 1673024 \| \| 0.1299 \| 0.5807 \| 2200 \| 0.1064 \| 1842304 \| \| 0.1179 \| 0.6335 \| 2400 \| 0.1051 \| 2007328 \| \| 0.0799 \| 0.6863 \| 2600 \| 0.1043 \| 2174880 \| \| 0.1575 \| 0.7391 \| 2800 \| 0.1027 \| 2341280 \| \| 0.116 \| 0.7919 \| 3000 \| 0.1017 \| 2509440 \| \| 0.0956 \| 0.8447 \| 3200 \| 0.1001 \| 2674784 \| \| 0.0767 \| 0.8975 \| 3400 \| 0.0986 \| 2843680 \| \| 0.1587 \| 0.9502 \| 3600 \| 0.1008 \| 3011904 \| \| 0.0879 \| 1.0029 \| 3800 \| 0.0968 \| 3178064 \| \| 0.1003 \| 1.0557 \| 4000 \| 0.0961 \| 3345904 \| \| 0.0937 \| 1.1085 \| 4200 \| 0.0947 \| 3514608 \| \| 0.0834 \| 1.1613 \| 4400 \| 0.0943 \| 3680560 \| \| 0.0678 \| 1.2141 \| 4600 \| 0.0933 \| 3849328 \| \| 0.1148 \| 1.2669 \| 4800 \| 0.0934 \| 4017200 \| \| 0.0771 \| 1.3197 \| 5000 \| 0.0917 \| 4187184 \| \| 0.0694 \| 1.3724 \| 5200 \| 0.0915 \| 4354416 \| \| 0.0979 \| 1.4252 \| 5400 \| 0.0907 \| 4519856 \| \| 0.1024 \| 1.4780 \| 5600 \| 0.0901 \| 4687280 \| \| 0.0657 \| 1.5308 \| 5800 \| 0.0896 \| 4856112 \| \| 0.0484 \| 1.5836 \| 6000 \| 0.0896 \| 5022736 \| \| 0.1531 \| 1.6364 \| 6200 \| 0.0881 \| 5188656 \| \| 0.0504 \| 1.6892 \| 6400 \| 0.0878 \| 5356208 \| \| 0.1301 \| 1.7420 \| 6600 \| 0.0874 \| 5523952 \| \| 0.1359 \| 1.7948 \| 6800 \| 0.0867 \| 5690672 \| \| 0.0949 \| 1.8476 \| 7000 \| 0.0864 \| 5857072 \| \| 0.0511 \| 1.9004 \| 7200 \| 0.0864 \| 6024976 \| \| 0.0878 \| 1.9531 \| 7400 \| 0.0859 \| 6191664 \| \| 0.0956 \| 2.0058 \| 7600 \| 0.0850 \| 6357472 \| \| 0.1002 \| 2.0586 \| 7800 \| 0.0851 \| 6525984 \| \| 0.0851 \| 2.1114 \| 8000 \| 0.0841 \| 6692320 \| \| 0.0766 \| 2.1642 \| 8200 \| 0.0838 \| 6860064 \| \| 0.1541 \| 2.2170 \| 8400 \| 0.0846 \| 7026528 \| \| 0.0482 \| 2.2698 \| 8600 \| 0.0834 \| 7192384 \| \| 0.1002 \| 2.3226 \| 8800 \| 0.0832 \| 7358816 \| \| 0.0775 \| 2.3753 \| 9000 \| 0.0827 \| 7526496 \| \| 0.0724 \| 2.4281 \| 9200 \| 0.0824 \| 7696064 \| \| 0.0841 \| 2.4809 \| 9400 \| 0.0831 \| 7863456 \| \| 0.1016 \| 2.5337 \| 9600 \| 0.0818 \| 8031776 \| \| 0.0756 \| 2.5865 \| 9800 \| 0.0819 \| 8199584 \| \| 0.0949 \| 2.6393 \| 10000 \| 0.0810 \| 8366016 \| \| 0.0677 \| 2.6921 \| 10200 \| 0.0812 \| 8531808 \| \| 0.0611 \| 2.7449 \| 10400 \| 0.0807 \| 8702976 \| \| 0.0474 \| 2.7977 \| 10600 \| 0.0804 \| 8870944 \| \| 0.0933 \| 2.8505 \| 10800 \| 0.0812 \| 9039680 \| \| 0.1127 \| 2.9033 \| 11000 \| 0.0813 \| 9206880 \| \| 0.0633 \| 2.9561 \| 11200 \| 0.0802 \| 9372128 \| \| 0.0816 \| 3.0087 \| 11400 \| 0.0794 \| 9538768 \| \| 0.0781 \| 3.0615 \| 11600 \| 0.0791 \| 9705232 \| \| 0.0599 \| 3.1143 \| 11800 \| 0.0793 \| 9871632 \| \| 0.0713 \| 3.1671 \| 12000 \| 0.0794 \| 10039472 \| \| 0.0291 \| 3.2199 \| 12200 \| 0.0789 \| 10206320 \| \| 0.0547 \| 3.2727 \| 12400 \| 0.0785 \| 10376240 \| \| 0.0882 \| 3.3255 \| 12600 \| 0.0787 \| 10544464 \| \| 0.0322 \| 3.3782 \| 12800 \| 0.0781 \| 10712240 \| \| 0.0395 \| 3.4310 \| 13000 \| 0.0778 \| 10879120 \| \| 0.0472 \| 3.4838 \| 13200 \| 0.0779 \| 11045072 \| \| 0.0689 \| 3.5366 \| 13400 \| 0.0781 \| 11211312 \| \| 0.09 \| 3.5894 \| 13600 \| 0.0774 \| 11378128 \| \| 0.0392 \| 3.6422 \| 13800 \| 0.0780 \| 11544592 \| \| 0.1368 \| 3.6950 \| 14000 \| 0.0771 \| 11713040 \| \| 0.1223 \| 3.7478 \| 14200 \| 0.0774 \| 11880432 \| \| 0.106 \| 3.8006 \| 14400 \| 0.0765 \| 12048176 \| \| 0.049 \| 3.8534 \| 14600 \| 0.0771 \| 12215792 \| \| 0.0427 \| 3.9062 \| 14800 \| 0.0769 \| 12383792 \| \| 0.052 \| 3.9590 \| 15000 \| 0.0764 \| 12549680 \| \| 0.0927 \| 4.0116 \| 15200 \| 0.0763 \| 12716448 \| \| 0.0437 \| 4.0644 \| 15400 \| 0.0767 \| 12882752 \| \| 0.0549 \| 4.1172 \| 15600 \| 0.0764 \| 13051200 \| \| 0.0587 \| 4.1700 \| 15800 \| 0.0761 \| 13217024 \| \| 0.0562 \| 4.2228 \| 16000 \| 0.0757 \| 13382784 \| \| 0.0657 \| 4.2756 \| 16200 \| 0.0764 \| 13549216 \| \| 0.0374 \| 4.3284 \| 16400 \| 0.0752 \| 13719072 \| \| 0.1196 \| 4.3812 \| 16600 \| 0.0752 \| 13884928 \| \| 0.0847 \| 4.4339 \| 16800 \| 0.0751 \| 14051584 \| \| 0.0485 \| 4.4867 \| 17000 \| 0.0769 \| 14220704 \| \| 0.0352 \| 4.5395 \| 17200 \| 0.0749 \| 14387008 \| \| 0.1084 \| 4.5923 \| 17400 \| 0.0749 \| 14555808 \| \| 0.0591 \| 4.6451 \| 17600 \| 0.0755 \| 14723456 \| \| 0.116 \| 4.6979 \| 17800 \| 0.0749 \| 14890880 \| \| 0.0692 \| 4.7507 \| 18000 \| 0.0755 \| 15059744 \| \| 0.0686 \| 4.8035 \| 18200 \| 0.0746 \| 15224512 \| \| 0.1239 \| 4.8563 \| 18400 \| 0.0744 \| 15392960 \| \| 0.0474 \| 4.9091 \| 18600 \| 0.0744 \| 15561696 \| \| 0.0925 \| 4.9619 \| 18800 \| 0.0744 \| 15728800 \| \| 0.0724 \| 5.0145 \| 19000 \| 0.0741 \| 15897552 \| \| 0.0674 \| 5.0673 \| 19200 \| 0.0740 \| 16064688 \| \| 0.0695 \| 5.1201 \| 19400 \| 0.0740 \| 16231120 \| \| 0.0706 \| 5.1729 \| 19600 \| 0.0737 \| 16397744 \| \| 0.1331 \| 5.2257 \| 19800 \| 0.0738 \| 16564176 \| \| 0.0663 \| 5.2785 \| 20000 \| 0.0737 \| 16731600 \| \| 0.0327 \| 5.3313 \| 20200 \| 0.0748 \| 16898064 \| \| 0.0879 \| 5.3841 \| 20400 \| 0.0738 \| 17064080 \| \| 0.0532 \| 5.4368 \| 20600 \| 0.0736 \| 17231888 \| \| 0.0614 \| 5.4896 \| 20800 \| 0.0735 \| 17399184 \| \| 0.0563 \| 5.5424 \| 21000 \| 0.0745 \| 17566160 \| \| 0.0631 \| 5.5952 \| 21200 \| 0.0736 \| 17732304 \| \| 0.0431 \| 5.6480 \| 21400 \| 0.0733 \| 17900880 \| \| 0.0466 \| 5.7008 \| 21600 \| 0.0733 \| 18070192 \| \| 0.0843 \| 5.7536 \| 21800 \| 0.0732 \| 18237168 \| \| 0.0494 \| 5.8064 \| 22000 \| 0.0731 \| 18403856 \| \| 0.1229 \| 5.8592 \| 22200 \| 0.0732 \| 18571248 \| \| 0.0307 \| 5.9120 \| 22400 \| 0.0731 \| 18738672 \| \| 0.0534 \| 5.9648 \| 22600 \| 0.0730 \| 18905744 \| \| 0.0806 \| 6.0174 \| 22800 \| 0.0731 \| 19073440 \| \| 0.0733 \| 6.0702 \| 23000 \| 0.0732 \| 19241920 \| \| 0.1169 \| 6.1230 \| 23200 \| 0.0732 \| 19409408 \| \| 0.0757 \| 6.1758 \| 23400 \| 0.0731 \| 19577024 \| \| 0.0495 \| 6.2286 \| 23600 \| 0.0728 \| 19744608 \| \| 0.0752 \| 6.2814 \| 23800 \| 0.0727 \| 19911488 \| \| 0.0694 \| 6.3342 \| 24000 \| 0.0726 \| 20078944 \| \| 0.0617 \| 6.3870 \| 24200 \| 0.0727 \| 20244928 \| \| 0.093 \| 6.4398 \| 24400 \| 0.0725 \| 20411232 \| \| 0.0579 \| 6.4925 \| 24600 \| 0.0728 \| 20578080 \| \| 0.0712 \| 6.5453 \| 24800 \| 0.0725 \| 20746592 \| \| 0.1026 \| 6.5981 \| 25000 \| 0.0727 \| 20913344 \| \| 0.0384 \| 6.6509 \| 25200 \| 0.0725 \| 21081952 \| \| 0.0928 \| 6.7037 \| 25400 \| 0.0724 \| 21248384 \| \| 0.0907 \| 6.7565 \| 25600 \| 0.0723 \| 21415872 \| \| 0.0511 \| 6.8093 \| 25800 \| 0.0729 \| 21584000 \| \| 0.1154 \| 6.8621 \| 26000 \| 0.0723 \| 21751168 \| \| 0.0398 \| 6.9149 \| 26200 \| 0.0722 \| 21918816 \| \| 0.0674 \| 6.9677 \| 26400 \| 0.0723 \| 22084384 \| \| 0.0688 \| 7.0203 \| 26600 \| 0.0722 \| 22251776 \| \| 0.0766 \| 7.0731 \| 26800 \| 0.0722 \| 22418080 \| \| 0.0622 \| 7.1259 \| 27000 \| 0.0721 \| 22587392 \| \| 0.0562 \| 7.1787 \| 27200 \| 0.0721 \| 22753056 \| \| 0.0631 \| 7.2315 \| 27400 \| 0.0724 \| 22920768 \| \| 0.0828 \| 7.2843 \| 27600 \| 0.0718 \| 23087296 \| \| 0.0412 \| 7.3371 \| 27800 \| 0.0721 \| 23254400 \| \| 0.0324 \| 7.3899 \| 28000 \| 0.0721 \| 23422752 \| \| 0.0441 \| 7.4427 \| 28200 \| 0.0721 \| 23588352 \| \| 0.0616 \| 7.4954 \| 28400 \| 0.0723 \| 23755840 \| \| 0.0565 \| 7.5482 \| 28600 \| 0.0721 \| 23923680 \| \| 0.0559 \| 7.6010 \| 28800 \| 0.0719 \| 24091168 \| \| 0.0394 \| 7.6538 \| 29000 \| 0.0721 \| 24258016 \| \| 0.0899 \| 7.7066 \| 29200 \| 0.0718 \| 24427808 \| \| 0.0231 \| 7.7594 \| 29400 \| 0.0718 \| 24596288 \| \| 0.0492 \| 7.8122 \| 29600 \| 0.0718 \| 24764192 \| \| 0.0627 \| 7.8650 \| 29800 \| 0.0719 \| 24932000 \| \| 0.0346 \| 7.9178 \| 30000 \| 0.0718 \| 25100224 \| \| 0.0597 \| 7.9706 \| 30200 \| 0.0722 \| 25267808 \| \| 0.0569 \| 8.0232 \| 30400 \| 0.0720 \| 25433440 \| \| 0.0757 \| 8.0760 \| 30600 \| 0.0717 \| 25600672 \| \| 0.0524 \| 8.1288 \| 30800 \| 0.0718 \| 25769408 \| \| 0.0424 \| 8.1816 \| 31000 \| 0.0717 \| 25936160 \| \| 0.0652 \| 8.2344 \| 31200 \| 0.0718 \| 26103744 \| \| 0.0822 \| 8.2872 \| 31400 \| 0.0715 \| 26270560 \| \| 0.0691 \| 8.3400 \| 31600 \| 0.0719 \| 26437536 \| \| 0.031 \| 8.3928 \| 31800 \| 0.0719 \| 26604480 \| \| 0.0484 \| 8.4456 \| 32000 \| 0.0716 \| 26771680 \| \| 0.1148 \| 8.4984 \| 32200 \| 0.0716 \| 26940256 \| \| 0.073 \| 8.5511 \| 32400 \| 0.0715 \| 27107680 \| \| 0.0813 \| 8.6039 \| 32600 \| 0.0718 \| 27274048 \| \| 0.1232 \| 8.6567 \| 32800 \| 0.0717 \| 27440544 \| \| 0.0994 \| 8.7095 \| 33000 \| 0.0716 \| 27608000 \| \| 0.0363 \| 8.7623 \| 33200 \| 0.0715 \| 27776704 \| \| 0.016 \| 8.8151 \| 33400 \| 0.0717 \| 27942752 \| \| 0.0744 \| 8.8679 \| 33600 \| 0.0716 \| 28108864 \| \| 0.0325 \| 8.9207 \| 33800 \| 0.0714 \| 28275296 \| \| 0.0517 \| 8.9735 \| 34000 \| 0.0716 \| 28443520 \| \| 0.028 \| 9.0261 \| 34200 \| 0.0716 \| 28609776 \| \| 0.061 \| 9.0789 \| 34400 \| 0.0716 \| 28777712 \| \| 0.1408 \| 9.1317 \| 34600 \| 0.0717 \| 28944144 \| \| 0.0362 \| 9.1845 \| 34800 \| 0.0716 \| 29111152 \| \| 0.0993 \| 9.2373 \| 35000 \| 0.0716 \| 29278000 \| \| 0.0391 \| 9.2901 \| 35200 \| 0.0716 \| 29443792 \| \| 0.0398 \| 9.3429 \| 35400 \| 0.0716 \| 29609072 \| \| 0.0981 \| 9.3957 \| 35600 \| 0.0715 \| 29776592 \| \| 0.0716 \| 9.4485 \| 35800 \| 0.0716 \| 29941616 \| \| 0.066 \| 9.5013 \| 36000 \| 0.0717 \| 30110160 \| \| 0.0694 \| 9.5540 \| 36200 \| 0.0716 \| 30277744 \| \| 0.1284 \| 9.6068 \| 36400 \| 0.0716 \| 30447152 \| \| 0.028 \| 9.6596 \| 36600 \| 0.0713 \| 30612976 \| \| 0.0429 \| 9.7124 \| 36800 \| 0.0714 \| 30780240 \| \| 0.0227 \| 9.7652 \| 37000 \| 0.0715 \| 30948048 \| \| 0.05 \| 9.8180 \| 37200 \| 0.0715 \| 31116368 \| \| 0.0342 \| 9.8708 \| 37400 \| 0.0715 \| 31283888 \| \| 0.0368 \| 9.9236 \| 37600 \| 0.0716 \| 31452560 \| \| 0.0681 \| 9.9764 \| 37800 \| 0.0714 \| 31620720 \| \| 0.0867 \| 10.0290 \| 38000 \| 0.0713 \| 31786016 \| \| 0.0869 \| 10.0818 \| 38200 \| 0.0715 \| 31952768 \| \| 0.0735 \| 10.1346 \| 38400 \| 0.0714 \| 32120320 \| \| 0.0173 \| 10.1874 \| 38600 \| 0.0715 \| 32287584 \| \| 0.0469 \| 10.2402 \| 38800 \| 0.0716 \| 32455072 \| \| 0.0459 \| 10.2930 \| 39000 \| 0.0713 \| 32621184 \| \| 0.0397 \| 10.3458 \| 39200 \| 0.0714 \| 32788960 \| \| 0.0401 \| 10.3986 \| 39400 \| 0.0716 \| 32955776 \| \| 0.0332 \| 10.4514 \| 39600 \| 0.0716 \| 33122816 \| \| 0.0907 \| 10.5042 \| 39800 \| 0.0716 \| 33291072 \| \| 0.0616 \| 10.5569 \| 40000 \| 0.0716 \| 33458560 \| ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf	RichardErkhov	"2025-04-19T08:41:02Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-19T05:27:18Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mp_mistral7bv3_sft_dpo_beta2e-1_epoch2 - GGUF - Model creator: https://huggingface.co/yjwon/ - Original model: https://huggingface.co/yjwon/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q2_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q2_K.gguf) \| Q2_K \| 2.54GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_XS.gguf) \| IQ3_XS \| 2.82GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_S.gguf) \| IQ3_S \| 2.97GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ4_XS.gguf) \| IQ4_XS \| 3.68GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_1.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_0.gguf) \| Q5_0 \| 4.66GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K_S.gguf) \| Q5_K_S \| 4.66GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_1.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q6_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q6_K.gguf) \| Q6_K \| 5.54GB \| \| [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q8_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

vladimir707/gpt-mini1

vladimir707

"2025-04-19T12:09:43Z"

0

null

[ "pytorch", "region:us" ]

null

"2025-04-19T12:09:30Z"

# GPT Tiny Shakespeare (Decoder-only) Ein leichtgewichtiges autoregressives Transformer-Modell (GPT-artig), trainiert auf dem Tiny Shakespeare-Datensatz. ## Architektur - Decoder-only Transformer (ähnlich GPT-2) - <1M Parameter - 2 Layers, 4 Attention Heads - Embedding-Dimension: 128 ## Trainingsdaten Tiny Shakespeare (ca. 100k Zeichen an Theater-Dialogen von Shakespeare). ## Verwendung Für einfache Textgenerierung und Experimente auf CPUs. ## Tags - gpt - decoder-only - tiny - shakespeare - text-generation - educational

SanderGi/PCB-OBB

SanderGi

"2025-04-19T12:08:57Z"

0

ultralytics

[ "ultralytics", "printed-circuit-boards", "base_model:Ultralytics/YOLO11", "base_model:finetune:Ultralytics/YOLO11", "license:mit", "model-index", "region:us" ]

null

"2025-04-19T11:43:34Z"

--- license: mit base_model: - Ultralytics/YOLO11 tags: - printed-circuit-boards library_name: ultralytics model-index: - name: ultralytics/yolo11 results: - task: type: object-detection metrics: - type: f1 value: 93.8% name: F1 Score - type: mAP50 value: 93.0% name: mAP50 metrics: - f1 - 93.8% - mAP50 - 93.0% --- # PCB Detection There are [a lot of models](https://universe.roboflow.com/roboflow-100/printed-circuit-board/model/3) for detecting components within a Printed Circuit Board (PCB), but not as many for detecting which pixels (if any) in an image contain the PCB itself. Being able to determine if and where a PCB is in an image is useful for [calculating its size to estimate carbon footprint]((https://github.com/SanderGi/LCA)), as a preprocessing step for detecting components, to limit the amount of image more expensive PCB defect detection models have to process, and more. Read more [here](https://github.com/SanderGi/PCB-Detection). ## Usage 1. Download [`the model weights`](https://huggingface.co/SanderGi/PCB-OBB/resolve/main/best.pt?download=true) 2. `pip install ultralytics` 3. Run the model with `yolo task=obb mode=predict model=[path to model weights] source=[path to test image]` from the terminal or with Python: ```python from ultralytics import YOLO model = YOLO('[path to model weights]') results = model.predict('[path/to/test/image.jpg]') ``` ## Results Dataset | Precision | Recall | F1 Score | mAP50 | mAP50-95 -----------|-----------|--------|----------|--------|--------- Training | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% Validation | 100.0% | 100.0% | 100.0% | 99.5% | 97.0% Test | 100.0% | 88.4% | 93.8% | 93.0% | 91.2% Sample predictions: ![sample predictions](https://github.com/SanderGi/PCB-Detection/raw/refs/heads/main/data/augmented_obb/runs/no_perspective3/val_batch1_pred.jpg)

phospho-app/lerobot_v2_ball040-mnsx4wnl5s

phospho-app

"2025-04-19T12:08:20Z"

0

null

[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "replicate", "region:us" ]

null

"2025-04-19T11:31:55Z"

--- tags: - phosphobot - gr00t - replicate task_categories: - robotics --- # Gr00t Model - phospho Replication Pipeline This model was trained using **phospho's Replicate pipeline** for **gr00t models**. Training parameters: - **Dataset**: [pgoffin/lerobot_v2_ball040](https://huggingface.co/datasets/pgoffin/lerobot_v2_ball040) - **Wandb run URL**: https://wandb.ai/artcomputer123-artcomputer/gr00t-replicate/runs/tcprmxc0 - **Epochs**: 20 - **Batch size**: 64 - **Training steps**: 1810 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline) 🔗 **Explore on Replicate**: [Replicate](https://replicate.com/phospho-app/gr00t-policy)

Mehrdadslehi/Qwen2-0.5B-GRPO-SFT1_RL

Mehrdadslehi

"2025-04-19T12:05:13Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "endpoints_compatible", "region:us" ]

null

"2025-04-18T14:43:03Z"

--- datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-SFT1_RL tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-SFT1_RL This model is a fine-tuned version of [None](https://huggingface.co/None) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Mehrdadslehi/Qwen2-0.5B-GRPO-SFT1_RL", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

saisasanky/Llama-3.1-8B-Instruct-4bit-aish_gguf

saisasanky

"2025-04-19T12:05:09Z"

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T12:02:51Z"

--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** saisasanky - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

TOMFORD79/Cake_13

TOMFORD79

"2025-04-19T12:02:30Z"

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

"2025-04-19T10:57:12Z"

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

hyokwan/llama31_famili_2025

hyokwan

"2025-04-19T11:56:41Z"

0

null

[ "safetensors", "llama", "license:apache-2.0", "region:us" ]

null

"2025-04-19T11:43:12Z"

--- license: apache-2.0 ---

rbelanec/train_mrpc_1744902647

rbelanec

"2025-04-19T11:55:12Z"

0

peft

[ "peft", "safetensors", "llama-factory", "prompt-tuning", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]

null

"2025-04-19T03:05:12Z"

--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - llama-factory - prompt-tuning - generated_from_trainer model-index: - name: train_mrpc_1744902647 results: [] ---  # train_mrpc_1744902647 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the mrpc dataset. It achieves the following results on the evaluation set: - Loss: 0.1153 - Num Input Tokens Seen: 65784064 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.3 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:--------:|:-----:|:---------------:|:-----------------:| | 0.178 | 0.9685 | 200 | 0.2087 | 329312 | | 0.1823 | 1.9395 | 400 | 0.1658 | 658560 | | 0.1749 | 2.9104 | 600 | 0.1568 | 987040 | | 0.1421 | 3.8814 | 800 | 0.1488 | 1316448 | | 0.1589 | 4.8523 | 1000 | 0.1386 | 1644608 | | 0.1413 | 5.8232 | 1200 | 0.1437 | 1974016 | | 0.1574 | 6.7942 | 1400 | 0.1415 | 2303584 | | 0.1059 | 7.7651 | 1600 | 0.1394 | 2630688 | | 0.1251 | 8.7361 | 1800 | 0.1364 | 2959808 | | 0.1088 | 9.7070 | 2000 | 0.1153 | 3287584 | | 0.0996 | 10.6780 | 2200 | 0.1288 | 3617920 | | 0.0731 | 11.6489 | 2400 | 0.1425 | 3945536 | | 0.0462 | 12.6199 | 2600 | 0.1264 | 4274560 | | 0.0608 | 13.5908 | 2800 | 0.1430 | 4603168 | | 0.022 | 14.5617 | 3000 | 0.1643 | 4932448 | | 0.0288 | 15.5327 | 3200 | 0.1873 | 5261312 | | 0.0596 | 16.5036 | 3400 | 0.1731 | 5589632 | | 0.0199 | 17.4746 | 3600 | 0.2195 | 5918112 | | 0.0126 | 18.4455 | 3800 | 0.1945 | 6246368 | | 0.0278 | 19.4165 | 4000 | 0.2397 | 6574848 | | 0.0266 | 20.3874 | 4200 | 0.2409 | 6903520 | | 0.005 | 21.3584 | 4400 | 0.2498 | 7231904 | | 0.0084 | 22.3293 | 4600 | 0.2794 | 7561504 | | 0.0394 | 23.3002 | 4800 | 0.3202 | 7890912 | | 0.0073 | 24.2712 | 5000 | 0.2466 | 8218592 | | 0.0014 | 25.2421 | 5200 | 0.2287 | 8548256 | | 0.0264 | 26.2131 | 5400 | 0.2157 | 8876704 | | 0.0123 | 27.1840 | 5600 | 0.2764 | 9206272 | | 0.0033 | 28.1550 | 5800 | 0.2357 | 9534720 | | 0.0057 | 29.1259 | 6000 | 0.2608 | 9864384 | | 0.0052 | 30.0969 | 6200 | 0.2388 | 10193376 | | 0.0011 | 31.0678 | 6400 | 0.3327 | 10521952 | | 0.0081 | 32.0387 | 6600 | 0.3200 | 10851520 | | 0.0102 | 33.0097 | 6800 | 0.2724 | 11180544 | | 0.0056 | 33.9782 | 7000 | 0.3153 | 11509344 | | 0.0106 | 34.9492 | 7200 | 0.3401 | 11838208 | | 0.0221 | 35.9201 | 7400 | 0.2370 | 12167872 | | 0.0417 | 36.8910 | 7600 | 0.2576 | 12496352 | | 0.017 | 37.8620 | 7800 | 0.2671 | 12826048 | | 0.0308 | 38.8329 | 8000 | 0.2404 | 13155040 | | 0.0004 | 39.8039 | 8200 | 0.3460 | 13483008 | | 0.0026 | 40.7748 | 8400 | 0.3096 | 13812064 | | 0.0096 | 41.7458 | 8600 | 0.2907 | 14140576 | | 0.0053 | 42.7167 | 8800 | 0.3575 | 14469248 | | 0.0004 | 43.6877 | 9000 | 0.3422 | 14796672 | | 0.0004 | 44.6586 | 9200 | 0.3874 | 15126752 | | 0.0001 | 45.6295 | 9400 | 0.4214 | 15456160 | | 0.0 | 46.6005 | 9600 | 0.4649 | 15784928 | | 0.0 | 47.5714 | 9800 | 0.4702 | 16113248 | | 0.0 | 48.5424 | 10000 | 0.4790 | 16442496 | | 0.0 | 49.5133 | 10200 | 0.4879 | 16772640 | | 0.0 | 50.4843 | 10400 | 0.4982 | 17100000 | | 0.0 | 51.4552 | 10600 | 0.5047 | 17428768 | | 0.0 | 52.4262 | 10800 | 0.5145 | 17757344 | | 0.0 | 53.3971 | 11000 | 0.5212 | 18085920 | | 0.0 | 54.3680 | 11200 | 0.5300 | 18414336 | | 0.0 | 55.3390 | 11400 | 0.5361 | 18743040 | | 0.0 | 56.3099 | 11600 | 0.5398 | 19072928 | | 0.0 | 57.2809 | 11800 | 0.5495 | 19401376 | | 0.0 | 58.2518 | 12000 | 0.5575 | 19730336 | | 0.0 | 59.2228 | 12200 | 0.5617 | 20059488 | | 0.0 | 60.1937 | 12400 | 0.5684 | 20388064 | | 0.0 | 61.1646 | 12600 | 0.5766 | 20718144 | | 0.0 | 62.1356 | 12800 | 0.5837 | 21048224 | | 0.0 | 63.1065 | 13000 | 0.5898 | 21376576 | | 0.0 | 64.0775 | 13200 | 0.5944 | 21706080 | | 0.0 | 65.0484 | 13400 | 0.6017 | 22034624 | | 0.0 | 66.0194 | 13600 | 0.6081 | 22364128 | | 0.0 | 66.9879 | 13800 | 0.6153 | 22692352 | | 0.0 | 67.9588 | 14000 | 0.6185 | 23020864 | | 0.0 | 68.9298 | 14200 | 0.6256 | 23349920 | | 0.0 | 69.9007 | 14400 | 0.6316 | 23679072 | | 0.0 | 70.8717 | 14600 | 0.6375 | 24007776 | | 0.0 | 71.8426 | 14800 | 0.6423 | 24336640 | | 0.0 | 72.8136 | 15000 | 0.6482 | 24664576 | | 0.0 | 73.7845 | 15200 | 0.6532 | 24994848 | | 0.0 | 74.7554 | 15400 | 0.6600 | 25322720 | | 0.0 | 75.7264 | 15600 | 0.6636 | 25650784 | | 0.0 | 76.6973 | 15800 | 0.6717 | 25980512 | | 0.0 | 77.6683 | 16000 | 0.6783 | 26309536 | | 0.0 | 78.6392 | 16200 | 0.6823 | 26638944 | | 0.0 | 79.6102 | 16400 | 0.6852 | 26967360 | | 0.0 | 80.5811 | 16600 | 0.6886 | 27297120 | | 0.0 | 81.5521 | 16800 | 0.6960 | 27626144 | | 0.0 | 82.5230 | 17000 | 0.6995 | 27954656 | | 0.0 | 83.4939 | 17200 | 0.7026 | 28284160 | | 0.0 | 84.4649 | 17400 | 0.7082 | 28612224 | | 0.0 | 85.4358 | 17600 | 0.7175 | 28940448 | | 0.0 | 86.4068 | 17800 | 0.7151 | 29270912 | | 0.0 | 87.3777 | 18000 | 0.7190 | 29599424 | | 0.0 | 88.3487 | 18200 | 0.7216 | 29929280 | | 0.0 | 89.3196 | 18400 | 0.7255 | 30257504 | | 0.0 | 90.2906 | 18600 | 0.7287 | 30586944 | | 0.0 | 91.2615 | 18800 | 0.7326 | 30915744 | | 0.0 | 92.2324 | 19000 | 0.7370 | 31245216 | | 0.0 | 93.2034 | 19200 | 0.7390 | 31573600 | | 0.0 | 94.1743 | 19400 | 0.7438 | 31903616 | | 0.0 | 95.1453 | 19600 | 0.7448 | 32232032 | | 0.0 | 96.1162 | 19800 | 0.7473 | 32560480 | | 0.0 | 97.0872 | 20000 | 0.7519 | 32889696 | | 0.0 | 98.0581 | 20200 | 0.7500 | 33218016 | | 0.0 | 99.0291 | 20400 | 0.7546 | 33547296 | | 0.0 | 99.9976 | 20600 | 0.7566 | 33876000 | | 0.0 | 100.9685 | 20800 | 0.7594 | 34205376 | | 0.0 | 101.9395 | 21000 | 0.7605 | 34534496 | | 0.0 | 102.9104 | 21200 | 0.7605 | 34864000 | | 0.0 | 103.8814 | 21400 | 0.7587 | 35192256 | | 0.0 | 104.8523 | 21600 | 0.7634 | 35521376 | | 0.0 | 105.8232 | 21800 | 0.7686 | 35851264 | | 0.0 | 106.7942 | 22000 | 0.7694 | 36180000 | | 0.0 | 107.7651 | 22200 | 0.7666 | 36508832 | | 0.0 | 108.7361 | 22400 | 0.7679 | 36837600 | | 0.0 | 109.7070 | 22600 | 0.7702 | 37166720 | | 0.0 | 110.6780 | 22800 | 0.7690 | 37495520 | | 0.0 | 111.6489 | 23000 | 0.7686 | 37824352 | | 0.0 | 112.6199 | 23200 | 0.7735 | 38153856 | | 0.0 | 113.5908 | 23400 | 0.7741 | 38483200 | | 0.0 | 114.5617 | 23600 | 0.7726 | 38812672 | | 0.0 | 115.5327 | 23800 | 0.7704 | 39142400 | | 0.0 | 116.5036 | 24000 | 0.7778 | 39471200 | | 0.0 | 117.4746 | 24200 | 0.7778 | 39798848 | | 0.0 | 118.4455 | 24400 | 0.7782 | 40127360 | | 0.0 | 119.4165 | 24600 | 0.7768 | 40456736 | | 0.0 | 120.3874 | 24800 | 0.7763 | 40785312 | | 0.0 | 121.3584 | 25000 | 0.7760 | 41112576 | | 0.0 | 122.3293 | 25200 | 0.7755 | 41442112 | | 0.0 | 123.3002 | 25400 | 0.7797 | 41771552 | | 0.0 | 124.2712 | 25600 | 0.7775 | 42101248 | | 0.0 | 125.2421 | 25800 | 0.7784 | 42427392 | | 0.0 | 126.2131 | 26000 | 0.7776 | 42756704 | | 0.0 | 127.1840 | 26200 | 0.7778 | 43085664 | | 0.0 | 128.1550 | 26400 | 0.7804 | 43414240 | | 0.0 | 129.1259 | 26600 | 0.7818 | 43743072 | | 0.0 | 130.0969 | 26800 | 0.7803 | 44072768 | | 0.0 | 131.0678 | 27000 | 0.7818 | 44400192 | | 0.0 | 132.0387 | 27200 | 0.7777 | 44729632 | | 0.0 | 133.0097 | 27400 | 0.7763 | 45058976 | | 0.0 | 133.9782 | 27600 | 0.7758 | 45388352 | | 0.0 | 134.9492 | 27800 | 0.7759 | 45717952 | | 0.0 | 135.9201 | 28000 | 0.7753 | 46046144 | | 0.0 | 136.8910 | 28200 | 0.7770 | 46375168 | | 0.0 | 137.8620 | 28400 | 0.7772 | 46702816 | | 0.0 | 138.8329 | 28600 | 0.7731 | 47033152 | | 0.0 | 139.8039 | 28800 | 0.7766 | 47361472 | | 0.0 | 140.7748 | 29000 | 0.7751 | 47691424 | | 0.0 | 141.7458 | 29200 | 0.7738 | 48019712 | | 0.0 | 142.7167 | 29400 | 0.7735 | 48348832 | | 0.0 | 143.6877 | 29600 | 0.7778 | 48678560 | | 0.0 | 144.6586 | 29800 | 0.7774 | 49008256 | | 0.0 | 145.6295 | 30000 | 0.7776 | 49337088 | | 0.0 | 146.6005 | 30200 | 0.7748 | 49665344 | | 0.0 | 147.5714 | 30400 | 0.7795 | 49996128 | | 0.0 | 148.5424 | 30600 | 0.7759 | 50324736 | | 0.0 | 149.5133 | 30800 | 0.7778 | 50652864 | | 0.0 | 150.4843 | 31000 | 0.7747 | 50981920 | | 0.0 | 151.4552 | 31200 | 0.7766 | 51310752 | | 0.0 | 152.4262 | 31400 | 0.7740 | 51640352 | | 0.0 | 153.3971 | 31600 | 0.7767 | 51969184 | | 0.0 | 154.3680 | 31800 | 0.7749 | 52297280 | | 0.0 | 155.3390 | 32000 | 0.7770 | 52625600 | | 0.0 | 156.3099 | 32200 | 0.7763 | 52953920 | | 0.0 | 157.2809 | 32400 | 0.7756 | 53283648 | | 0.0 | 158.2518 | 32600 | 0.7734 | 53613056 | | 0.0 | 159.2228 | 32800 | 0.7745 | 53941632 | | 0.0 | 160.1937 | 33000 | 0.7744 | 54270272 | | 0.0 | 161.1646 | 33200 | 0.7751 | 54599104 | | 0.0 | 162.1356 | 33400 | 0.7762 | 54929056 | | 0.0 | 163.1065 | 33600 | 0.7753 | 55257728 | | 0.0 | 164.0775 | 33800 | 0.7750 | 55587456 | | 0.0 | 165.0484 | 34000 | 0.7761 | 55916576 | | 0.0 | 166.0194 | 34200 | 0.7766 | 56245664 | | 0.0 | 166.9879 | 34400 | 0.7779 | 56574272 | | 0.0 | 167.9588 | 34600 | 0.7757 | 56903360 | | 0.0 | 168.9298 | 34800 | 0.7782 | 57232032 | | 0.0 | 169.9007 | 35000 | 0.7748 | 57561504 | | 0.0 | 170.8717 | 35200 | 0.7741 | 57891168 | | 0.0 | 171.8426 | 35400 | 0.7750 | 58220352 | | 0.0 | 172.8136 | 35600 | 0.7764 | 58548960 | | 0.0 | 173.7845 | 35800 | 0.7737 | 58878688 | | 0.0 | 174.7554 | 36000 | 0.7754 | 59207104 | | 0.0 | 175.7264 | 36200 | 0.7763 | 59536800 | | 0.0 | 176.6973 | 36400 | 0.7769 | 59865312 | | 0.0 | 177.6683 | 36600 | 0.7765 | 60194816 | | 0.0 | 178.6392 | 36800 | 0.7797 | 60523584 | | 0.0 | 179.6102 | 37000 | 0.7767 | 60852352 | | 0.0 | 180.5811 | 37200 | 0.7763 | 61181024 | | 0.0 | 181.5521 | 37400 | 0.7752 | 61510624 | | 0.0 | 182.5230 | 37600 | 0.7787 | 61840672 | | 0.0 | 183.4939 | 37800 | 0.7763 | 62167808 | | 0.0 | 184.4649 | 38000 | 0.7755 | 62496960 | | 0.0 | 185.4358 | 38200 | 0.7765 | 62826016 | | 0.0 | 186.4068 | 38400 | 0.7740 | 63154784 | | 0.0 | 187.3777 | 38600 | 0.7765 | 63483904 | | 0.0 | 188.3487 | 38800 | 0.7759 | 63811808 | | 0.0 | 189.3196 | 39000 | 0.7742 | 64139488 | | 0.0 | 190.2906 | 39200 | 0.7752 | 64467808 | | 0.0 | 191.2615 | 39400 | 0.7753 | 64798112 | | 0.0 | 192.2324 | 39600 | 0.7759 | 65126304 | | 0.0 | 193.2034 | 39800 | 0.7780 | 65455776 | | 0.0 | 194.1743 | 40000 | 0.7753 | 65784064 | ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

shadenmoh/xlm-roberta-base

shadenmoh

"2025-04-19T11:54:04Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:tner/xlm-roberta-base-panx-dataset-ar", "base_model:finetune:tner/xlm-roberta-base-panx-dataset-ar", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

"2025-04-19T11:51:06Z"

--- library_name: transformers base_model: tner/xlm-roberta-base-panx-dataset-ar tags: - generated_from_trainer model-index: - name: xlm-roberta-base results: [] ---  # xlm-roberta-base This model is a fine-tuned version of [tner/xlm-roberta-base-panx-dataset-ar](https://huggingface.co/tner/xlm-roberta-base-panx-dataset-ar) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

Vergill2345/Razor

Vergill2345

"2025-04-19T11:53:44Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2025-04-19T11:53:44Z"

--- license: apache-2.0 ---

rbelanec/train_sst2_1744902627

rbelanec

"2025-04-19T11:52:17Z"

0

peft

[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]

null

"2025-04-18T23:09:42Z"

--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - llama-factory - lora - generated_from_trainer model-index: - name: train_sst2_1744902627 results: [] ---  # train_sst2_1744902627 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0593 - Num Input Tokens Seen: 33458560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:-------:|:-----:|:---------------:|:-----------------:| | 0.0745 | 0.0528 | 200 | 0.0783 | 166688 | | 0.1164 | 0.1056 | 400 | 0.1055 | 334048 | | 0.0125 | 0.1584 | 600 | 0.0809 | 500448 | | 0.0293 | 0.2112 | 800 | 0.0696 | 667872 | | 0.0633 | 0.2640 | 1000 | 0.0697 | 834848 | | 0.037 | 0.3167 | 1200 | 0.0937 | 1002816 | | 0.0586 | 0.3695 | 1400 | 0.0752 | 1169088 | | 0.1164 | 0.4223 | 1600 | 0.0692 | 1337088 | | 0.0527 | 0.4751 | 1800 | 0.0690 | 1505536 | | 0.0513 | 0.5279 | 2000 | 0.0648 | 1673024 | | 0.0544 | 0.5807 | 2200 | 0.0687 | 1842304 | | 0.0687 | 0.6335 | 2400 | 0.0648 | 2007328 | | 0.0739 | 0.6863 | 2600 | 0.0617 | 2174880 | | 0.1184 | 0.7391 | 2800 | 0.0758 | 2341280 | | 0.113 | 0.7919 | 3000 | 0.0676 | 2509440 | | 0.0589 | 0.8447 | 3200 | 0.0651 | 2674784 | | 0.0484 | 0.8975 | 3400 | 0.0629 | 2843680 | | 0.1224 | 0.9502 | 3600 | 0.0674 | 3011904 | | 0.0241 | 1.0029 | 3800 | 0.0719 | 3178064 | | 0.0495 | 1.0557 | 4000 | 0.0655 | 3345904 | | 0.0462 | 1.1085 | 4200 | 0.0620 | 3514608 | | 0.0457 | 1.1613 | 4400 | 0.0608 | 3680560 | | 0.0455 | 1.2141 | 4600 | 0.0638 | 3849328 | | 0.0395 | 1.2669 | 4800 | 0.0638 | 4017200 | | 0.037 | 1.3197 | 5000 | 0.0653 | 4187184 | | 0.0199 | 1.3724 | 5200 | 0.0640 | 4354416 | | 0.0455 | 1.4252 | 5400 | 0.0654 | 4519856 | | 0.0231 | 1.4780 | 5600 | 0.0632 | 4687280 | | 0.0568 | 1.5308 | 5800 | 0.0660 | 4856112 | | 0.0563 | 1.5836 | 6000 | 0.0604 | 5022736 | | 0.1588 | 1.6364 | 6200 | 0.0595 | 5188656 | | 0.0301 | 1.6892 | 6400 | 0.0716 | 5356208 | | 0.0572 | 1.7420 | 6600 | 0.0593 | 5523952 | | 0.0309 | 1.7948 | 6800 | 0.0615 | 5690672 | | 0.0549 | 1.8476 | 7000 | 0.0636 | 5857072 | | 0.0461 | 1.9004 | 7200 | 0.0662 | 6024976 | | 0.0753 | 1.9531 | 7400 | 0.0680 | 6191664 | | 0.0403 | 2.0058 | 7600 | 0.0632 | 6357472 | | 0.0261 | 2.0586 | 7800 | 0.0631 | 6525984 | | 0.0126 | 2.1114 | 8000 | 0.0692 | 6692320 | | 0.0039 | 2.1642 | 8200 | 0.0756 | 6860064 | | 0.0463 | 2.2170 | 8400 | 0.0737 | 7026528 | | 0.0118 | 2.2698 | 8600 | 0.0701 | 7192384 | | 0.0211 | 2.3226 | 8800 | 0.0734 | 7358816 | | 0.0369 | 2.3753 | 9000 | 0.0806 | 7526496 | | 0.0336 | 2.4281 | 9200 | 0.1007 | 7696064 | | 0.0252 | 2.4809 | 9400 | 0.0934 | 7863456 | | 0.0041 | 2.5337 | 9600 | 0.0711 | 8031776 | | 0.0297 | 2.5865 | 9800 | 0.0753 | 8199584 | | 0.0712 | 2.6393 | 10000 | 0.0741 | 8366016 | | 0.008 | 2.6921 | 10200 | 0.0924 | 8531808 | | 0.0324 | 2.7449 | 10400 | 0.0728 | 8702976 | | 0.0155 | 2.7977 | 10600 | 0.0793 | 8870944 | | 0.057 | 2.8505 | 10800 | 0.0790 | 9039680 | | 0.0472 | 2.9033 | 11000 | 0.0685 | 9206880 | | 0.0067 | 2.9561 | 11200 | 0.0759 | 9372128 | | 0.023 | 3.0087 | 11400 | 0.1036 | 9538768 | | 0.0009 | 3.0615 | 11600 | 0.1041 | 9705232 | | 0.0026 | 3.1143 | 11800 | 0.1420 | 9871632 | | 0.007 | 3.1671 | 12000 | 0.1002 | 10039472 | | 0.0023 | 3.2199 | 12200 | 0.1041 | 10206320 | | 0.003 | 3.2727 | 12400 | 0.1281 | 10376240 | | 0.0087 | 3.3255 | 12600 | 0.1209 | 10544464 | | 0.0146 | 3.3782 | 12800 | 0.1133 | 10712240 | | 0.0024 | 3.4310 | 13000 | 0.1138 | 10879120 | | 0.0002 | 3.4838 | 13200 | 0.1368 | 11045072 | | 0.0046 | 3.5366 | 13400 | 0.1290 | 11211312 | | 0.0263 | 3.5894 | 13600 | 0.1125 | 11378128 | | 0.0051 | 3.6422 | 13800 | 0.1019 | 11544592 | | 0.019 | 3.6950 | 14000 | 0.0911 | 11713040 | | 0.0066 | 3.7478 | 14200 | 0.1016 | 11880432 | | 0.0148 | 3.8006 | 14400 | 0.1121 | 12048176 | | 0.0001 | 3.8534 | 14600 | 0.1492 | 12215792 | | 0.0281 | 3.9062 | 14800 | 0.1034 | 12383792 | | 0.0056 | 3.9590 | 15000 | 0.0986 | 12549680 | | 0.0001 | 4.0116 | 15200 | 0.1283 | 12716448 | | 0.0037 | 4.0644 | 15400 | 0.2098 | 12882752 | | 0.0078 | 4.1172 | 15600 | 0.1637 | 13051200 | | 0.0078 | 4.1700 | 15800 | 0.1420 | 13217024 | | 0.0162 | 4.2228 | 16000 | 0.1648 | 13382784 | | 0.0003 | 4.2756 | 16200 | 0.1115 | 13549216 | | 0.0021 | 4.3284 | 16400 | 0.1300 | 13719072 | | 0.0028 | 4.3812 | 16600 | 0.1370 | 13884928 | | 0.0028 | 4.4339 | 16800 | 0.1263 | 14051584 | | 0.0001 | 4.4867 | 17000 | 0.1414 | 14220704 | | 0.0038 | 4.5395 | 17200 | 0.1233 | 14387008 | | 0.0052 | 4.5923 | 17400 | 0.2157 | 14555808 | | 0.0 | 4.6451 | 17600 | 0.2525 | 14723456 | | 0.0196 | 4.6979 | 17800 | 0.1433 | 14890880 | | 0.0229 | 4.7507 | 18000 | 0.1838 | 15059744 | | 0.057 | 4.8035 | 18200 | 0.1577 | 15224512 | | 0.0453 | 4.8563 | 18400 | 0.1232 | 15392960 | | 0.0066 | 4.9091 | 18600 | 0.1784 | 15561696 | | 0.0162 | 4.9619 | 18800 | 0.1757 | 15728800 | | 0.0485 | 5.0145 | 19000 | 0.1900 | 15897552 | | 0.0009 | 5.0673 | 19200 | 0.1809 | 16064688 | | 0.0005 | 5.1201 | 19400 | 0.1684 | 16231120 | | 0.0044 | 5.1729 | 19600 | 0.1712 | 16397744 | | 0.1072 | 5.2257 | 19800 | 0.1722 | 16564176 | | 0.037 | 5.2785 | 20000 | 0.1688 | 16731600 | | 0.0052 | 5.3313 | 20200 | 0.1799 | 16898064 | | 0.0 | 5.3841 | 20400 | 0.1782 | 17064080 | | 0.0002 | 5.4368 | 20600 | 0.2476 | 17231888 | | 0.0057 | 5.4896 | 20800 | 0.1952 | 17399184 | | 0.0022 | 5.5424 | 21000 | 0.2121 | 17566160 | | 0.0063 | 5.5952 | 21200 | 0.1922 | 17732304 | | 0.004 | 5.6480 | 21400 | 0.1764 | 17900880 | | 0.0001 | 5.7008 | 21600 | 0.1741 | 18070192 | | 0.0001 | 5.7536 | 21800 | 0.1708 | 18237168 | | 0.0058 | 5.8064 | 22000 | 0.2006 | 18403856 | | 0.002 | 5.8592 | 22200 | 0.2176 | 18571248 | | 0.0 | 5.9120 | 22400 | 0.2260 | 18738672 | | 0.0002 | 5.9648 | 22600 | 0.1856 | 18905744 | | 0.0 | 6.0174 | 22800 | 0.2380 | 19073440 | | 0.0 | 6.0702 | 23000 | 0.1898 | 19241920 | | 0.0036 | 6.1230 | 23200 | 0.1885 | 19409408 | | 0.0 | 6.1758 | 23400 | 0.1937 | 19577024 | | 0.0532 | 6.2286 | 23600 | 0.1851 | 19744608 | | 0.0036 | 6.2814 | 23800 | 0.1758 | 19911488 | | 0.0001 | 6.3342 | 24000 | 0.2023 | 20078944 | | 0.0 | 6.3870 | 24200 | 0.2283 | 20244928 | | 0.0107 | 6.4398 | 24400 | 0.1919 | 20411232 | | 0.0 | 6.4925 | 24600 | 0.2069 | 20578080 | | 0.0002 | 6.5453 | 24800 | 0.1839 | 20746592 | | 0.0 | 6.5981 | 25000 | 0.1915 | 20913344 | | 0.0 | 6.6509 | 25200 | 0.2142 | 21081952 | | 0.0051 | 6.7037 | 25400 | 0.1797 | 21248384 | | 0.0027 | 6.7565 | 25600 | 0.1834 | 21415872 | | 0.0004 | 6.8093 | 25800 | 0.1750 | 21584000 | | 0.0004 | 6.8621 | 26000 | 0.2093 | 21751168 | | 0.0 | 6.9149 | 26200 | 0.1849 | 21918816 | | 0.0291 | 6.9677 | 26400 | 0.1955 | 22084384 | | 0.0001 | 7.0203 | 26600 | 0.2036 | 22251776 | | 0.0 | 7.0731 | 26800 | 0.2368 | 22418080 | | 0.0 | 7.1259 | 27000 | 0.2574 | 22587392 | | 0.0 | 7.1787 | 27200 | 0.2346 | 22753056 | | 0.0 | 7.2315 | 27400 | 0.2273 | 22920768 | | 0.0 | 7.2843 | 27600 | 0.2511 | 23087296 | | 0.0 | 7.3371 | 27800 | 0.2544 | 23254400 | | 0.0 | 7.3899 | 28000 | 0.2559 | 23422752 | | 0.0 | 7.4427 | 28200 | 0.2674 | 23588352 | | 0.0055 | 7.4954 | 28400 | 0.2443 | 23755840 | | 0.0001 | 7.5482 | 28600 | 0.2886 | 23923680 | | 0.0 | 7.6010 | 28800 | 0.3007 | 24091168 | | 0.0 | 7.6538 | 29000 | 0.2789 | 24258016 | | 0.0 | 7.7066 | 29200 | 0.2825 | 24427808 | | 0.0 | 7.7594 | 29400 | 0.2882 | 24596288 | | 0.0 | 7.8122 | 29600 | 0.2831 | 24764192 | | 0.0 | 7.8650 | 29800 | 0.2660 | 24932000 | | 0.0 | 7.9178 | 30000 | 0.2519 | 25100224 | | 0.0229 | 7.9706 | 30200 | 0.2555 | 25267808 | | 0.0 | 8.0232 | 30400 | 0.2564 | 25433440 | | 0.0 | 8.0760 | 30600 | 0.2685 | 25600672 | | 0.0 | 8.1288 | 30800 | 0.2806 | 25769408 | | 0.0 | 8.1816 | 31000 | 0.2828 | 25936160 | | 0.0 | 8.2344 | 31200 | 0.2733 | 26103744 | | 0.0 | 8.2872 | 31400 | 0.2767 | 26270560 | | 0.0 | 8.3400 | 31600 | 0.2765 | 26437536 | | 0.0 | 8.3928 | 31800 | 0.2866 | 26604480 | | 0.0 | 8.4456 | 32000 | 0.2873 | 26771680 | | 0.0 | 8.4984 | 32200 | 0.2827 | 26940256 | | 0.0 | 8.5511 | 32400 | 0.2836 | 27107680 | | 0.0 | 8.6039 | 32600 | 0.2861 | 27274048 | | 0.0 | 8.6567 | 32800 | 0.2895 | 27440544 | | 0.0 | 8.7095 | 33000 | 0.2866 | 27608000 | | 0.0 | 8.7623 | 33200 | 0.2908 | 27776704 | | 0.0 | 8.8151 | 33400 | 0.2903 | 27942752 | | 0.0 | 8.8679 | 33600 | 0.2963 | 28108864 | | 0.0 | 8.9207 | 33800 | 0.2992 | 28275296 | | 0.0 | 8.9735 | 34000 | 0.2939 | 28443520 | | 0.0 | 9.0261 | 34200 | 0.3001 | 28609776 | | 0.0 | 9.0789 | 34400 | 0.3015 | 28777712 | | 0.0 | 9.1317 | 34600 | 0.3028 | 28944144 | | 0.0 | 9.1845 | 34800 | 0.3033 | 29111152 | | 0.0002 | 9.2373 | 35000 | 0.2992 | 29278000 | | 0.0 | 9.2901 | 35200 | 0.2962 | 29443792 | | 0.0 | 9.3429 | 35400 | 0.2975 | 29609072 | | 0.0 | 9.3957 | 35600 | 0.2983 | 29776592 | | 0.0 | 9.4485 | 35800 | 0.2987 | 29941616 | | 0.0 | 9.5013 | 36000 | 0.2990 | 30110160 | | 0.0 | 9.5540 | 36200 | 0.2988 | 30277744 | | 0.0 | 9.6068 | 36400 | 0.2996 | 30447152 | | 0.0 | 9.6596 | 36600 | 0.3006 | 30612976 | | 0.0 | 9.7124 | 36800 | 0.3007 | 30780240 | | 0.0 | 9.7652 | 37000 | 0.3018 | 30948048 | | 0.0 | 9.8180 | 37200 | 0.3031 | 31116368 | | 0.0 | 9.8708 | 37400 | 0.3022 | 31283888 | | 0.0 | 9.9236 | 37600 | 0.3025 | 31452560 | | 0.0 | 9.9764 | 37800 | 0.3033 | 31620720 | | 0.0 | 10.0290 | 38000 | 0.3030 | 31786016 | | 0.0 | 10.0818 | 38200 | 0.3018 | 31952768 | | 0.0 | 10.1346 | 38400 | 0.3032 | 32120320 | | 0.0 | 10.1874 | 38600 | 0.3024 | 32287584 | | 0.0 | 10.2402 | 38800 | 0.3030 | 32455072 | | 0.0 | 10.2930 | 39000 | 0.3025 | 32621184 | | 0.0 | 10.3458 | 39200 | 0.3031 | 32788960 | | 0.0 | 10.3986 | 39400 | 0.3028 | 32955776 | | 0.0 | 10.4514 | 39600 | 0.3024 | 33122816 | | 0.0 | 10.5042 | 39800 | 0.3032 | 33291072 | | 0.0 | 10.5569 | 40000 | 0.3027 | 33458560 | ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

swarup3204/byt5-sanskrit-original-data-ft

swarup3204

"2025-04-19T11:47:47Z"

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "sa", "arxiv:1910.09700", "base_model:chronbmm/sanskrit5-multitask", "base_model:finetune:chronbmm/sanskrit5-multitask", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

"2025-04-19T07:46:59Z"

--- library_name: transformers license: apache-2.0 language: - sa base_model: - chronbmm/sanskrit5-multitask --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Beinsezii/Mistral-Small-24B-Instruct-2501-Q6_K_C-GGUF

Beinsezii

"2025-04-19T11:47:22Z"

0

null

[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T11:16:34Z"

--- license: apache-2.0 --- https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501 Q6_K_C: Q6_K weights, copied output, copied embed Fits 24K CTX on a 24GiB GPU

Dans-DiscountModels/7b-m-dans-optimizersweeps-repremover-1-ademamix-hi-lr-b1_0.9-b2_0.999-b3_0.999-a10

Dans-DiscountModels

"2025-04-19T11:45:52Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "axolotl", "generated_from_trainer", "conversational", "dataset:Dans-DiscountModels/pretokenization-test-3", "base_model:Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2", "base_model:finetune:Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T11:12:01Z"

--- library_name: transformers base_model: Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2 tags: - axolotl - generated_from_trainer datasets: - Dans-DiscountModels/pretokenization-test-3 model-index: - name: 7b-m-dans-optimizersweeps-repremover-1-ademamix-hi-lr-b1_0.9-b2_0.999-b3_0.999-a10 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.8.0` ```yaml base_model: Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer trust_remote_code: # wandb configuration wandb_project: 7b-m-dans-optimizersweeps wandb_watch: wandb_run_id: repremover-1-1-ademamix-hi-lr-b1_0.9-b2_0.999-b3_0.999-a10 wandb_log_model: # push checkpoints to hub hub_model_id: Dans-DiscountModels/7b-m-dans-optimizersweeps-repremover-1-ademamix-hi-lr-b1_0.9-b2_0.999-b3_0.999-a10 # how to push checkpoints to hub # https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy hub_strategy: "every_save" # Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets # Required to be true when used in combination with `push_dataset_to_hub` hf_use_auth_token: true # where to save the finished model to output_dir: ./7b-m-dans-optimizersweeps # where to save the dataset to dataset_prepared_path: ./7b-m-dans-optimizersweeps-data save_safetensors: true # dataset settings (local or huggingface repo) datasets: - path: Dans-DiscountModels/pretokenization-test-3 ds_type: parquet type: plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true liger_fused_linear_cross_entropy: false cut_cross_entropy: true load_in_8bit: false load_in_4bit: false strict: false adapter: lora_model_dir: val_set_size: 0.01 sequence_len: 8192 sample_packing: false eval_sample_packing: false pad_to_sequence_len: true gradient_checkpointing: true # gradient_checkpointing_kwargs: # use_reentrant: false gradient_accumulation_steps: 1 micro_batch_size: 4 num_epochs: 3 optimizer: ademamix optim_args: "beta1=0.9,beta2=0.999,beta3=0.999,alpha=10" lr_scheduler: rex learning_rate: 0.0000003 cosine_min_lr_ratio: # weight_decay: 0.03 max_grad_norm: 0.001 train_on_inputs: false group_by_length: true bf16: true fp16: false tf32: false early_stopping_patience: resume_from_checkpoint: auto_resume_from_checkpoints: false local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_ratio: 0.1 evals_per_epoch: 24 eval_table_size: eval_max_new_tokens: saves_per_epoch: 1 save_total_limit: 2 debug: false deepspeed: deepspeed_configs/zero3_bf16.json fsdp: fsdp_config: special_tokens: ``` </details><br> # 7b-m-dans-optimizersweeps-repremover-1-ademamix-hi-lr-b1_0.9-b2_0.999-b3_0.999-a10 This model is a fine-tuned version of [Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2](https://huggingface.co/Dans-DiscountModels/7b-m-dans-personalityengine-v1.2.1-rc-2) on the Dans-DiscountModels/pretokenization-test-3 dataset. It achieves the following results on the evaluation set: - Loss: 2.0850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-07 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Use ademamix and the args are: beta1=0.9,beta2=0.999,beta3=0.999,alpha=10 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 41 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.0376 | 0.0072 | 1 | 2.1457 | | 2.2661 | 0.0432 | 6 | 2.1208 | | 2.307 | 0.0863 | 12 | 2.1567 | | 2.1831 | 0.1295 | 18 | 2.1175 | | 2.2321 | 0.1727 | 24 | 2.1267 | | 2.0512 | 0.2158 | 30 | 2.1440 | | 2.1275 | 0.2590 | 36 | 2.1177 | | 2.0276 | 0.3022 | 42 | 2.1115 | | 2.0803 | 0.3453 | 48 | 2.1289 | | 2.1525 | 0.3885 | 54 | 2.1259 | | 2.0461 | 0.4317 | 60 | 2.1080 | | 2.0416 | 0.4748 | 66 | 2.1281 | | 2.2091 | 0.5180 | 72 | 2.1134 | | 2.1002 | 0.5612 | 78 | 2.1163 | | 2.0207 | 0.6043 | 84 | 2.1346 | | 2.1418 | 0.6475 | 90 | 2.1178 | | 2.0907 | 0.6906 | 96 | 2.1061 | | 2.1079 | 0.7338 | 102 | 2.1228 | | 2.0733 | 0.7770 | 108 | 2.1221 | | 2.0229 | 0.8201 | 114 | 2.1011 | | 2.0239 | 0.8633 | 120 | 2.1111 | | 1.9952 | 0.9065 | 126 | 2.1107 | | 2.1515 | 0.9496 | 132 | 2.0999 | | 1.9878 | 0.9928 | 138 | 2.1050 | | 2.0482 | 1.0360 | 144 | 2.1151 | | 1.9203 | 1.0791 | 150 | 2.0964 | | 2.0638 | 1.1223 | 156 | 2.1202 | | 1.9855 | 1.1655 | 162 | 2.1308 | | 1.9788 | 1.2086 | 168 | 2.1189 | | 1.9651 | 1.2518 | 174 | 2.1124 | | 1.9656 | 1.2950 | 180 | 2.1234 | | 2.0319 | 1.3381 | 186 | 2.1157 | | 2.0527 | 1.3813 | 192 | 2.1175 | | 2.0895 | 1.4245 | 198 | 2.1198 | | 1.9853 | 1.4676 | 204 | 2.1186 | | 2.0482 | 1.5108 | 210 | 2.1123 | | 1.892 | 1.5540 | 216 | 2.1013 | | 2.0457 | 1.5971 | 222 | 2.1133 | | 1.9954 | 1.6403 | 228 | 2.1084 | | 1.9719 | 1.6835 | 234 | 2.1045 | | 2.0459 | 1.7266 | 240 | 2.1159 | | 1.9969 | 1.7698 | 246 | 2.1020 | | 1.9273 | 1.8129 | 252 | 2.1154 | | 1.9269 | 1.8561 | 258 | 2.1231 | | 1.9751 | 1.8993 | 264 | 2.1132 | | 1.9338 | 1.9424 | 270 | 2.0767 | | 1.9924 | 1.9856 | 276 | 2.1092 | | 1.9114 | 2.0288 | 282 | 2.1149 | | 1.9014 | 2.0719 | 288 | 2.1025 | | 1.9959 | 2.1151 | 294 | 2.0986 | | 1.9145 | 2.1583 | 300 | 2.1133 | | 1.8756 | 2.2014 | 306 | 2.1224 | | 1.8999 | 2.2446 | 312 | 2.1034 | | 1.963 | 2.2878 | 318 | 2.1198 | | 1.9189 | 2.3309 | 324 | 2.1308 | | 1.9539 | 2.3741 | 330 | 2.1069 | | 1.9463 | 2.4173 | 336 | 2.1014 | | 1.9892 | 2.4604 | 342 | 2.1129 | | 1.9526 | 2.5036 | 348 | 2.1019 | | 2.0455 | 2.5468 | 354 | 2.1284 | | 1.9248 | 2.5899 | 360 | 2.1191 | | 1.8867 | 2.6331 | 366 | 2.0985 | | 1.7824 | 2.6763 | 372 | 2.1137 | | 1.6577 | 2.7194 | 378 | 2.0967 | | 1.7822 | 2.7626 | 384 | 2.0938 | | 1.84 | 2.8058 | 390 | 2.1322 | | 1.8023 | 2.8489 | 396 | 2.0898 | | 1.8613 | 2.8921 | 402 | 2.1231 | | 1.7858 | 2.9353 | 408 | 2.1254 | | 1.7629 | 2.9784 | 414 | 2.0850 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

rbelanec/train_mrpc_1744902649

rbelanec

"2025-04-19T11:45:31Z"

0

peft

[ "peft", "safetensors", "llama-factory", "lntuning", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]

null

"2025-04-19T04:29:20Z"

--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - llama-factory - lntuning - generated_from_trainer model-index: - name: train_mrpc_1744902649 results: [] ---  # train_mrpc_1744902649 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the mrpc dataset. It achieves the following results on the evaluation set: - Loss: 0.1272 - Num Input Tokens Seen: 65784064 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:--------:|:-----:|:---------------:|:-----------------:| | 0.1792 | 0.9685 | 200 | 0.1595 | 329312 | | 0.1616 | 1.9395 | 400 | 0.1593 | 658560 | | 0.1105 | 2.9104 | 600 | 0.1366 | 987040 | | 0.093 | 3.8814 | 800 | 0.1343 | 1316448 | | 0.1363 | 4.8523 | 1000 | 0.1369 | 1644608 | | 0.1061 | 5.8232 | 1200 | 0.1310 | 1974016 | | 0.1695 | 6.7942 | 1400 | 0.1355 | 2303584 | | 0.0548 | 7.7651 | 1600 | 0.1272 | 2630688 | | 0.1025 | 8.7361 | 1800 | 0.1282 | 2959808 | | 0.1103 | 9.7070 | 2000 | 0.1275 | 3287584 | | 0.073 | 10.6780 | 2200 | 0.1276 | 3617920 | | 0.0808 | 11.6489 | 2400 | 0.1375 | 3945536 | | 0.0832 | 12.6199 | 2600 | 0.1372 | 4274560 | | 0.043 | 13.5908 | 2800 | 0.1441 | 4603168 | | 0.1192 | 14.5617 | 3000 | 0.1549 | 4932448 | | 0.0522 | 15.5327 | 3200 | 0.1576 | 5261312 | | 0.0495 | 16.5036 | 3400 | 0.1620 | 5589632 | | 0.0463 | 17.4746 | 3600 | 0.1867 | 5918112 | | 0.0316 | 18.4455 | 3800 | 0.2041 | 6246368 | | 0.0302 | 19.4165 | 4000 | 0.2137 | 6574848 | | 0.0489 | 20.3874 | 4200 | 0.2474 | 6903520 | | 0.0428 | 21.3584 | 4400 | 0.2885 | 7231904 | | 0.0092 | 22.3293 | 4600 | 0.2955 | 7561504 | | 0.0247 | 23.3002 | 4800 | 0.3410 | 7890912 | | 0.0108 | 24.2712 | 5000 | 0.3752 | 8218592 | | 0.003 | 25.2421 | 5200 | 0.3848 | 8548256 | | 0.0001 | 26.2131 | 5400 | 0.4551 | 8876704 | | 0.0038 | 27.1840 | 5600 | 0.5005 | 9206272 | | 0.0011 | 28.1550 | 5800 | 0.5081 | 9534720 | | 0.0186 | 29.1259 | 6000 | 0.5632 | 9864384 | | 0.0001 | 30.0969 | 6200 | 0.6207 | 10193376 | | 0.0001 | 31.0678 | 6400 | 0.6485 | 10521952 | | 0.0001 | 32.0387 | 6600 | 0.6780 | 10851520 | | 0.0 | 33.0097 | 6800 | 0.7211 | 11180544 | | 0.0001 | 33.9782 | 7000 | 0.7425 | 11509344 | | 0.0 | 34.9492 | 7200 | 0.7592 | 11838208 | | 0.0022 | 35.9201 | 7400 | 0.7934 | 12167872 | | 0.0 | 36.8910 | 7600 | 0.7808 | 12496352 | | 0.0 | 37.8620 | 7800 | 0.8029 | 12826048 | | 0.0 | 38.8329 | 8000 | 0.8225 | 13155040 | | 0.0003 | 39.8039 | 8200 | 0.8472 | 13483008 | | 0.0 | 40.7748 | 8400 | 0.8385 | 13812064 | | 0.0 | 41.7458 | 8600 | 0.9110 | 14140576 | | 0.0001 | 42.7167 | 8800 | 0.8721 | 14469248 | | 0.0006 | 43.6877 | 9000 | 0.8889 | 14796672 | | 0.0002 | 44.6586 | 9200 | 0.8948 | 15126752 | | 0.0042 | 45.6295 | 9400 | 0.9480 | 15456160 | | 0.0006 | 46.6005 | 9600 | 0.8594 | 15784928 | | 0.0 | 47.5714 | 9800 | 0.9047 | 16113248 | | 0.0 | 48.5424 | 10000 | 0.8897 | 16442496 | | 0.0 | 49.5133 | 10200 | 0.8949 | 16772640 | | 0.0 | 50.4843 | 10400 | 0.8780 | 17100000 | | 0.0 | 51.4552 | 10600 | 0.8463 | 17428768 | | 0.0 | 52.4262 | 10800 | 0.8947 | 17757344 | | 0.0 | 53.3971 | 11000 | 0.8721 | 18085920 | | 0.0 | 54.3680 | 11200 | 0.8789 | 18414336 | | 0.0 | 55.3390 | 11400 | 0.8813 | 18743040 | | 0.0 | 56.3099 | 11600 | 0.8915 | 19072928 | | 0.0 | 57.2809 | 11800 | 0.8866 | 19401376 | | 0.0 | 58.2518 | 12000 | 0.8915 | 19730336 | | 0.0 | 59.2228 | 12200 | 0.8939 | 20059488 | | 0.0 | 60.1937 | 12400 | 0.8958 | 20388064 | | 0.0 | 61.1646 | 12600 | 0.8991 | 20718144 | | 0.0 | 62.1356 | 12800 | 0.9055 | 21048224 | | 0.0 | 63.1065 | 13000 | 0.9546 | 21376576 | | 0.0029 | 64.0775 | 13200 | 0.9045 | 21706080 | | 0.0 | 65.0484 | 13400 | 0.9358 | 22034624 | | 0.0 | 66.0194 | 13600 | 0.8919 | 22364128 | | 0.0 | 66.9879 | 13800 | 0.8877 | 22692352 | | 0.0101 | 67.9588 | 14000 | 0.8636 | 23020864 | | 0.0 | 68.9298 | 14200 | 0.9585 | 23349920 | | 0.0 | 69.9007 | 14400 | 0.8971 | 23679072 | | 0.0 | 70.8717 | 14600 | 0.8881 | 24007776 | | 0.0 | 71.8426 | 14800 | 0.9130 | 24336640 | | 0.0 | 72.8136 | 15000 | 0.9017 | 24664576 | | 0.0 | 73.7845 | 15200 | 0.9239 | 24994848 | | 0.0 | 74.7554 | 15400 | 0.9034 | 25322720 | | 0.0 | 75.7264 | 15600 | 0.9104 | 25650784 | | 0.0013 | 76.6973 | 15800 | 0.9375 | 25980512 | | 0.0007 | 77.6683 | 16000 | 0.9748 | 26309536 | | 0.0 | 78.6392 | 16200 | 0.9272 | 26638944 | | 0.0 | 79.6102 | 16400 | 0.9310 | 26967360 | | 0.0 | 80.5811 | 16600 | 0.9371 | 27297120 | | 0.0 | 81.5521 | 16800 | 0.9354 | 27626144 | | 0.0 | 82.5230 | 17000 | 0.9427 | 27954656 | | 0.0 | 83.4939 | 17200 | 0.9468 | 28284160 | | 0.0 | 84.4649 | 17400 | 0.9542 | 28612224 | | 0.0 | 85.4358 | 17600 | 0.9413 | 28940448 | | 0.0 | 86.4068 | 17800 | 0.9482 | 29270912 | | 0.0 | 87.3777 | 18000 | 0.9457 | 29599424 | | 0.0 | 88.3487 | 18200 | 0.9610 | 29929280 | | 0.0 | 89.3196 | 18400 | 0.9695 | 30257504 | | 0.0 | 90.2906 | 18600 | 0.9427 | 30586944 | | 0.0 | 91.2615 | 18800 | 0.9648 | 30915744 | | 0.0 | 92.2324 | 19000 | 0.9566 | 31245216 | | 0.0 | 93.2034 | 19200 | 1.0410 | 31573600 | | 0.0 | 94.1743 | 19400 | 1.0290 | 31903616 | | 0.0 | 95.1453 | 19600 | 1.0224 | 32232032 | | 0.0 | 96.1162 | 19800 | 1.0002 | 32560480 | | 0.0 | 97.0872 | 20000 | 1.0333 | 32889696 | | 0.0 | 98.0581 | 20200 | 0.9999 | 33218016 | | 0.0 | 99.0291 | 20400 | 1.0188 | 33547296 | | 0.0 | 99.9976 | 20600 | 1.0259 | 33876000 | | 0.0 | 100.9685 | 20800 | 1.0148 | 34205376 | | 0.0 | 101.9395 | 21000 | 1.0062 | 34534496 | | 0.0 | 102.9104 | 21200 | 0.9976 | 34864000 | | 0.0 | 103.8814 | 21400 | 1.0242 | 35192256 | | 0.0 | 104.8523 | 21600 | 1.0044 | 35521376 | | 0.0 | 105.8232 | 21800 | 1.0179 | 35851264 | | 0.0 | 106.7942 | 22000 | 1.0085 | 36180000 | | 0.0 | 107.7651 | 22200 | 1.0040 | 36508832 | | 0.0 | 108.7361 | 22400 | 1.0053 | 36837600 | | 0.0 | 109.7070 | 22600 | 0.9748 | 37166720 | | 0.0 | 110.6780 | 22800 | 1.0201 | 37495520 | | 0.0 | 111.6489 | 23000 | 1.0137 | 37824352 | | 0.0 | 112.6199 | 23200 | 1.0274 | 38153856 | | 0.0 | 113.5908 | 23400 | 1.0198 | 38483200 | | 0.0 | 114.5617 | 23600 | 1.0236 | 38812672 | | 0.0 | 115.5327 | 23800 | 1.0075 | 39142400 | | 0.0 | 116.5036 | 24000 | 1.0092 | 39471200 | | 0.0 | 117.4746 | 24200 | 1.0208 | 39798848 | | 0.0 | 118.4455 | 24400 | 1.0163 | 40127360 | | 0.0 | 119.4165 | 24600 | 1.0297 | 40456736 | | 0.0 | 120.3874 | 24800 | 1.0208 | 40785312 | | 0.0 | 121.3584 | 25000 | 1.0032 | 41112576 | | 0.0 | 122.3293 | 25200 | 1.0071 | 41442112 | | 0.0 | 123.3002 | 25400 | 1.0182 | 41771552 | | 0.0 | 124.2712 | 25600 | 1.0241 | 42101248 | | 0.0 | 125.2421 | 25800 | 0.9986 | 42427392 | | 0.0 | 126.2131 | 26000 | 1.0178 | 42756704 | | 0.0 | 127.1840 | 26200 | 1.0377 | 43085664 | | 0.0 | 128.1550 | 26400 | 1.0162 | 43414240 | | 0.0 | 129.1259 | 26600 | 1.0307 | 43743072 | | 0.0 | 130.0969 | 26800 | 1.0224 | 44072768 | | 0.0 | 131.0678 | 27000 | 1.0235 | 44400192 | | 0.0 | 132.0387 | 27200 | 1.0353 | 44729632 | | 0.0 | 133.0097 | 27400 | 1.0296 | 45058976 | | 0.0 | 133.9782 | 27600 | 1.0324 | 45388352 | | 0.0 | 134.9492 | 27800 | 1.0443 | 45717952 | | 0.0 | 135.9201 | 28000 | 1.0478 | 46046144 | | 0.0 | 136.8910 | 28200 | 1.0435 | 46375168 | | 0.0 | 137.8620 | 28400 | 1.0442 | 46702816 | | 0.0 | 138.8329 | 28600 | 1.0448 | 47033152 | | 0.0 | 139.8039 | 28800 | 1.0729 | 47361472 | | 0.0 | 140.7748 | 29000 | 1.0439 | 47691424 | | 0.0 | 141.7458 | 29200 | 1.0689 | 48019712 | | 0.0 | 142.7167 | 29400 | 1.0791 | 48348832 | | 0.0 | 143.6877 | 29600 | 1.0849 | 48678560 | | 0.0 | 144.6586 | 29800 | 1.0461 | 49008256 | | 0.0 | 145.6295 | 30000 | 1.0701 | 49337088 | | 0.0 | 146.6005 | 30200 | 1.0699 | 49665344 | | 0.0 | 147.5714 | 30400 | 1.0625 | 49996128 | | 0.0 | 148.5424 | 30600 | 1.0711 | 50324736 | | 0.0 | 149.5133 | 30800 | 1.0653 | 50652864 | | 0.0 | 150.4843 | 31000 | 1.0867 | 50981920 | | 0.0 | 151.4552 | 31200 | 1.0732 | 51310752 | | 0.0 | 152.4262 | 31400 | 1.0587 | 51640352 | | 0.0 | 153.3971 | 31600 | 1.0614 | 51969184 | | 0.0 | 154.3680 | 31800 | 1.0761 | 52297280 | | 0.0 | 155.3390 | 32000 | 1.0690 | 52625600 | | 0.0 | 156.3099 | 32200 | 1.0777 | 52953920 | | 0.0 | 157.2809 | 32400 | 1.0818 | 53283648 | | 0.0 | 158.2518 | 32600 | 1.0866 | 53613056 | | 0.0 | 159.2228 | 32800 | 1.0812 | 53941632 | | 0.0 | 160.1937 | 33000 | 1.0887 | 54270272 | | 0.0 | 161.1646 | 33200 | 1.0782 | 54599104 | | 0.0 | 162.1356 | 33400 | 1.0808 | 54929056 | | 0.0 | 163.1065 | 33600 | 1.0965 | 55257728 | | 0.0 | 164.0775 | 33800 | 1.0854 | 55587456 | | 0.0 | 165.0484 | 34000 | 1.0979 | 55916576 | | 0.0 | 166.0194 | 34200 | 1.0962 | 56245664 | | 0.0 | 166.9879 | 34400 | 1.1092 | 56574272 | | 0.0 | 167.9588 | 34600 | 1.1052 | 56903360 | | 0.0 | 168.9298 | 34800 | 1.1229 | 57232032 | | 0.0 | 169.9007 | 35000 | 1.0853 | 57561504 | | 0.0 | 170.8717 | 35200 | 1.1070 | 57891168 | | 0.0 | 171.8426 | 35400 | 1.1014 | 58220352 | | 0.0 | 172.8136 | 35600 | 1.1065 | 58548960 | | 0.0 | 173.7845 | 35800 | 1.0964 | 58878688 | | 0.0 | 174.7554 | 36000 | 1.0980 | 59207104 | | 0.0 | 175.7264 | 36200 | 1.1023 | 59536800 | | 0.0 | 176.6973 | 36400 | 1.0831 | 59865312 | | 0.0 | 177.6683 | 36600 | 1.0948 | 60194816 | | 0.0 | 178.6392 | 36800 | 1.1205 | 60523584 | | 0.0 | 179.6102 | 37000 | 1.0883 | 60852352 | | 0.0 | 180.5811 | 37200 | 1.0916 | 61181024 | | 0.0 | 181.5521 | 37400 | 1.1090 | 61510624 | | 0.0 | 182.5230 | 37600 | 1.1083 | 61840672 | | 0.0 | 183.4939 | 37800 | 1.1169 | 62167808 | | 0.0 | 184.4649 | 38000 | 1.1141 | 62496960 | | 0.0 | 185.4358 | 38200 | 1.0932 | 62826016 | | 0.0 | 186.4068 | 38400 | 1.1050 | 63154784 | | 0.0 | 187.3777 | 38600 | 1.0873 | 63483904 | | 0.0 | 188.3487 | 38800 | 1.1244 | 63811808 | | 0.0 | 189.3196 | 39000 | 1.1015 | 64139488 | | 0.0 | 190.2906 | 39200 | 1.0930 | 64467808 | | 0.0 | 191.2615 | 39400 | 1.0899 | 64798112 | | 0.0 | 192.2324 | 39600 | 1.0952 | 65126304 | | 0.0 | 193.2034 | 39800 | 1.1142 | 65455776 | | 0.0 | 194.1743 | 40000 | 1.1018 | 65784064 | ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

mlx-community/InternVL3-8B-bf16

mlx-community

"2025-04-19T11:43:56Z"

4

0

transformers

[ "transformers", "safetensors", "internvl_chat", "feature-extraction", "internvl", "custom_code", "mlx", "image-text-to-text", "conversational", "multilingual", "dataset:OpenGVLab/MMPR-v1.2", "base_model:OpenGVLab/InternVL3-1B-Instruct", "base_model:finetune:OpenGVLab/InternVL3-1B-Instruct", "license:other", "region:us" ]

image-text-to-text

"2025-04-18T12:20:43Z"

--- license: other license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE pipeline_tag: image-text-to-text library_name: transformers base_model: - OpenGVLab/InternVL3-1B-Instruct base_model_relation: finetune datasets: - OpenGVLab/MMPR-v1.2 language: - multilingual tags: - internvl - custom_code - mlx --- # mlx-community/InternVL3-8B-bf16 This model was converted to MLX format from [`models/InternVL3-8B`]() using mlx-vlm version **0.1.23**. Refer to the [original model card](https://huggingface.co/models/InternVL3-8B) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/InternVL3-8B-bf16 --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image> ```

warmachine68/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-solitary_zealous_cat

warmachine68

"2025-04-19T11:42:54Z"

4

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am solitary zealous cat", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-08T22:38:25Z"

--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-solitary_zealous_cat tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am solitary zealous cat - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-solitary_zealous_cat This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="warmachine68/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-solitary_zealous_cat", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.1 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF

mradermacher

"2025-04-19T11:41:26Z"

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B", "base_model:quantized:grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2025-04-19T10:55:02Z"

--- base_model: grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About      weighted/imatrix quants of https://huggingface.co/grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B  static quants are available at https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.2 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q4_1.gguf) | i1-Q4_1 | 4.7 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF

mradermacher

"2025-04-19T11:38:07Z"

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B", "base_model:quantized:grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T09:00:09Z"

--- base_model: grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/grimjim/kunoichi-lemon-royale-v2experiment2-32K-7B  weighted/imatrix quants are available at https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2experiment2-32K-7B-GGUF/resolve/main/kunoichi-lemon-royale-v2experiment2-32K-7B.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/teapotllm-chat-GGUF

mradermacher

"2025-04-19T11:36:48Z"

0

transformers

[ "transformers", "gguf", "en", "base_model:teapotai/teapotllm-chat", "base_model:quantized:teapotai/teapotllm-chat", "license:mit", "endpoints_compatible", "region:us" ]

null

"2025-04-19T11:30:30Z"

--- base_model: teapotai/teapotllm-chat language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/teapotai/teapotllm-chat  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q2_K.gguf) | Q2_K | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q3_K_S.gguf) | Q3_K_S | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q3_K_L.gguf) | Q3_K_L | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.IQ4_XS.gguf) | IQ4_XS | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q4_K_S.gguf) | Q4_K_S | 0.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q4_K_M.gguf) | Q4_K_M | 0.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q5_K_S.gguf) | Q5_K_S | 0.6 | | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q5_K_M.gguf) | Q5_K_M | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q6_K.gguf) | Q6_K | 0.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.Q8_0.gguf) | Q8_0 | 0.9 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/teapotllm-chat-GGUF/resolve/main/teapotllm-chat.f16.gguf) | f16 | 1.7 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

fedovtt/a1097dee-9584-49fe-8602-d38eff229ef6

fedovtt

"2025-04-19T11:35:05Z"

0

peft

[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]

null

"2025-04-19T10:02:03Z"

--- library_name: peft license: apache-2.0 base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B tags: - axolotl - generated_from_trainer model-index: - name: a1097dee-9584-49fe-8602-d38eff229ef6 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 33ca191ef861beb8_train_data.json ds_type: json format: custom path: /workspace/input_data/33ca191ef861beb8_train_data.json type: field_instruction: premise field_output: hypothesis format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: fedovtt/a1097dee-9584-49fe-8602-d38eff229ef6 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/33ca191ef861beb8_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 36fecfe5-f91d-46c3-aeef-47b486bafbe0 wandb_project: 01-31 wandb_run: your_name wandb_runid: 36fecfe5-f91d-46c3-aeef-47b486bafbe0 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # a1097dee-9584-49fe-8602-d38eff229ef6 This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0 | 0.0082 | 200 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

jethrowang/whisper-tiny_tat-esc_exp_nr_0.5_embed

jethrowang

"2025-04-19T11:30:36Z"

1

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:formospeech/tat_asr_aligned", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2025-04-18T10:47:51Z"

--- library_name: transformers language: - zh license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - formospeech/tat_asr_aligned model-index: - name: Whisper Tiny Taiwanese (exp_nr_0.5_embed) results: [] ---  # Whisper Tiny Taiwanese (exp_nr_0.5_embed) This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the TAT ASR Aligned dataset. It achieves the following results on the evaluation set: - Loss: 1.3660 - Cer: 32.6917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 681 - training_steps: 6810 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.4831 | 0.9985 | 681 | 0.9359 | 46.4492 | | 0.2884 | 1.9971 | 1362 | 0.8960 | 35.3926 | | 0.1794 | 2.9956 | 2043 | 0.9427 | 38.7004 | | 0.1035 | 3.9941 | 2724 | 1.0423 | 35.5451 | | 0.0585 | 4.9927 | 3405 | 1.1355 | 35.1530 | | 0.0287 | 5.9912 | 4086 | 1.2120 | 35.6566 | | 0.0146 | 6.9897 | 4767 | 1.2845 | 33.8159 | | 0.0052 | 7.9883 | 5448 | 1.3243 | 33.4713 | | 0.0018 | 8.9868 | 6129 | 1.3504 | 32.9579 | | 0.0008 | 9.9853 | 6810 | 1.3660 | 32.6917 | ### Framework versions - Transformers 4.49.0 - Pytorch 2.0.0.post304 - Datasets 3.3.2 - Tokenizers 0.21.0

YOYO-AI/QwQ-Olympic-coder-32B

YOYO-AI

"2025-04-19T11:28:28Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2408.07990", "base_model:Qwen/QwQ-32B", "base_model:merge:Qwen/QwQ-32B", "base_model:Qwen/Qwen2.5-Coder-32B", "base_model:merge:Qwen/Qwen2.5-Coder-32B", "base_model:open-r1/OlympicCoder-32B", "base_model:merge:open-r1/OlympicCoder-32B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T11:14:10Z"

--- base_model: - Qwen/Qwen2.5-Coder-32B - Qwen/QwQ-32B - open-r1/OlympicCoder-32B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [Qwen/Qwen2.5-Coder-32B](https://huggingface.co/Qwen/Qwen2.5-Coder-32B) as a base. ### Models Merged The following models were included in the merge: * [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) * [open-r1/OlympicCoder-32B](https://huggingface.co/open-r1/OlympicCoder-32B) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: sce models: # Pivot model - model: Qwen/Qwen2.5-Coder-32B # Target models - model: Qwen/QwQ-32B - model: open-r1/OlympicCoder-32B base_model: Qwen/Qwen2.5-Coder-32B parameters: select_topk: 1 dtype: bfloat16 tokenizer_source: Qwen/QwQ-32B normalize: true int8_mask: true ```

mradermacher/Nemo-Chuckles-12B-GGUF

mradermacher

"2025-04-19T11:28:26Z"

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:spacematt/Nemo-Chuckles-12B", "base_model:quantized:spacematt/Nemo-Chuckles-12B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T09:22:20Z"

--- base_model: spacematt/Nemo-Chuckles-12B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/spacematt/Nemo-Chuckles-12B  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Nemo-Chuckles-12B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q2_K.gguf) | Q2_K | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q3_K_S.gguf) | Q3_K_S | 5.6 | | | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q3_K_L.gguf) | Q3_K_L | 6.7 | | | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.IQ4_XS.gguf) | IQ4_XS | 6.9 | | | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q5_K_S.gguf) | Q5_K_S | 8.6 | | | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q5_K_M.gguf) | Q5_K_M | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q6_K.gguf) | Q6_K | 10.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Nemo-Chuckles-12B-GGUF/resolve/main/Nemo-Chuckles-12B.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

pnle/Qwen2.5-1.5B-Open-R1-Distill

pnle

"2025-04-19T11:26:39Z"

8

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:open-r1/OpenR1-Math-220k", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-08T08:45:49Z"

--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: open-r1/OpenR1-Math-220k library_name: transformers model_name: Qwen2.5-1.5B-Open-R1-Distill tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-1.5B-Open-R1-Distill This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="pnle/Qwen2.5-1.5B-Open-R1-Distill", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/pnnnnnnn-sun-yat-sen-university/huggingface/runs/6wo0gpqn) This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.0 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

ppagare/Meta-Llama-3.1-8B-Instruct-pg-chatbot-LORA

ppagare

"2025-04-19T11:23:17Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2025-04-18T10:11:22Z"

--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** ppagare - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

mradermacher/Gemma-3-1B-Roblox-Luau-GGUF

mradermacher

"2025-04-19T11:23:08Z"

0

transformers

[ "transformers", "gguf", "chat", "roblox", "luau", "en", "dataset:boatbomber/roblox-info-dump", "dataset:boatbomber/the-luau-stack", "base_model:boatbomber/Gemma-3-1B-Roblox-Luau", "base_model:quantized:boatbomber/Gemma-3-1B-Roblox-Luau", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T11:11:15Z"

--- base_model: boatbomber/Gemma-3-1B-Roblox-Luau datasets: - boatbomber/roblox-info-dump - boatbomber/the-luau-stack language: - en library_name: transformers license: gemma license_link: https://ai.google.dev/gemma/terms quantized_by: mradermacher tags: - chat - roblox - luau --- ## About      static quants of https://huggingface.co/boatbomber/Gemma-3-1B-Roblox-Luau  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q3_K_S.gguf) | Q3_K_S | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q2_K.gguf) | Q2_K | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.IQ4_XS.gguf) | IQ4_XS | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q3_K_L.gguf) | Q3_K_L | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q4_K_S.gguf) | Q4_K_S | 1.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q5_K_S.gguf) | Q5_K_S | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q5_K_M.gguf) | Q5_K_M | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q6_K.gguf) | Q6_K | 1.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.Q8_0.gguf) | Q8_0 | 1.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Gemma-3-1B-Roblox-Luau-GGUF/resolve/main/Gemma-3-1B-Roblox-Luau.f16.gguf) | f16 | 2.7 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-3

Shaleen123

"2025-04-19T11:22:49Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T11:16:58Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/Llama_3.x_70b_Triads_V7-GGUF

mradermacher

"2025-04-19T11:19:59Z"

19

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:NexesMess/Llama_3.x_70b_Triads_V7", "base_model:quantized:NexesMess/Llama_3.x_70b_Triads_V7", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-03-14T10:04:24Z"

--- base_model: NexesMess/Llama_3.x_70b_Triads_V7 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/NexesMess/Llama_3.x_70b_Triads_V7  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q2_K.gguf) | Q2_K | 26.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q3_K_S.gguf) | Q3_K_S | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q3_K_L.gguf) | Q3_K_L | 37.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.IQ4_XS.gguf) | IQ4_XS | 38.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q5_K_S.gguf) | Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q5_K_M.gguf) | Q5_K_M | 50.1 | | | [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality | | [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V7-GGUF/resolve/main/Llama_3.x_70b_Triads_V7.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

RioShiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2

RioShiina

"2025-04-19T11:17:30Z"

0

null

[ "ja", "base_model:abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1", "base_model:quantized:abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1", "license:apache-2.0", "region:us" ]

null

"2025-04-19T11:17:28Z"

--- license: apache-2.0 base_model: abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1 base_model_relation: quantized language: - ja --- Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.2.8">turboderp's ExLlamaV2 v0.2.8</a> for quantization. **[2.2bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/2.2bpw)** **[3.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/3.0bpw)** **[4.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/4.0bpw)** **[5.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/5.0bpw)** **[6.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/6.0bpw)** **[7.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/7.0bpw)** **[8.0bpw](https://huggingface.co/rioshiina/ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2/tree/8.0bpw)** ## Calibration Dataset [TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm) ## ABEJA-Qwen2.5-7b-Japanese-v0.1-exl2 - Model creator: [abeja](https://huggingface.co/abeja) - Original model: [ABEJA-Qwen2.5-7b-Japanese-v0.1](https://huggingface.co/abeja/ABEJA-Qwen2.5-7b-Japanese-v0.1)

rbelanec/train_qqp_1744902597

rbelanec

"2025-04-19T11:17:03Z"

0

peft

[ "peft", "safetensors", "llama-factory", "prompt-tuning", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]

null

"2025-04-17T15:18:48Z"

--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - llama-factory - prompt-tuning - generated_from_trainer model-index: - name: train_qqp_1744902597 results: [] ---  # train_qqp_1744902597 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the qqp dataset. It achieves the following results on the evaluation set: - Loss: 0.2268 - Num Input Tokens Seen: 49022016 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.3 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:------:|:-----:|:---------------:|:-----------------:| | 0.3195 | 0.0098 | 200 | 0.3106 | 245536 | | 0.2888 | 0.0195 | 400 | 0.3139 | 489696 | | 0.256 | 0.0293 | 600 | 0.2695 | 737824 | | 0.2961 | 0.0391 | 800 | 0.2602 | 981856 | | 0.2767 | 0.0489 | 1000 | 0.2627 | 1225952 | | 0.3652 | 0.0586 | 1200 | 0.3079 | 1469920 | | 0.285 | 0.0684 | 1400 | 0.2651 | 1715360 | | 0.2802 | 0.0782 | 1600 | 0.2552 | 1961952 | | 0.2617 | 0.0879 | 1800 | 0.2957 | 2205952 | | 0.3155 | 0.0977 | 2000 | 0.2709 | 2453792 | | 0.3359 | 0.1075 | 2200 | 0.2968 | 2698976 | | 0.2723 | 0.1173 | 2400 | 0.2812 | 2944000 | | 0.3139 | 0.1270 | 2600 | 0.2977 | 3190496 | | 0.228 | 0.1368 | 2800 | 0.2626 | 3439104 | | 0.2619 | 0.1466 | 3000 | 0.2564 | 3684640 | | 0.3061 | 0.1564 | 3200 | 0.2572 | 3931744 | | 0.2571 | 0.1661 | 3400 | 0.2869 | 4179680 | | 0.2624 | 0.1759 | 3600 | 0.2561 | 4424000 | | 0.2622 | 0.1857 | 3800 | 0.2517 | 4667488 | | 0.2741 | 0.1954 | 4000 | 0.2555 | 4910752 | | 0.2699 | 0.2052 | 4200 | 0.2509 | 5157152 | | 0.2502 | 0.2150 | 4400 | 0.2667 | 5403360 | | 0.2635 | 0.2248 | 4600 | 0.2509 | 5647360 | | 0.2642 | 0.2345 | 4800 | 0.2685 | 5889632 | | 0.2264 | 0.2443 | 5000 | 0.2568 | 6135424 | | 0.2425 | 0.2541 | 5200 | 0.2495 | 6380320 | | 0.2447 | 0.2638 | 5400 | 0.2495 | 6627360 | | 0.2926 | 0.2736 | 5600 | 0.2547 | 6873760 | | 0.3135 | 0.2834 | 5800 | 0.2659 | 7121504 | | 0.2274 | 0.2932 | 6000 | 0.2668 | 7366208 | | 0.2814 | 0.3029 | 6200 | 0.2612 | 7615264 | | 0.2345 | 0.3127 | 6400 | 0.2557 | 7860128 | | 0.2643 | 0.3225 | 6600 | 0.2545 | 8103360 | | 0.2393 | 0.3323 | 6800 | 0.2910 | 8350976 | | 0.2462 | 0.3420 | 7000 | 0.2714 | 8597664 | | 0.2514 | 0.3518 | 7200 | 0.2714 | 8842400 | | 0.2713 | 0.3616 | 7400 | 0.2500 | 9087456 | | 0.358 | 0.3713 | 7600 | 0.2564 | 9331520 | | 0.2397 | 0.3811 | 7800 | 0.2481 | 9576704 | | 0.2357 | 0.3909 | 8000 | 0.2521 | 9819200 | | 0.2608 | 0.4007 | 8200 | 0.2518 | 10064928 | | 0.2234 | 0.4104 | 8400 | 0.2646 | 10308768 | | 0.272 | 0.4202 | 8600 | 0.2483 | 10551296 | | 0.2518 | 0.4300 | 8800 | 0.2720 | 10798144 | | 0.242 | 0.4397 | 9000 | 0.2520 | 11047776 | | 0.2411 | 0.4495 | 9200 | 0.2625 | 11292384 | | 0.2778 | 0.4593 | 9400 | 0.2483 | 11534944 | | 0.2841 | 0.4691 | 9600 | 0.3225 | 11778880 | | 0.2869 | 0.4788 | 9800 | 0.2492 | 12025472 | | 0.2562 | 0.4886 | 10000 | 0.2491 | 12267968 | | 0.245 | 0.4984 | 10200 | 0.2480 | 12511488 | | 0.2534 | 0.5081 | 10400 | 0.2492 | 12755904 | | 0.2657 | 0.5179 | 10600 | 0.2542 | 13002048 | | 0.2525 | 0.5277 | 10800 | 0.2583 | 13246272 | | 0.2363 | 0.5375 | 11000 | 0.2486 | 13491456 | | 0.2056 | 0.5472 | 11200 | 0.2683 | 13735936 | | 0.2512 | 0.5570 | 11400 | 0.2502 | 13982176 | | 0.2496 | 0.5668 | 11600 | 0.2485 | 14227136 | | 0.2499 | 0.5766 | 11800 | 0.2652 | 14472704 | | 0.221 | 0.5863 | 12000 | 0.2578 | 14717856 | | 0.3302 | 0.5961 | 12200 | 0.2479 | 14963520 | | 0.2619 | 0.6059 | 12400 | 0.2624 | 15208224 | | 0.2512 | 0.6156 | 12600 | 0.2487 | 15453408 | | 0.2312 | 0.6254 | 12800 | 0.2576 | 15698016 | | 0.2569 | 0.6352 | 13000 | 0.2521 | 15942720 | | 0.252 | 0.6450 | 13200 | 0.2469 | 16186528 | | 0.2567 | 0.6547 | 13400 | 0.2460 | 16433472 | | 0.2417 | 0.6645 | 13600 | 0.2502 | 16679360 | | 0.2363 | 0.6743 | 13800 | 0.2536 | 16924896 | | 0.2023 | 0.6840 | 14000 | 0.2571 | 17171072 | | 0.2533 | 0.6938 | 14200 | 0.2453 | 17416704 | | 0.2489 | 0.7036 | 14400 | 0.2610 | 17663488 | | 0.2735 | 0.7134 | 14600 | 0.2442 | 17910272 | | 0.2151 | 0.7231 | 14800 | 0.2596 | 18151712 | | 0.2568 | 0.7329 | 15000 | 0.2432 | 18395744 | | 0.2308 | 0.7427 | 15200 | 0.2456 | 18642368 | | 0.2532 | 0.7524 | 15400 | 0.2430 | 18889312 | | 0.2515 | 0.7622 | 15600 | 0.2442 | 19133312 | | 0.2491 | 0.7720 | 15800 | 0.2443 | 19376992 | | 0.2613 | 0.7818 | 16000 | 0.2443 | 19620672 | | 0.2467 | 0.7915 | 16200 | 0.2485 | 19866240 | | 0.2548 | 0.8013 | 16400 | 0.2481 | 20112160 | | 0.2564 | 0.8111 | 16600 | 0.2450 | 20358464 | | 0.2713 | 0.8209 | 16800 | 0.2460 | 20602112 | | 0.2777 | 0.8306 | 17000 | 0.2397 | 20845696 | | 0.2217 | 0.8404 | 17200 | 0.2408 | 21089792 | | 0.251 | 0.8502 | 17400 | 0.2454 | 21334176 | | 0.1997 | 0.8599 | 17600 | 0.2507 | 21577600 | | 0.2264 | 0.8697 | 17800 | 0.2566 | 21822848 | | 0.2631 | 0.8795 | 18000 | 0.2381 | 22067296 | | 0.2491 | 0.8893 | 18200 | 0.2405 | 22313824 | | 0.2151 | 0.8990 | 18400 | 0.2373 | 22558912 | | 0.2593 | 0.9088 | 18600 | 0.2564 | 22803456 | | 0.2586 | 0.9186 | 18800 | 0.2396 | 23047552 | | 0.2396 | 0.9283 | 19000 | 0.2389 | 23293856 | | 0.2385 | 0.9381 | 19200 | 0.2390 | 23539488 | | 0.2906 | 0.9479 | 19400 | 0.2493 | 23786464 | | 0.2623 | 0.9577 | 19600 | 0.2394 | 24032064 | | 0.2404 | 0.9674 | 19800 | 0.2371 | 24278464 | | 0.2486 | 0.9772 | 20000 | 0.2393 | 24521632 | | 0.2454 | 0.9870 | 20200 | 0.2435 | 24765600 | | 0.2408 | 0.9968 | 20400 | 0.2354 | 25007520 | | 0.2772 | 1.0065 | 20600 | 0.2488 | 25253920 | | 0.28 | 1.0163 | 20800 | 0.2345 | 25498432 | | 0.2189 | 1.0261 | 21000 | 0.2350 | 25745120 | | 0.2038 | 1.0359 | 21200 | 0.2462 | 25989952 | | 0.2399 | 1.0456 | 21400 | 0.2449 | 26234080 | | 0.2399 | 1.0554 | 21600 | 0.2423 | 26482784 | | 0.2106 | 1.0652 | 21800 | 0.2333 | 26728608 | | 0.194 | 1.0750 | 22000 | 0.2438 | 26977792 | | 0.2419 | 1.0847 | 22200 | 0.2328 | 27218080 | | 0.2607 | 1.0945 | 22400 | 0.2352 | 27463456 | | 0.2204 | 1.1043 | 22600 | 0.2364 | 27708832 | | 0.2387 | 1.1140 | 22800 | 0.2334 | 27956000 | | 0.2512 | 1.1238 | 23000 | 0.2327 | 28204704 | | 0.2076 | 1.1336 | 23200 | 0.2332 | 28452992 | | 0.2111 | 1.1434 | 23400 | 0.2331 | 28696640 | | 0.2251 | 1.1531 | 23600 | 0.2315 | 28937792 | | 0.2526 | 1.1629 | 23800 | 0.2321 | 29186016 | | 0.2118 | 1.1727 | 24000 | 0.2375 | 29431872 | | 0.253 | 1.1824 | 24200 | 0.2321 | 29673216 | | 0.2585 | 1.1922 | 24400 | 0.2326 | 29916864 | | 0.2545 | 1.2020 | 24600 | 0.2308 | 30163136 | | 0.2238 | 1.2118 | 24800 | 0.2309 | 30405920 | | 0.2053 | 1.2215 | 25000 | 0.2311 | 30652960 | | 0.2021 | 1.2313 | 25200 | 0.2308 | 30897184 | | 0.2241 | 1.2411 | 25400 | 0.2310 | 31141248 | | 0.2576 | 1.2508 | 25600 | 0.2547 | 31385376 | | 0.1997 | 1.2606 | 25800 | 0.2426 | 31630880 | | 0.2535 | 1.2704 | 26000 | 0.2305 | 31876320 | | 0.2404 | 1.2802 | 26200 | 0.2330 | 32120640 | | 0.2961 | 1.2899 | 26400 | 0.2408 | 32365056 | | 0.2544 | 1.2997 | 26600 | 0.2298 | 32611072 | | 0.2309 | 1.3095 | 26800 | 0.2332 | 32855648 | | 0.2574 | 1.3193 | 27000 | 0.2293 | 33097440 | | 0.2147 | 1.3290 | 27200 | 0.2324 | 33342208 | | 0.2189 | 1.3388 | 27400 | 0.2373 | 33587968 | | 0.2572 | 1.3486 | 27600 | 0.2337 | 33831872 | | 0.2355 | 1.3583 | 27800 | 0.2298 | 34076864 | | 0.19 | 1.3681 | 28000 | 0.2303 | 34319616 | | 0.2344 | 1.3779 | 28200 | 0.2296 | 34563968 | | 0.2514 | 1.3877 | 28400 | 0.2300 | 34808704 | | 0.2254 | 1.3974 | 28600 | 0.2340 | 35054656 | | 0.2162 | 1.4072 | 28800 | 0.2293 | 35297248 | | 0.2615 | 1.4170 | 29000 | 0.2393 | 35543232 | | 0.2474 | 1.4267 | 29200 | 0.2354 | 35787200 | | 0.2404 | 1.4365 | 29400 | 0.2288 | 36033344 | | 0.2282 | 1.4463 | 29600 | 0.2294 | 36277664 | | 0.2092 | 1.4561 | 29800 | 0.2296 | 36522912 | | 0.2429 | 1.4658 | 30000 | 0.2293 | 36766912 | | 0.2493 | 1.4756 | 30200 | 0.2291 | 37010880 | | 0.1932 | 1.4854 | 30400 | 0.2317 | 37255808 | | 0.2201 | 1.4952 | 30600 | 0.2287 | 37500256 | | 0.2086 | 1.5049 | 30800 | 0.2282 | 37744128 | | 0.2215 | 1.5147 | 31000 | 0.2332 | 37989600 | | 0.2221 | 1.5245 | 31200 | 0.2286 | 38233760 | | 0.202 | 1.5342 | 31400 | 0.2285 | 38480384 | | 0.2619 | 1.5440 | 31600 | 0.2291 | 38728448 | | 0.2256 | 1.5538 | 31800 | 0.2300 | 38975296 | | 0.2269 | 1.5636 | 32000 | 0.2287 | 39221728 | | 0.2851 | 1.5733 | 32200 | 0.2288 | 39465280 | | 0.2444 | 1.5831 | 32400 | 0.2297 | 39712992 | | 0.2923 | 1.5929 | 32600 | 0.2279 | 39960032 | | 0.221 | 1.6026 | 32800 | 0.2297 | 40206624 | | 0.225 | 1.6124 | 33000 | 0.2322 | 40449856 | | 0.2395 | 1.6222 | 33200 | 0.2280 | 40693312 | | 0.2141 | 1.6320 | 33400 | 0.2278 | 40936672 | | 0.2278 | 1.6417 | 33600 | 0.2273 | 41180480 | | 0.2443 | 1.6515 | 33800 | 0.2271 | 41422272 | | 0.2371 | 1.6613 | 34000 | 0.2290 | 41666752 | | 0.198 | 1.6710 | 34200 | 0.2277 | 41912096 | | 0.2219 | 1.6808 | 34400 | 0.2272 | 42157856 | | 0.2452 | 1.6906 | 34600 | 0.2273 | 42402496 | | 0.2011 | 1.7004 | 34800 | 0.2276 | 42645088 | | 0.2132 | 1.7101 | 35000 | 0.2270 | 42889536 | | 0.2117 | 1.7199 | 35200 | 0.2285 | 43134208 | | 0.2199 | 1.7297 | 35400 | 0.2273 | 43377824 | | 0.1868 | 1.7395 | 35600 | 0.2285 | 43623232 | | 0.2633 | 1.7492 | 35800 | 0.2272 | 43872416 | | 0.2165 | 1.7590 | 36000 | 0.2280 | 44117632 | | 0.2574 | 1.7688 | 36200 | 0.2271 | 44363488 | | 0.2696 | 1.7785 | 36400 | 0.2268 | 44608000 | | 0.2385 | 1.7883 | 36600 | 0.2271 | 44852672 | | 0.2374 | 1.7981 | 36800 | 0.2269 | 45098144 | | 0.216 | 1.8079 | 37000 | 0.2276 | 45342912 | | 0.1787 | 1.8176 | 37200 | 0.2269 | 45590720 | | 0.1936 | 1.8274 | 37400 | 0.2271 | 45835200 | | 0.2439 | 1.8372 | 37600 | 0.2272 | 46079328 | | 0.2397 | 1.8469 | 37800 | 0.2272 | 46322496 | | 0.2493 | 1.8567 | 38000 | 0.2268 | 46565536 | | 0.2368 | 1.8665 | 38200 | 0.2268 | 46809376 | | 0.2552 | 1.8763 | 38400 | 0.2269 | 47052352 | | 0.2151 | 1.8860 | 38600 | 0.2269 | 47298816 | | 0.188 | 1.8958 | 38800 | 0.2270 | 47547712 | | 0.2345 | 1.9056 | 39000 | 0.2272 | 47794048 | | 0.2166 | 1.9153 | 39200 | 0.2270 | 48039872 | | 0.2361 | 1.9251 | 39400 | 0.2271 | 48286368 | | 0.2186 | 1.9349 | 39600 | 0.2271 | 48530880 | | 0.2108 | 1.9447 | 39800 | 0.2270 | 48774656 | | 0.2436 | 1.9544 | 40000 | 0.2270 | 49022016 | ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

mlfoundations-dev/b1_science_top_2_10k

mlfoundations-dev

"2025-04-19T11:16:34Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T04:45:07Z"

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b1_science_top_2_10k results: [] ---  # b1_science_top_2_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b1_science_top_2_10k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

rehatr/chan

rehatr

"2025-04-19T11:15:50Z"

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

"2025-04-19T10:49:53Z"

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: chan --- # Chan <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `chan` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "chan", "lora_weights": "https://huggingface.co/rehatr/chan/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('rehatr/chan', weight_name='lora.safetensors') image = pipeline('chan').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/rehatr/chan/discussions) to add images that show off what you’ve made with this LoRA.

aleegis/9d59f849-08fd-4dd8-9b12-765a10cae01d

aleegis

"2025-04-19T11:15:22Z"

0

peft

[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:upstage/SOLAR-10.7B-Instruct-v1.0", "base_model:adapter:upstage/SOLAR-10.7B-Instruct-v1.0", "license:cc-by-nc-4.0", "region:us" ]

null

"2025-04-19T09:15:38Z"

--- library_name: peft license: cc-by-nc-4.0 base_model: upstage/SOLAR-10.7B-Instruct-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: 9d59f849-08fd-4dd8-9b12-765a10cae01d results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: upstage/SOLAR-10.7B-Instruct-v1.0 bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - fc41e3171106b27b_train_data.json ds_type: json format: custom path: /workspace/input_data/fc41e3171106b27b_train_data.json type: field_instruction: source field_output: good-translation format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: aleegis/9d59f849-08fd-4dd8-9b12-765a10cae01d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: null lora_alpha: 32 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true loraplus_lr_embedding: 1.0e-06 loraplus_lr_ratio: 16 lr_scheduler: cosine max_grad_norm: 1 max_steps: 1500 micro_batch_size: 8 mlflow_experiment_name: /tmp/fc41e3171106b27b_train_data.json model_type: AutoModelForCausalLM num_epochs: 200 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null save_total_limit: 10 saves_per_epoch: 0 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.0 wandb_entity: null wandb_mode: online wandb_name: ec623392-2dd6-4ede-83e3-b5ca8a66c621 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: ec623392-2dd6-4ede-83e3-b5ca8a66c621 warmup_steps: 100 weight_decay: 0 xformers_attention: null ``` </details><br> # 9d59f849-08fd-4dd8-9b12-765a10cae01d This model is a fine-tuned version of [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1500 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

jyc0325/Qwen2.5-1.5B-Instruct-SFT-code

jyc0325

"2025-04-19T11:13:28Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:open-r1/codeforces-cots", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T00:39:44Z"

--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: open-r1/codeforces-cots library_name: transformers model_name: Qwen2.5-1.5B-Instruct-SFT-code tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-SFT-code This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/codeforces-cots](https://huggingface.co/datasets/open-r1/codeforces-cots) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jyc0325/Qwen2.5-1.5B-Instruct-SFT-code", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/davidcho2356-purdue-university/huggingface/runs/xuwfbac1) This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

dekos2606/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-beaked_aquatic_dove

dekos2606

"2025-04-19T11:13:06Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am beaked aquatic dove", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T11:11:51Z"

--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-beaked_aquatic_dove tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am beaked aquatic dove - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-beaked_aquatic_dove This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dekos2606/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-beaked_aquatic_dove", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

ushnaiffath/gita-text-generation-gpt2

ushnaiffath

"2025-04-19T11:11:51Z"

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T11:11:29Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

TOMFORD79/Cake_7

TOMFORD79

"2025-04-19T11:09:59Z"

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

"2025-04-19T10:56:37Z"

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

TOMFORD79/Cake_6

TOMFORD79

"2025-04-19T11:09:43Z"

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

"2025-04-19T10:56:31Z"

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf

RichardErkhov

"2025-04-19T11:07:22Z"

0

null

[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T09:32:59Z"

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3 - GGUF - Model creator: https://huggingface.co/yjwon/ - Original model: https://huggingface.co/yjwon/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3/ | Name | Quant method | Size | | ---- | ---- | ---- | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q2_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q2_K.gguf) | Q2_K | 2.54GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_XS.gguf) | IQ3_XS | 2.82GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_S.gguf) | IQ3_S | 2.97GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ3_M.gguf) | IQ3_M | 3.06GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K.gguf) | Q3_K | 3.28GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ4_XS.gguf) | IQ4_XS | 3.68GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_0.gguf) | Q4_0 | 3.83GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K.gguf) | Q4_K | 4.07GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_1.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q4_1.gguf) | Q4_1 | 4.24GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_0.gguf) | Q5_0 | 4.66GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K_S.gguf) | Q5_K_S | 4.66GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K.gguf) | Q5_K | 4.78GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_1.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q5_1.gguf) | Q5_1 | 5.07GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q6_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q6_K.gguf) | Q6_K | 5.54GB | | [mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q8_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3-gguf/blob/main/mpg27_mistral7bv3_sft_dpo_beta1e-1_epoch3.Q8_0.gguf) | Q8_0 | 7.17GB | Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Raniahossam33/qwen2.5-7b-instruct-ditto-Tunisia-food-sap1-custom

Raniahossam33

"2025-04-19T11:03:17Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-03-21T00:55:05Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

dzanbek/cfd34c94-17a4-4d2b-a13b-ec0e25df2cab

dzanbek

"2025-04-19T11:02:45Z"

0

peft

[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "8-bit", "bitsandbytes", "region:us" ]

null

"2025-04-19T10:58:12Z"

--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: cfd34c94-17a4-4d2b-a13b-ec0e25df2cab results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Llama-3.2-1B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1f97354293835a5f_train_data.json ds_type: json format: custom path: /workspace/input_data/1f97354293835a5f_train_data.json type: field_instruction: prompt field_output: init_response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/cfd34c94-17a4-4d2b-a13b-ec0e25df2cab hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/1f97354293835a5f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 40576177-a321-4699-bf15-9122727da99d wandb_project: 01-31 wandb_run: your_name wandb_runid: 40576177-a321-4699-bf15-9122727da99d warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # cfd34c94-17a4-4d2b-a13b-ec0e25df2cab This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0 | 0.0288 | 150 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

nicksedov/rubert-tiny2-classifier

nicksedov

"2025-04-19T10:58:24Z"

21

0

null

[ "safetensors", "bert", "text-classification", "ru", "base_model:cointegrated/rubert-tiny2", "base_model:finetune:cointegrated/rubert-tiny2", "license:mit", "region:us" ]

text-classification

"2025-04-09T17:47:48Z"

--- license: mit language: - ru base_model: - cointegrated/rubert-tiny2 pipeline_tag: text-classification --- Модель бинарной классификации текстов на русском языке. Получает на вход текст запроса и определяет, содержится ли в нем просьба сгенерировать изображение.

itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF

itlwas

"2025-04-19T10:58:01Z"

0

transformers

[ "transformers", "gguf", "text-generation-inference", "hiber-multi", "safetensors", "Llama3.1", "multilingual-llm", "instruction-tuning", "flash-attention2", "quantization", "llama-cpp", "gguf-my-repo", "text-generation", "en", "zh", "es", "fr", "de", "ja", "ko", "ru", "base_model:Hibernates/Hiber-Multi-10B-Instruct", "base_model:quantized:Hibernates/Hiber-Multi-10B-Instruct", "license:llama3.1", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2025-04-19T10:57:35Z"

--- base_model: Hibernates/Hiber-Multi-10B-Instruct language: - en - zh - es - fr - de - ja - ko - ru library_name: transformers license: llama3.1 pipeline_tag: text-generation tags: - text-generation-inference - hiber-multi - safetensors - Llama3.1 - multilingual-llm - instruction-tuning - flash-attention2 - quantization - llama-cpp - gguf-my-repo --- # itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Hibernates/Hiber-Multi-10B-Instruct`](https://huggingface.co/Hibernates/Hiber-Multi-10B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Hibernates/Hiber-Multi-10B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF --hf-file hiber-multi-10b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF --hf-file hiber-multi-10b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF --hf-file hiber-multi-10b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo itlwas/Hiber-Multi-10B-Instruct-Q4_K_M-GGUF --hf-file hiber-multi-10b-instruct-q4_k_m.gguf -c 2048 ```

unsloth/DeepSeek-V3-0324-BF16

unsloth

"2025-04-19T10:57:32Z"

113

2

transformers

[ "transformers", "safetensors", "deepseek_v3", "text-generation", "deepseek", "unsloth", "conversational", "custom_code", "en", "arxiv:2412.19437", "base_model:deepseek-ai/DeepSeek-V3-0324", "base_model:quantized:deepseek-ai/DeepSeek-V3-0324", "license:mit", "autotrain_compatible", "endpoints_compatible", "fp8", "region:us" ]

text-generation

"2025-03-25T00:34:56Z"

--- base_model: deepseek-ai/DeepSeek-V3-0324 language: - en library_name: transformers license: mit tags: - deepseek_v3 - deepseek - unsloth - transformers --- # DeepSeek-V3-0324    <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V3-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> ## Features DeepSeek-V3-0324 demonstrates notable improvements over its predecessor, DeepSeek-V3, in several key aspects. ![Model Performance](figures/0324_comparison.png) ### Reasoning Capabilities - Significant improvements in benchmark performance: - MMLU-Pro: 75.9 → 81.2 (+5.3) - GPQA: 59.1 → 68.4 (+9.3) - AIME: 39.6 → 59.4 (+19.8) - LiveCodeBench: 39.2 → 49.2 (+10.0) ### Front-End Web Development - Improved the executability of the code - More aesthetically pleasing web pages and game front-ends ### Chinese Writing Proficiency - Enhanced style and content quality: - Aligned with the R1 writing style - Better quality in medium-to-long-form writing - Feature Enhancements - Improved multi-turn interactive rewriting - Optimized translation quality and letter writing ### Chinese Search Capabilities - Enhanced report analysis requests with more detailed outputs ### Function Calling Improvements - Increased accuracy in Function Calling, fixing issues from previous V3 versions --- ## Usage Recommendations ### System Prompt In the official DeepSeek web/app, we use the same system prompt with a specific date. ``` 该助手为DeepSeek Chat，由深度求索公司创造。今天是{current date}。 ``` For example, ``` 该助手为DeepSeek Chat，由深度求索公司创造。今天是3月24日，星期一。 ``` ### Temperature In our web and application environments, the temperature parameter $T_{model}$ is set to 0.3. Because many users use the default temperature 1.0 in API call, we have implemented an API temperature $T_{api}$ mapping mechanism that adjusts the input API temperature value of 1.0 to the most suitable model temperature setting of 0.3. $$ T_{model} = T_{api} \times 0.3 \quad (0 \leq T_{api} \leq 1) $$ $$ T_{model} = T_{api} - 0.7 \quad (1 < T_{api} \leq 2) $$ Thus, if you call V3 via API, temperature 1.0 equals to the model temperature 0.3. ### Prompts for File Uploading and Web Search For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments. ``` file_template = \ """[file name]: {file_name} [file content begin] {file_content} [file content end] {question}""" ``` For Web Search, {search_results}, {cur_date}, and {question} are arguments. For Chinese query, we use the prompt: ``` search_answer_zh_template = \ '''# 以下内容是基于用户发送的消息的搜索结果: {search_results} 在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。在回答时，请注意以下几点： - 今天是{cur_date}。 - 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。 - 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。 - 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。 - 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。 - 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。 - 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。 - 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。 - 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。 # 用户消息为： {question}''' ``` For English query, we use the prompt: ``` search_answer_en_template = \ '''# The following contents are the search results related to the user's message: {search_results} In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer. When responding, please keep the following points in mind: - Today is {cur_date}. - Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question. - For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary. - For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough. - If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content. - For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content. - Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability. - Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage. - Unless the user requests otherwise, your response should be in the same language as the user's question. # The user's message is: {question}''' ``` ## How to Run Locally The model structure of DeepSeek-V3-0324 is exactly the same as DeepSeek-V3. Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running this model locally. **This model supports features such as function calling, JSON output, and FIM completion. For instructions on how to construct prompts to use these features, please refer to [DeepSeek-V2.5](https://huggingface.co/deepseek-ai/DeepSeek-V2.5#function-calling) repo.** **NOTE: Hugging Face's Transformers has not been directly supported yet.** ## License This repository and the model weights are licensed under the [MIT License](LICENSE). ## Citation ``` @misc{deepseekai2024deepseekv3technicalreport, title={DeepSeek-V3 Technical Report}, author={DeepSeek-AI}, year={2024}, eprint={2412.19437}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.19437}, } ``` ## Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).

unsloth/DeepSeek-V3-bf16

unsloth

"2025-04-19T10:55:22Z"

505

16

transformers

[ "transformers", "safetensors", "deepseek_v3", "text-generation", "deepseek", "unsloth", "conversational", "custom_code", "en", "arxiv:2412.19437", "base_model:deepseek-ai/DeepSeek-V3", "base_model:quantized:deepseek-ai/DeepSeek-V3", "license:mit", "autotrain_compatible", "endpoints_compatible", "fp8", "region:us" ]

text-generation

"2025-01-06T09:27:41Z"

--- base_model: deepseek-ai/DeepSeek-V3 language: - en library_name: transformers license: mit tags: - deepseek_v3 - deepseek - unsloth - transformers --- ## ***See [our collection](https://huggingface.co/collections/unsloth/deepseek-v3-all-versions-677cf5cfd7df8b7815fc723c) for versions of Deepseek V3 including GGUF, bf16 and original formats.*** # Finetune Llama 3.3, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth! We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) # unsloth/DeepSeek-V3-bf16 For more details on the model, please go to Deepseek's original [model card](https://huggingface.co/deepseek-ai/DeepSeek-V3) ## ✨ Finetune for Free All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. | Unsloth supports | Free Notebooks | Performance | Memory use | |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| | **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2.4x faster | 58% less | | **Llama-3.2 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) | 2x faster | 60% less | | **Qwen2 VL (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb) | 1.8x faster | 60% less | | **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | 2x faster | 60% less | | **Llama-3.1 (8B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb) | 2.4x faster | 58% less | | **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb) | 2x faster | 50% less | | **Gemma 2 (9B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb) | 2.4x faster | 58% less | | **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 62% less | [<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="200"/>](https://docs.unsloth.ai) - This [Llama 3.2 conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_(7B)-Text_Completion.ipynb) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. ## Special Thanks A huge thank you to the Deepseek team for creating and releasing these models. ## Model Information We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. ## 2. Model Summary --- **Architecture: Innovative Load Balancing Strategy and Training Objective** - On top of the efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which minimizes the performance degradation that arises from encouraging load balancing. - We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model performance. It can also be used for speculative decoding for inference acceleration. --- **Pre-Training: Towards Ultimate Training Efficiency** - We design an FP8 mixed precision training framework and, for the first time, validate the feasibility and effectiveness of FP8 training on an extremely large-scale model. - Through co-design of algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, nearly achieving full computation-communication overlap. This significantly enhances our training efficiency and reduces the training costs, enabling us to further scale up the model size without additional overhead. - At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the currently strongest open-source base model. The subsequent training stages after pre-training require only 0.1M GPU hours. --- **Post-Training: Knowledge Distillation from DeepSeek-R1** - We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and notably improves its reasoning performance. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3. --- ## 3. Model Downloads <div align="center"> | **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** | | :------------: | :------------: | :------------: | :------------: | :------------: | | DeepSeek-V3-Base | 671B | 37B | 128K | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base) | | DeepSeek-V3 | 671B | 37B | 128K | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3) | </div> **NOTE: The total size of DeepSeek-V3 models on HuggingFace is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.** To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: [How_to Run_Locally](#6-how-to-run-locally). For developers looking to dive deeper, we recommend exploring [README_WEIGHTS.md](./README_WEIGHTS.md) for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is currently under active development within the community, and we welcome your contributions and feedback. ## 4. Evaluation Results ### Base Model #### Standard Benchmarks <div align="center"> | | Benchmark (Metric) | # Shots | DeepSeek-V2 | Qwen2.5 72B | LLaMA3.1 405B | DeepSeek-V3 | |---|-------------------|----------|--------|-------------|---------------|---------| | | Architecture | - | MoE | Dense | Dense | MoE | | | # Activated Params | - | 21B | 72B | 405B | 37B | | | # Total Params | - | 236B | 72B | 405B | 671B | | English | Pile-test (BPB) | - | 0.606 | 0.638 | **0.542** | 0.548 | | | BBH (EM) | 3-shot | 78.8 | 79.8 | 82.9 | **87.5** | | | MMLU (Acc.) | 5-shot | 78.4 | 85.0 | 84.4 | **87.1** | | | MMLU-Redux (Acc.) | 5-shot | 75.6 | 83.2 | 81.3 | **86.2** | | | MMLU-Pro (Acc.) | 5-shot | 51.4 | 58.3 | 52.8 | **64.4** | | | DROP (F1) | 3-shot | 80.4 | 80.6 | 86.0 | **89.0** | | | ARC-Easy (Acc.) | 25-shot | 97.6 | 98.4 | 98.4 | **98.9** | | | ARC-Challenge (Acc.) | 25-shot | 92.2 | 94.5 | **95.3** | **95.3** | | | HellaSwag (Acc.) | 10-shot | 87.1 | 84.8 | **89.2** | 88.9 | | | PIQA (Acc.) | 0-shot | 83.9 | 82.6 | **85.9** | 84.7 | | | WinoGrande (Acc.) | 5-shot | **86.3** | 82.3 | 85.2 | 84.9 | | | RACE-Middle (Acc.) | 5-shot | 73.1 | 68.1 | **74.2** | 67.1 | | | RACE-High (Acc.) | 5-shot | 52.6 | 50.3 | **56.8** | 51.3 | | | TriviaQA (EM) | 5-shot | 80.0 | 71.9 | **82.7** | **82.9** | | | NaturalQuestions (EM) | 5-shot | 38.6 | 33.2 | **41.5** | 40.0 | | | AGIEval (Acc.) | 0-shot | 57.5 | 75.8 | 60.6 | **79.6** | | Code | HumanEval (Pass@1) | 0-shot | 43.3 | 53.0 | 54.9 | **65.2** | | | MBPP (Pass@1) | 3-shot | 65.0 | 72.6 | 68.4 | **75.4** | | | LiveCodeBench-Base (Pass@1) | 3-shot | 11.6 | 12.9 | 15.5 | **19.4** | | | CRUXEval-I (Acc.) | 2-shot | 52.5 | 59.1 | 58.5 | **67.3** | | | CRUXEval-O (Acc.) | 2-shot | 49.8 | 59.9 | 59.9 | **69.8** | | Math | GSM8K (EM) | 8-shot | 81.6 | 88.3 | 83.5 | **89.3** | | | MATH (EM) | 4-shot | 43.4 | 54.4 | 49.0 | **61.6** | | | MGSM (EM) | 8-shot | 63.6 | 76.2 | 69.9 | **79.8** | | | CMath (EM) | 3-shot | 78.7 | 84.5 | 77.3 | **90.7** | | Chinese | CLUEWSC (EM) | 5-shot | 82.0 | 82.5 | **83.0** | 82.7 | | | C-Eval (Acc.) | 5-shot | 81.4 | 89.2 | 72.5 | **90.1** | | | CMMLU (Acc.) | 5-shot | 84.0 | **89.5** | 73.7 | 88.8 | | | CMRC (EM) | 1-shot | **77.4** | 75.8 | 76.0 | 76.3 | | | C3 (Acc.) | 0-shot | 77.4 | 76.7 | **79.7** | 78.6 | | | CCPM (Acc.) | 0-shot | **93.0** | 88.5 | 78.6 | 92.0 | | Multilingual | MMMLU-non-English (Acc.) | 5-shot | 64.0 | 74.8 | 73.8 | **79.4** | </div> Note: Best results are shown in bold. Scores with a gap not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 achieves the best performance on most benchmarks, especially on math and code tasks. For more evaluation details, please check our paper. #### Context Window <p align="center"> <img width="80%" src="figures/niah.png"> </p> Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-V3 performs well across all context window lengths up to **128K**. ### Chat Model #### Standard Benchmarks (Models larger than 67B) <div align="center"> | | **Benchmark (Metric)** | **DeepSeek V2-0506** | **DeepSeek V2.5-0905** | **Qwen2.5 72B-Inst.** | **Llama3.1 405B-Inst.** | **Claude-3.5-Sonnet-1022** | **GPT-4o 0513** | **DeepSeek V3** | |---|---------------------|---------------------|----------------------|---------------------|----------------------|---------------------------|----------------|----------------| | | Architecture | MoE | MoE | Dense | Dense | - | - | MoE | | | # Activated Params | 21B | 21B | 72B | 405B | - | - | 37B | | | # Total Params | 236B | 236B | 72B | 405B | - | - | 671B | | English | MMLU (EM) | 78.2 | 80.6 | 85.3 | **88.6** | **88.3** | 87.2 | **88.5** | | | MMLU-Redux (EM) | 77.9 | 80.3 | 85.6 | 86.2 | **88.9** | 88.0 | **89.1** | | | MMLU-Pro (EM) | 58.5 | 66.2 | 71.6 | 73.3 | **78.0** | 72.6 | 75.9 | | | DROP (3-shot F1) | 83.0 | 87.8 | 76.7 | 88.7 | 88.3 | 83.7 | **91.6** | | | IF-Eval (Prompt Strict) | 57.7 | 80.6 | 84.1 | 86.0 | **86.5** | 84.3 | 86.1 | | | GPQA-Diamond (Pass@1) | 35.3 | 41.3 | 49.0 | 51.1 | **65.0** | 49.9 | 59.1 | | | SimpleQA (Correct) | 9.0 | 10.2 | 9.1 | 17.1 | 28.4 | **38.2** | 24.9 | | | FRAMES (Acc.) | 66.9 | 65.4 | 69.8 | 70.0 | 72.5 | **80.5** | 73.3 | | | LongBench v2 (Acc.) | 31.6 | 35.4 | 39.4 | 36.1 | 41.0 | 48.1 | **48.7** | | Code | HumanEval-Mul (Pass@1) | 69.3 | 77.4 | 77.3 | 77.2 | 81.7 | 80.5 | **82.6** | | | LiveCodeBench (Pass@1-COT) | 18.8 | 29.2 | 31.1 | 28.4 | 36.3 | 33.4 | **40.5** | | | LiveCodeBench (Pass@1) | 20.3 | 28.4 | 28.7 | 30.1 | 32.8 | 34.2 | **37.6** | | | Codeforces (Percentile) | 17.5 | 35.6 | 24.8 | 25.3 | 20.3 | 23.6 | **51.6** | | | SWE Verified (Resolved) | - | 22.6 | 23.8 | 24.5 | **50.8** | 38.8 | 42.0 | | | Aider-Edit (Acc.) | 60.3 | 71.6 | 65.4 | 63.9 | **84.2** | 72.9 | 79.7 | | | Aider-Polyglot (Acc.) | - | 18.2 | 7.6 | 5.8 | 45.3 | 16.0 | **49.6** | | Math | AIME 2024 (Pass@1) | 4.6 | 16.7 | 23.3 | 23.3 | 16.0 | 9.3 | **39.2** | | | MATH-500 (EM) | 56.3 | 74.7 | 80.0 | 73.8 | 78.3 | 74.6 | **90.2** | | | CNMO 2024 (Pass@1) | 2.8 | 10.8 | 15.9 | 6.8 | 13.1 | 10.8 | **43.2** | | Chinese | CLUEWSC (EM) | 89.9 | 90.4 | **91.4** | 84.7 | 85.4 | 87.9 | 90.9 | | | C-Eval (EM) | 78.6 | 79.5 | 86.1 | 61.5 | 76.7 | 76.0 | **86.5** | | | C-SimpleQA (Correct) | 48.5 | 54.1 | 48.4 | 50.4 | 51.3 | 59.3 | **64.8** | Note: All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature settings to derive robust final results. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance against frontier closed-source models. </div> #### Open Ended Generation Evaluation <div align="center"> | Model | Arena-Hard | AlpacaEval 2.0 | |-------|------------|----------------| | DeepSeek-V2.5-0905 | 76.2 | 50.5 | | Qwen2.5-72B-Instruct | 81.2 | 49.1 | | LLaMA-3.1 405B | 69.3 | 40.5 | | GPT-4o-0513 | 80.4 | 51.1 | | Claude-Sonnet-3.5-1022 | 85.2 | 52.0 | | DeepSeek-V3 | **85.5** | **70.0** | Note: English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric. </div> ## 5. Chat Website & API Platform You can chat with DeepSeek-V3 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in) We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 6. How to Run Locally DeepSeek-V3 can be deployed locally using the following hardware and open-source community software: 1. **DeepSeek-Infer Demo**: We provide a simple and lightweight demo for FP8 and BF16 inference. 2. **SGLang**: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes. 3. **LMDeploy**: Enables efficient FP8 and BF16 inference for local and cloud deployment. 4. **TensorRT-LLM**: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon. 5. **vLLM**: Support DeekSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism. 6. **AMD GPU**: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes. 7. **Huawei Ascend NPU**: Supports running DeepSeek-V3 on Huawei Ascend devices. Since FP8 training is natively adopted in our framework, we only provide FP8 weights. If you require BF16 weights for experimentation, you can use the provided conversion script to perform the transformation. Here is an example of converting FP8 weights to BF16: ```shell cd inference python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-hf-path /path/to/bf16_weights ``` **NOTE: Huggingface's Transformers has not been directly supported yet.** ### 6.1 Inference with DeepSeek-Infer Demo (example only) #### Model Weights & Demo Code Preparation First, clone our DeepSeek-V3 GitHub repository: ```shell git clone https://github.com/deepseek-ai/DeepSeek-V3.git ``` Navigate to the `inference` folder and install dependencies listed in `requirements.txt`. ```shell cd DeepSeek-V3/inference pip install -r requirements.txt ``` Download the model weights from HuggingFace, and put them into `/path/to/DeepSeek-V3` folder. #### Model Weights Conversion Convert HuggingFace model weights to a specific format: ```shell python convert.py --hf-ckpt-path /path/to/DeepSeek-V3 --save-path /path/to/DeepSeek-V3-Demo --n-experts 256 --model-parallel 16 ``` #### Run Then you can chat with DeepSeek-V3: ```shell torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-addr $ADDR --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200 ``` Or batch inference on a given file: ```shell torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-addr $ADDR --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --input-file $FILE ``` ### 6.2 Inference with SGLang (recommended) [SGLang](https://github.com/sgl-project/sglang) currently supports MLA optimizations, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering state-of-the-art latency and throughput performance among open-source frameworks. Notably, [SGLang v0.4.1](https://github.com/sgl-project/sglang/releases/tag/v0.4.1) fully supports running DeepSeek-V3 on both **NVIDIA and AMD GPUs**, making it a highly versatile and robust solution. Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3 ### 6.3 Inference with LMDeploy (recommended) [LMDeploy](https://github.com/InternLM/lmdeploy), a flexible and high-performance inference and serving framework tailored for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online deployment capabilities, seamlessly integrating with PyTorch-based workflows. For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: https://github.com/InternLM/lmdeploy/issues/2960 ### 6.4 Inference with TRT-LLM (recommended) [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3. ### 6.5 Inference with vLLM (recommended) [vLLM](https://github.com/vllm-project/vllm) v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard techniques, vLLM offers _pipeline parallelism_ allowing you to run this model on multiple machines connected by networks. For detailed guidance, please refer to the [vLLM instructions](https://docs.vllm.ai/en/latest/serving/distributed_serving.html). Please feel free to follow [the enhancement plan](https://github.com/vllm-project/vllm/issues/11539) as well. ### 6.6 Recommended Inference Functionality with AMD GPUs In collaboration with the AMD team, we have achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For detailed guidance, please refer to the [SGLang instructions](#63-inference-with-lmdeploy-recommended). ### 6.7 Recommended Inference Functionality with Huawei Ascend NPUs The [MindIE](https://www.hiascend.com/en/software/mindie) framework from the Huawei Ascend community has successfully adapted the BF16 version of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the [instructions here](https://modelers.cn/models/MindIE/deepseekv3). ## 7. License This code repository is licensed under [the MIT License](LICENSE-CODE). The use of DeepSeek-V3 Base/Chat models is subject to [the Model License](LICENSE-MODEL). DeepSeek-V3 series (including Base and Chat) supports commercial use. ## 8. Citation ``` @misc{deepseekai2024deepseekv3technicalreport, title={DeepSeek-V3 Technical Report}, author={DeepSeek-AI and Aixin Liu and Bei Feng and Bing Xue and Bingxuan Wang and Bochao Wu and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Daya Guo and Dejian Yang and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Haowei Zhang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Li and Hui Qu and J. L. Cai and Jian Liang and Jianzhong Guo and Jiaqi Ni and Jiashi Li and Jiawei Wang and Jin Chen and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and Junxiao Song and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Lei Xu and Leyi Xia and Liang Zhao and Litong Wang and Liyue Zhang and Meng Li and Miaojun Wang and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Mingming Li and Ning Tian and Panpan Huang and Peiyi Wang and Peng Zhang and Qiancheng Wang and Qihao Zhu and Qinyu Chen and Qiushi Du and R. J. Chen and R. L. Jin and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and Runxin Xu and Ruoyu Zhang and Ruyi Chen and S. S. Li and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shaoqing Wu and Shengfeng Ye and Shengfeng Ye and Shirong Ma and Shiyu Wang and Shuang Zhou and Shuiping Yu and Shunfeng Zhou and Shuting Pan and T. Wang and Tao Yun and Tian Pei and Tianyu Sun and W. L. Xiao and Wangding Zeng and Wanjia Zhao and Wei An and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and X. Q. Li and Xiangyue Jin and Xianzu Wang and Xiao Bi and Xiaodong Liu and Xiaohan Wang and Xiaojin Shen and Xiaokang Chen and Xiaokang Zhang and Xiaosha Chen and Xiaotao Nie and Xiaowen Sun and Xiaoxiang Wang and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xingkai Yu and Xinnan Song and Xinxia Shan and Xinyi Zhou and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and Y. K. Li and Y. Q. Wang and Y. X. Wei and Y. X. Zhu and Yang Zhang and Yanhong Xu and Yanhong Xu and Yanping Huang and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Li and Yaohui Wang and Yi Yu and Yi Zheng and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Ying Tang and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yu Wu and Yuan Ou and Yuchen Zhu and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yukun Zha and Yunfan Xiong and Yunxian Ma and Yuting Yan and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Z. F. Wu and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhen Huang and Zhen Zhang and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhibin Gou and Zhicheng Ma and Zhigang Yan and Zhihong Shao and Zhipeng Xu and Zhiyu Wu and Zhongyu Zhang and Zhuoshu Li and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Ziyi Gao and Zizheng Pan}, year={2024}, eprint={2412.19437}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.19437}, } ``` ## 9. Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).

itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF

itlwas

"2025-04-19T10:51:33Z"

0

transformers

[ "transformers", "gguf", "nvidia", "llama-3", "pytorch", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:nvidia/Nemotron-Mini-4B-Instruct", "base_model:quantized:nvidia/Nemotron-Mini-4B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2025-04-19T10:51:15Z"

--- base_model: nvidia/Nemotron-Mini-4B-Instruct language: - en library_name: transformers license: other license_name: nvidia-open-model-license license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf pipeline_tag: text-generation tags: - nvidia - llama-3 - pytorch - llama-cpp - gguf-my-repo --- # itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`nvidia/Nemotron-Mini-4B-Instruct`](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-mini-4b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-mini-4b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-mini-4b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo itlwas/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-mini-4b-instruct-q4_k_m.gguf -c 2048 ```

amarkale/irnx_ironman_suit_mac_42

amarkale

"2025-04-19T10:48:57Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2025-04-18T16:03:00Z"

--- license: apache-2.0 ---

RobotsMali/stt-bm-quartznet15x5-V0

RobotsMali

"2025-04-19T10:47:18Z"

75

1

nemo

[ "nemo", "automatic-speech-recognition", "speech", "audio", "CTC", "QuartzNet", "pytorch", "Bambara", "NeMo", "bm", "dataset:RobotsMali/bam-asr-early", "license:cc-by-4.0", "model-index", "region:us" ]

automatic-speech-recognition

"2025-02-07T04:07:11Z"

--- language: - bm library_name: nemo datasets: - RobotsMali/bam-asr-early thumbnail: null tags: - automatic-speech-recognition - speech - audio - CTC - QuartzNet - pytorch - Bambara - NeMo license: cc-by-4.0 base_model: stt_fr_quartznet15x5 model-index: - name: stt-bm-quartznet15x5 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: bam-asr-early type: RobotsMali/bam-asr-early split: test args: language: bm metrics: - name: Test WER type: wer value: 46.5 metrics: - wer pipeline_tag: automatic-speech-recognition --- # QuartzNet 15x5 CTC Bambara <style> img { display: inline; } </style> [![Model architecture](https://img.shields.io/badge/Model_Arch-QuartzNet-lightgrey#model-badge)](#model-architecture) | [![Model size](https://img.shields.io/badge/Params-19M-lightgrey#model-badge)](#model-architecture) | [![Language](https://img.shields.io/badge/Language-bm-lightgrey#model-badge)](#datasets) `stt-bm-quartznet15x5-V0` is a fine-tuned version of NVIDIA’s [`stt_fr_quartznet15x5`](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_quartznet15x5) optimized for **Bambara ASR**. This model cannot write **Punctuations and Capitalizations**, it utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the training set of bam-asr-all dataset. The model was fine-tuned using **NVIDIA NeMo** and is trained with **CTC (Connectionist Temporal Classification) Loss**. ## **🚨 Important Note** This model, along with its associated resources, is part of an **ongoing research effort**, improvements and refinements are expected in future versions. Users should be aware that: - **The model may not generalize very well accross all speaking conditions and dialects.** - **Community feedback is welcome, and contributions are encouraged to refine the model further.** ## NVIDIA NeMo: Training To fine-tune or use the model, install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend installing it after setting up the latest PyTorch version. ```bash pip install nemo_toolkit['asr'] ``` ## How to Use This Model ### Load Model with NeMo ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="RobotsMali/stt-bm-quartznet15x5") ``` ### Transcribe Audio ```python # Assuming you have a test audio file named sample_audio.wav asr_model.transcribe(['sample_audio.wav']) ``` ### Input This model accepts **16 kHz mono-channel audio (wav files)** as input. ### Output This model provides transcribed speech as a string for a given speech sample. ## Model Architecture QuartzNet is a convolutional architecture, which consists of **1D time-channel separable convolutions** optimized for speech recognition. More information on QuartzNet can be found here: [QuartzNet Model](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/models.html#quartznet). ## Training The NeMo toolkit was used to fine-tune this model for **25939 steps** over the `stt_fr_quartznet15x5` model. This model is trained with this [base config](https://github.com/RobotsMali-AI/bambara-asr/blob/main/configs/quartznet-20m-config-v2.yaml). The full training configurations, scripts, and experimental logs are available here: 🔗 [Bambara-ASR Experiments](https://github.com/RobotsMali-AI/bambara-asr) ## Dataset This model was fine-tuned on the [bam-asr-early](https://huggingface.co/datasets/RobotsMali/bam-asr-early) dataset, which consists of **37 hours of transcribed Bambara speech data**. The dataset is primarily derived from **Jeli-ASR dataset** (~87%). ## Performance The performance of Automatic Speech Recognition models is measured using **Word Error Rate (WER%)**. |**Version**|**Tokenizer**|**Vocabulary Size**|**bam-asr-all (test set)**| |---------|-----------------------|-----------------|---------| | V2 | Character-wise | 45 | 46.5 | These are **greedy WER numbers without external LM**. ## License This model is released under the **CC-BY-4.0** license. By using this model, you agree to the terms of the license. --- More details are available in the **Experimental Technical Report**: 📄 [Draft Technical Report - Weights & Biases](https://wandb.ai/yacoudiarra-wl/bam-asr-nemo-training/reports/Draft-Technical-Report-V1--VmlldzoxMTIyOTMzOA). Feel free to open a discussion on Hugging Face or [file an issue](https://github.com/RobotsMali-AI/bambara-asr/issues) on GitHub if you have any contributions. ---

itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF

itlwas

"2025-04-19T10:44:20Z"

0

nemo

[ "nemo", "gguf", "llama-cpp", "gguf-my-repo", "en", "hi", "base_model:nvidia/Nemotron-4-Mini-Hindi-4B-Instruct", "base_model:quantized:nvidia/Nemotron-4-Mini-Hindi-4B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T10:44:07Z"

--- base_model: nvidia/Nemotron-4-Mini-Hindi-4B-Instruct language: - en - hi library_name: nemo license: other license_name: nvidia-open-model-license license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf tags: - llama-cpp - gguf-my-repo --- # itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`nvidia/Nemotron-4-Mini-Hindi-4B-Instruct`](https://huggingface.co/nvidia/Nemotron-4-Mini-Hindi-4B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/Nemotron-4-Mini-Hindi-4B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-4-mini-hindi-4b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-4-mini-hindi-4b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-4-mini-hindi-4b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo itlwas/Nemotron-4-Mini-Hindi-4B-Instruct-Q4_K_M-GGUF --hf-file nemotron-4-mini-hindi-4b-instruct-q4_k_m.gguf -c 2048 ```

RobotsMali/soloni-114m-tdt-ctc-V0

RobotsMali

"2025-04-19T10:42:31Z"

23

2

nemo

[ "nemo", "automatic-speech-recognition", "speech", "audio", "Transducer", "TDT", "FastConformer", "Conformer", "pytorch", "Bambara", "NeMo", "bm", "dataset:RobotsMali/bam-asr-early", "base_model:nvidia/parakeet-tdt_ctc-110m", "base_model:finetune:nvidia/parakeet-tdt_ctc-110m", "license:cc-by-4.0", "model-index", "region:us" ]

automatic-speech-recognition

"2025-02-07T04:04:28Z"

--- language: - bm library_name: nemo datasets: - RobotsMali/bam-asr-early thumbnail: null tags: - automatic-speech-recognition - speech - audio - Transducer - TDT - FastConformer - Conformer - pytorch - Bambara - NeMo license: cc-by-4.0 base_model: nvidia/parakeet-tdt_ctc-110m model-index: - name: soloni-114m-tdt-ctc results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: bam-asr-early type: RobotsMali/bam-asr-early split: test args: language: bm metrics: - name: Test WER (TDT) type: wer value: 66.7 - name: Test WER (CTC) type: wer value: 40.6 metrics: - wer pipeline_tag: automatic-speech-recognition --- # Soloni TDT-CTC 114M Bambara <style> img { display: inline; } </style> [![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--TDT-lightgrey#model-badge)](#model-architecture) | [![Model size](https://img.shields.io/badge/Params-114M-lightgrey#model-badge)](#model-architecture) | [![Language](https://img.shields.io/badge/Language-bm-lightgrey#model-badge)](#datasets) `soloni-114m-tdt-ctc-V0` is a fine tuned version of nvidia's [`parakeet-tdt_ctc-110m`](https://huggingface.co/nvidia/parakeet-tdt_ctc-110m) that transcribes bambara language speech. Unlike its base model, this model cannot write Punctuations and Capitalizations since these were absent from its training. The model was fine-tuned using **NVIDIA NeMo** and supports **both TDT (Token-and-Duration Transducer) and CTC (Connectionist Temporal Classification) decoding**. ## **🚨 Important Note** **Update (February 17th):** We observed a significantly lower WER **(\~36%)** for the TDT branch when using an external WER calculation method that relies solely on the predicted and reference transcriptions. However, the WER values reported in this model card are derived from the standard NeMo workflow using PyTorch Lightning's trainer, where the TDT branch yielded higher WER scores **(\~66%)**. Differences may arise due to variations in post-processing, alignment handling, or evaluation methodologies. This model, along with its associated resources, is part of an **ongoing research effort**, improvements and refinements are expected in future versions. Users should be aware that: - **The model may not generalize very well accross all speaking conditions and dialects.** - **Community feedback is welcome, and contributions are encouraged to refine the model further.** ## NVIDIA NeMo: Training To fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version. ```bash pip install nemo_toolkit['asr'] ``` ## How to Use This Model Note that this model has been released for research purposes primarily. ### Load Model with NeMo ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name="RobotsMali/soloni-114m-tdt-ctc") ``` ### Transcribe Audio ```python # Assuming you have a test audio file named sample_audio.wav asr_model.transcribe(['sample_audio.wav']) ``` Note that the decoding strategy for the TDT decoder use CUDA Graphs by default but not all GPUs and versions of cuda support that parameter. If you run into a `RuntimeError: CUDA error: invalid argument` you should set that argument to false in the decoding strategy before calling asr_model.transcribe() ```python decoding_cfg = asr_model.cfg.decoding # Disable CUDA Graphs decoding_cfg.greedy.use_cuda_graph_decoder = False # Then change the decoding strategy asr_model.change_decoding_strategy(decoding_cfg=decoding_cfg) ``` ### Input This model accepts **16000 Hz mono-channel** audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture This model uses a Hybrid FastConformer-TDT-CTC architecture. FastConformer is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. You may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer). ## Training The NeMo toolkit was used for finetuning this model for **16,296 steps** over `parakeet-tdt_ctc-110m` model. This model is trained with this [base config](https://github.com/RobotsMali-AI/bambara-asr/blob/main/configs/parakeet-110m-config-v6.yaml). The full training configurations, scripts, and experimental logs are available here: 🔗 [Bambara-ASR Experiments](https://github.com/RobotsMali-AI/bambara-asr) The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). ## Dataset This model was fine-tuned on the [bam-asr-early](https://huggingface.co/datasets/RobotsMali/bam-asr-early) dataset, which consists of 37 hours of transcribed Bambara speech data. The dataset is primarily derived from **Jeli-ASR dataset** (~87%). ## Performance The performance of Automatic Speech Recognition models is measured using Word Error Rate. Since this model has two decoders operating independently, each decoder is evaluated independently too. The following table summarizes the performance of the available models in this collection with the Transducer decoder. Performances of the ASR models are reported in terms of **Word Error Rate (WER%)**. |**Decoder (Version)**|**Tokenizer**|**Vocabulary Size**|**bam-asr-all (test set)**| |---------|-----------------------|-----------------|---------| | CTC (V6) | BPE | 1024 | 40.6 | | TDT (V6) | BPE | 1024 | 66.7 | These are greedy WER numbers without external LM. By default the main decoder branch is the TDT branch, if you would like to switch to the CTC decoder simply run this block of code before calling the .transcribe method ```python # Retrieve the CTC decoding config ctc_decoding_cfg = asr_model.cfg.aux_ctc.decoding # Then change the decoding strategy asr_model.change_decoding_strategy(decoder_type='ctc', decoding_cfg=ctc_decoding_cfg) # Transcribe with the CTC decoder asr_model.transcribe(['sample_audio.wav']) ``` ## License This model is released under the **CC-BY-4.0** license. By using this model, you agree to the terms of the license. --- More details are available in the **Experimental Technical Report**: 📄 [Draft Technical Report - Weights & Biases](https://wandb.ai/yacoudiarra-wl/bam-asr-nemo-training/reports/Draft-Technical-Report-V1--VmlldzoxMTIyOTMzOA). Feel free to open a discussion on Hugging Face or [file an issue](https://github.com/RobotsMali-AI/bambara-asr/issues) on github if you have any contributions ---

ishvets/Sarah

ishvets

"2025-04-19T10:40:42Z"

0

null

[ "license:other", "region:us" ]

null

"2025-04-19T09:30:42Z"

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

Raniahossam33/qwen2.5-7b-instruct-ditto-Syria-topic-sap1-custom

Raniahossam33

"2025-04-19T10:37:45Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-03-19T19:51:28Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

aliyuksal/mistral-mailwizz-merged

aliyuksal

"2025-04-19T10:37:12Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T10:23:22Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

heyIamUmair/llama3-3b-merged-legal

heyIamUmair

"2025-04-19T10:36:06Z"

0

null

[ "safetensors", "llama", "legal", "pakistan", "merged", "instruction-tuned", "base_model:unsloth/Llama-3.2-3B-Instruct", "base_model:finetune:unsloth/Llama-3.2-3B-Instruct", "license:apache-2.0", "region:us" ]

null

"2025-04-19T10:31:00Z"

--- license: apache-2.0 tags: - llama - legal - pakistan - merged - instruction-tuned model_type: causal-lm base_model: unsloth/Llama-3.2-3B-Instruct inference: true --- # 🧠 LLaMA 3.2 3B – Legal Chatbot (Merged) This is a merged model combining `unsloth/Llama-3.2-3B-Instruct` with LoRA adapters fine-tuned on Pakistani law, including family, criminal, and property law. ✅ Merged ✅ Inference API compatible ✅ No Unsloth or adapter loading needed

Bouquets/SecGPT-1.5B-Q4_K_M-GGUF

Bouquets

"2025-04-19T10:35:48Z"

0

transformers

[ "transformers", "gguf", "cybersecurity", "security", "network-security", "llama-cpp", "gguf-my-repo", "zh", "en", "base_model:clouditera/SecGPT-1.5B", "base_model:quantized:clouditera/SecGPT-1.5B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T10:35:40Z"

--- base_model: clouditera/SecGPT-1.5B language: - zh - en library_name: transformers license: apache-2.0 tags: - cybersecurity - security - network-security - llama-cpp - gguf-my-repo --- # Bouquets/SecGPT-1.5B-Q4_K_M-GGUF This model was converted to GGUF format from [`clouditera/SecGPT-1.5B`](https://huggingface.co/clouditera/SecGPT-1.5B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/clouditera/SecGPT-1.5B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Bouquets/SecGPT-1.5B-Q4_K_M-GGUF --hf-file secgpt-1.5b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Bouquets/SecGPT-1.5B-Q4_K_M-GGUF --hf-file secgpt-1.5b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Bouquets/SecGPT-1.5B-Q4_K_M-GGUF --hf-file secgpt-1.5b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Bouquets/SecGPT-1.5B-Q4_K_M-GGUF --hf-file secgpt-1.5b-q4_k_m.gguf -c 2048 ```

itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF

itlwas

"2025-04-19T10:35:08Z"

0

transformers

[ "transformers", "gguf", "nvidia", "llama-3", "pytorch", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:nvidia/Llama-3.1-Nemotron-Nano-8B-v1", "base_model:quantized:nvidia/Llama-3.1-Nemotron-Nano-8B-v1", "license:other", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2025-04-19T10:34:44Z"

--- base_model: nvidia/Llama-3.1-Nemotron-Nano-8B-v1 language: - en library_name: transformers license: other license_name: nvidia-open-model-license license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ pipeline_tag: text-generation tags: - nvidia - llama-3 - pytorch - llama-cpp - gguf-my-repo --- # itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF This model was converted to GGUF format from [`nvidia/Llama-3.1-Nemotron-Nano-8B-v1`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo itlwas/Llama-3.1-Nemotron-Nano-8B-v1-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q4_k_m.gguf -c 2048 ```

iTroned/weight_test_early_fusion_sentiment_False_hate_speech_False_extra_layer_True

iTroned

"2025-04-19T10:33:56Z"

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

"2025-04-19T09:39:26Z"

--- library_name: transformers tags: - generated_from_trainer model-index: - name: weight_test_early_fusion_sentiment_False_hate_speech_False_extra_layer_True results: [] ---  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/moufl4td) # weight_test_early_fusion_sentiment_False_hate_speech_False_extra_layer_True This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7058 - Accuracy Offensive: 0.7980 - F1 Offensive: 0.7770 - Accuracy Targeted: 0.7727 - F1 Targeted: 0.5053 - Accuracy Stance: 0.7247 - F1 Stance: 0.3955 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Offensive | F1 Offensive | Accuracy Targeted | F1 Targeted | Accuracy Stance | F1 Stance | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:------------:|:-----------------:|:-----------:|:---------------:|:---------:| | 0.8406 | 1.0 | 1324 | 0.7136 | 0.7938 | 0.7702 | 0.7659 | 0.4982 | 0.6952 | 0.2286 | | 0.6996 | 2.0 | 2648 | 0.7058 | 0.7980 | 0.7770 | 0.7727 | 0.5053 | 0.7247 | 0.3955 | | 0.6313 | 3.0 | 3972 | 0.8419 | 0.7874 | 0.7714 | 0.7632 | 0.5038 | 0.6798 | 0.3803 | | 0.5086 | 4.0 | 5296 | 1.2924 | 0.7949 | 0.7662 | 0.7704 | 0.5001 | 0.7356 | 0.3709 | ### Framework versions - Transformers 4.50.2 - Pytorch 2.6.0+cu124 - Datasets 3.0.1 - Tokenizers 0.21.1

yamatazen/StarrySky-12B-Q4_K_M-GGUF

yamatazen

"2025-04-19T10:31:54Z"

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:yamatazen/StarrySky-12B", "base_model:quantized:yamatazen/StarrySky-12B", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T10:31:21Z"

--- base_model: yamatazen/StarrySky-12B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # yamatazen/StarrySky-12B-Q4_K_M-GGUF This model was converted to GGUF format from [`yamatazen/StarrySky-12B`](https://huggingface.co/yamatazen/StarrySky-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/yamatazen/StarrySky-12B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo yamatazen/StarrySky-12B-Q4_K_M-GGUF --hf-file starrysky-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo yamatazen/StarrySky-12B-Q4_K_M-GGUF --hf-file starrysky-12b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo yamatazen/StarrySky-12B-Q4_K_M-GGUF --hf-file starrysky-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo yamatazen/StarrySky-12B-Q4_K_M-GGUF --hf-file starrysky-12b-q4_k_m.gguf -c 2048 ```

adarsh3601/my_gemma3_4b_pt

adarsh3601

"2025-04-19T10:28:51Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it", "base_model:finetune:unsloth/gemma-3-4b-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2025-04-19T10:28:37Z"

--- base_model: unsloth/gemma-3-4b-it tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** adarsh3601 - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-4b-it This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ngtranAI1/RawMomentum

ngtranAI1

"2025-04-19T10:27:08Z"

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-04-19T10:27:03Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF

itlwas

"2025-04-19T10:27:04Z"

0

transformers

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct", "base_model:quantized:nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T10:26:42Z"

--- base_model: nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct language: - en library_name: transformers license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo itlwas/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-8b-ultralong-4m-instruct-q4_k_m.gguf -c 2048 ```

CrimsonZockt/PaigeBueckers-FLUXLORA

CrimsonZockt

"2025-04-19T10:25:15Z"

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

"2025-04-19T10:24:44Z"

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- photoshoot of Paige Bueckers, female, woman, solo, black tanktop, professional headshot. output: url: images/photoshoot of Paige Bueckers, female, woman, so....png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Paige Bueckers --- # PaigeBueckers <Gallery /> ## Model description This is a LORA Model that i have train on Weights.gg ## Trigger words You should use `Paige Bueckers` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/CrimsonZockt/PaigeBueckers-FLUXLORA/tree/main) them in the Files & versions tab.

mradermacher/Pathos-Eta-LLaMa-70B-GGUF

mradermacher

"2025-04-19T10:23:22Z"

108

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:TareksGraveyard/Pathos-Eta-LLaMa-70B", "base_model:quantized:TareksGraveyard/Pathos-Eta-LLaMa-70B", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-02-20T03:18:43Z"

--- base_model: TareksGraveyard/Pathos-Eta-LLaMa-70B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/TareksGraveyard/Pathos-Eta-LLaMa-70B  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q2_K.gguf) | Q2_K | 26.5 | | | [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q3_K_S.gguf) | Q3_K_S | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q3_K_L.gguf) | Q3_K_L | 37.2 | | | [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.IQ4_XS.gguf) | IQ4_XS | 38.4 | | | [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q5_K_S.gguf) | Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q5_K_M.gguf) | Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality | | [PART 1](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pathos-Eta-LLaMa-70B-GGUF/resolve/main/Pathos-Eta-LLaMa-70B.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF

mradermacher

"2025-04-19T10:23:22Z"

0

transformers

[ "transformers", "gguf", "en", "base_model:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final", "base_model:quantized:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T09:42:12Z"

--- base_model: Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About      static quants of https://huggingface.co/Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q2_K.gguf) | Q2_K | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q3_K_S.gguf) | Q3_K_S | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q3_K_L.gguf) | Q3_K_L | 8.0 | | | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.IQ4_XS.gguf) | IQ4_XS | 8.3 | | | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q5_K_S.gguf) | Q5_K_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q5_K_M.gguf) | Q5_K_M | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q6_K.gguf) | Q6_K | 12.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

ahmadtalha/whisper-small-dv

ahmadtalha

"2025-04-19T10:20:40Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2025-04-19T09:03:11Z"

--- library_name: transformers language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 13.683624856556664 ---  # Whisper Small Dv - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1749 - Wer Ortho: 63.2844 - Wer: 13.6836 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:------:|:----:|:---------------:|:---------:|:-------:| | 0.1201 | 1.6287 | 500 | 0.1749 | 63.2844 | 13.6836 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

mlfoundations-dev/b1_code_top_8_3k

mlfoundations-dev

"2025-04-19T10:19:51Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T05:18:44Z"

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b1_code_top_8_3k results: [] ---  # b1_code_top_8_3k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b1_code_top_8_3k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

MinaMila/phi3_LoRa_Adult_ep5_22

MinaMila

"2025-04-19T10:16:37Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-04-19T10:16:34Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

dgambettaphd/M_gmm2_gen8_run0_W_doc1000_synt64_MPP

dgambettaphd

"2025-04-19T10:12:42Z"

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-04-19T10:12:05Z"

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

railispeople/nalmis

railispeople

"2025-04-19T10:11:05Z"

0

null

[ "license:other", "region:us" ]

null

"2025-04-19T09:37:15Z"

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

thanhhau097/cm9o18aaq009ags6rcdmtp9ks

thanhhau097

"2025-04-19T10:06:16Z"

0

diffusers

[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

"2025-04-19T09:46:48Z"

--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers license: other instance_prompt: a photo of sks fashion model widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora ---  # Flux DreamBooth LoRA - thanhhau097/cm9o18aaq009ags6rcdmtp9ks <Gallery /> ## Model description These are thanhhau097/cm9o18aaq009ags6rcdmtp9ks DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `a photo of sks fashion model` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](thanhhau097/cm9o18aaq009ags6rcdmtp9ks/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('thanhhau097/cm9o18aaq009ags6rcdmtp9ks', weight_name='pytorch_lora_weights.safetensors') image = pipeline('a photo of sks fashion model').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

TommyClas/phaseseg_models

TommyClas

"2025-04-19T10:06:10Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "segformer", "image-segmentation", "vision", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]

image-segmentation

"2025-04-19T02:57:18Z"

--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - image-segmentation - vision - generated_from_trainer model-index: - name: phaseseg_models results: [] ---  # phaseseg_models This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the TommyClas/phase_seg dataset. It achieves the following results on the evaluation set: - Loss: 0.0372 - Mean Iou: 0.9744 - Mean Accuracy: 0.9872 - Overall Accuracy: 0.9869 - Accuracy 背景: nan - Accuracy 未水化水泥颗粒: 0.9806 - Accuracy 孔隙: 0.9893 - Accuracy 氢氧化钙: 0.9901 - Accuracy 其他水化物: 0.9887 - Iou 背景: nan - Iou 未水化水泥颗粒: 0.9730 - Iou 孔隙: 0.9695 - Iou 氢氧化钙: 0.9767 - Iou 其他水化物: 0.9782 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy 背景 | Accuracy 未水化水泥颗粒 | Accuracy 孔隙 | Accuracy 氢氧化钙 | Accuracy 其他水化物 | Iou 背景 | Iou 未水化水泥颗粒 | Iou 孔隙 | Iou 氢氧化钙 | Iou 其他水化物 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:----------------:|:-----------:|:-------------:|:--------------:|:------:|:-----------:|:------:|:--------:|:---------:| | No log | 1.0 | 50 | 0.2994 | 0.7375 | 0.9586 | 0.9580 | nan | 0.9557 | 0.9290 | 0.9696 | 0.9802 | 0.0 | 0.9224 | 0.9066 | 0.9195 | 0.9392 | | 0.4501 | 2.0 | 100 | 0.1558 | 0.7645 | 0.9767 | 0.9766 | nan | 0.9802 | 0.9580 | 0.9758 | 0.9929 | 0.0 | 0.9585 | 0.9504 | 0.9524 | 0.9609 | | 0.4501 | 3.0 | 150 | 0.1193 | 0.7715 | 0.9814 | 0.9812 | nan | 0.9797 | 0.9718 | 0.9829 | 0.9912 | 0.0 | 0.9661 | 0.9628 | 0.9607 | 0.9680 | | 0.0949 | 4.0 | 200 | 0.0898 | 0.7745 | 0.9835 | 0.9834 | nan | 0.9844 | 0.9751 | 0.9842 | 0.9902 | 0.0 | 0.9702 | 0.9667 | 0.9655 | 0.9699 | | 0.0949 | 5.0 | 250 | 0.0766 | 0.7762 | 0.9848 | 0.9848 | nan | 0.9848 | 0.9799 | 0.9842 | 0.9905 | 0.0 | 0.9729 | 0.9696 | 0.9674 | 0.9713 | | 0.0584 | 6.0 | 300 | 0.0624 | 0.7771 | 0.9856 | 0.9855 | nan | 0.9865 | 0.9802 | 0.9852 | 0.9905 | 0.0 | 0.9747 | 0.9704 | 0.9684 | 0.9723 | | 0.0584 | 7.0 | 350 | 0.0628 | 0.7777 | 0.9859 | 0.9858 | nan | 0.9845 | 0.9817 | 0.9865 | 0.9907 | 0.0 | 0.9743 | 0.9717 | 0.9695 | 0.9731 | | 0.0441 | 8.0 | 400 | 0.0575 | 0.7784 | 0.9863 | 0.9863 | nan | 0.9852 | 0.9841 | 0.9846 | 0.9914 | 0.0 | 0.9750 | 0.9732 | 0.9709 | 0.9732 | | 0.0441 | 9.0 | 450 | 0.0500 | 0.7788 | 0.9867 | 0.9866 | nan | 0.9855 | 0.9847 | 0.9839 | 0.9925 | 0.0 | 0.9762 | 0.9738 | 0.9706 | 0.9734 | | 0.0363 | 10.0 | 500 | 0.0496 | 0.7795 | 0.9870 | 0.9869 | nan | 0.9841 | 0.9859 | 0.9875 | 0.9905 | 0.0 | 0.9753 | 0.9745 | 0.9726 | 0.9751 | | 0.0363 | 11.0 | 550 | 0.0458 | 0.7798 | 0.9873 | 0.9872 | nan | 0.9844 | 0.9863 | 0.9875 | 0.9910 | 0.0 | 0.9758 | 0.9749 | 0.9731 | 0.9755 | | 0.0315 | 12.0 | 600 | 0.0423 | 0.7802 | 0.9875 | 0.9875 | nan | 0.9872 | 0.9845 | 0.9895 | 0.9891 | 0.0 | 0.9771 | 0.9750 | 0.9731 | 0.9757 | | 0.0315 | 13.0 | 650 | 0.0437 | 0.7800 | 0.9874 | 0.9873 | nan | 0.9851 | 0.9848 | 0.9891 | 0.9908 | 0.0 | 0.9762 | 0.9749 | 0.9731 | 0.9760 | | 0.0278 | 14.0 | 700 | 0.0390 | 0.7805 | 0.9878 | 0.9877 | nan | 0.9862 | 0.9859 | 0.9874 | 0.9916 | 0.0 | 0.9772 | 0.9753 | 0.9738 | 0.9762 | | 0.0278 | 15.0 | 750 | 0.0404 | 0.7799 | 0.9874 | 0.9873 | nan | 0.9834 | 0.9872 | 0.9896 | 0.9896 | 0.0 | 0.9753 | 0.9738 | 0.9740 | 0.9764 | | 0.0255 | 16.0 | 800 | 0.0422 | 0.7789 | 0.9868 | 0.9866 | nan | 0.9809 | 0.9872 | 0.9878 | 0.9911 | 0.0 | 0.9725 | 0.9709 | 0.9745 | 0.9765 | | 0.0255 | 17.0 | 850 | 0.0387 | 0.7794 | 0.9871 | 0.9869 | nan | 0.9831 | 0.9858 | 0.9900 | 0.9895 | 0.0 | 0.9742 | 0.9720 | 0.9739 | 0.9767 | | 0.0235 | 18.0 | 900 | 0.0395 | 0.7791 | 0.9869 | 0.9867 | nan | 0.9810 | 0.9882 | 0.9881 | 0.9903 | 0.0 | 0.9725 | 0.9706 | 0.9751 | 0.9770 | | 0.0235 | 19.0 | 950 | 0.0364 | 0.7790 | 0.9868 | 0.9866 | nan | 0.9809 | 0.9886 | 0.9867 | 0.9911 | 0.0 | 0.9723 | 0.9706 | 0.9752 | 0.9769 | | 0.0221 | 20.0 | 1000 | 0.0394 | 0.7785 | 0.9865 | 0.9863 | nan | 0.9801 | 0.9870 | 0.9887 | 0.9904 | 0.0 | 0.9713 | 0.9691 | 0.9751 | 0.9769 | | 0.0221 | 21.0 | 1050 | 0.0374 | 0.7787 | 0.9866 | 0.9864 | nan | 0.9812 | 0.9873 | 0.9871 | 0.9910 | 0.0 | 0.9720 | 0.9697 | 0.9750 | 0.9768 | | 0.021 | 22.0 | 1100 | 0.0364 | 0.7787 | 0.9867 | 0.9865 | nan | 0.9804 | 0.9874 | 0.9884 | 0.9906 | 0.0 | 0.9718 | 0.9695 | 0.9753 | 0.9771 | | 0.021 | 23.0 | 1150 | 0.0375 | 0.7784 | 0.9865 | 0.9863 | nan | 0.9792 | 0.9883 | 0.9888 | 0.9897 | 0.0 | 0.9708 | 0.9687 | 0.9754 | 0.9774 | | 0.0199 | 24.0 | 1200 | 0.0371 | 0.7782 | 0.9864 | 0.9861 | nan | 0.9792 | 0.9871 | 0.9878 | 0.9913 | 0.0 | 0.9709 | 0.9684 | 0.9749 | 0.9768 | | 0.0199 | 25.0 | 1250 | 0.0393 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9788 | 0.9885 | 0.9890 | 0.9897 | 0.0 | 0.9707 | 0.9683 | 0.9754 | 0.9776 | | 0.0191 | 26.0 | 1300 | 0.0387 | 0.7783 | 0.9865 | 0.9862 | nan | 0.9791 | 0.9878 | 0.9904 | 0.9887 | 0.0 | 0.9709 | 0.9683 | 0.9750 | 0.9775 | | 0.0191 | 27.0 | 1350 | 0.0384 | 0.7785 | 0.9865 | 0.9863 | nan | 0.9794 | 0.9880 | 0.9897 | 0.9890 | 0.0 | 0.9711 | 0.9685 | 0.9754 | 0.9775 | | 0.0188 | 28.0 | 1400 | 0.0383 | 0.7783 | 0.9865 | 0.9862 | nan | 0.9779 | 0.9893 | 0.9884 | 0.9903 | 0.0 | 0.9705 | 0.9682 | 0.9754 | 0.9776 | | 0.0188 | 29.0 | 1450 | 0.0377 | 0.7784 | 0.9864 | 0.9862 | nan | 0.9785 | 0.9902 | 0.9890 | 0.9880 | 0.0 | 0.9703 | 0.9680 | 0.9759 | 0.9775 | | 0.018 | 30.0 | 1500 | 0.0378 | 0.9732 | 0.9866 | 0.9863 | nan | 0.9794 | 0.9885 | 0.9888 | 0.9895 | nan | 0.9710 | 0.9683 | 0.9757 | 0.9777 | | 0.018 | 31.0 | 1550 | 0.0379 | 0.9730 | 0.9865 | 0.9862 | nan | 0.9794 | 0.9875 | 0.9901 | 0.9890 | nan | 0.9710 | 0.9681 | 0.9753 | 0.9776 | | 0.0175 | 32.0 | 1600 | 0.0381 | 0.9730 | 0.9865 | 0.9862 | nan | 0.9792 | 0.9884 | 0.9894 | 0.9889 | nan | 0.9708 | 0.9682 | 0.9755 | 0.9775 | | 0.0175 | 33.0 | 1650 | 0.0394 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9783 | 0.9896 | 0.9894 | 0.9886 | 0.0 | 0.9705 | 0.9679 | 0.9758 | 0.9777 | | 0.0171 | 34.0 | 1700 | 0.0390 | 0.7784 | 0.9865 | 0.9863 | nan | 0.9800 | 0.9871 | 0.9902 | 0.9887 | 0.0 | 0.9712 | 0.9682 | 0.9753 | 0.9775 | | 0.0171 | 35.0 | 1750 | 0.0385 | 0.9729 | 0.9865 | 0.9862 | nan | 0.9790 | 0.9878 | 0.9892 | 0.9899 | nan | 0.9710 | 0.9680 | 0.9754 | 0.9774 | | 0.0166 | 36.0 | 1800 | 0.0384 | 0.9731 | 0.9865 | 0.9863 | nan | 0.9791 | 0.9884 | 0.9889 | 0.9897 | nan | 0.9711 | 0.9682 | 0.9756 | 0.9775 | | 0.0166 | 37.0 | 1850 | 0.0389 | 0.9730 | 0.9865 | 0.9862 | nan | 0.9794 | 0.9875 | 0.9891 | 0.9898 | nan | 0.9711 | 0.9680 | 0.9754 | 0.9775 | | 0.0162 | 38.0 | 1900 | 0.0375 | 0.9731 | 0.9865 | 0.9863 | nan | 0.9797 | 0.9879 | 0.9901 | 0.9884 | nan | 0.9711 | 0.9681 | 0.9755 | 0.9777 | | 0.0162 | 39.0 | 1950 | 0.0389 | 0.9731 | 0.9866 | 0.9863 | nan | 0.9786 | 0.9891 | 0.9891 | 0.9894 | nan | 0.9709 | 0.9681 | 0.9759 | 0.9776 | | 0.0158 | 40.0 | 2000 | 0.0396 | 0.9730 | 0.9865 | 0.9862 | nan | 0.9783 | 0.9897 | 0.9894 | 0.9886 | nan | 0.9705 | 0.9678 | 0.9761 | 0.9777 | | 0.0158 | 41.0 | 2050 | 0.0397 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9788 | 0.9889 | 0.9887 | 0.9895 | 0.0 | 0.9708 | 0.9679 | 0.9759 | 0.9773 | | 0.0156 | 42.0 | 2100 | 0.0401 | 0.9730 | 0.9865 | 0.9862 | nan | 0.9782 | 0.9889 | 0.9890 | 0.9898 | nan | 0.9707 | 0.9678 | 0.9758 | 0.9775 | | 0.0156 | 43.0 | 2150 | 0.0399 | 0.9730 | 0.9865 | 0.9862 | nan | 0.9789 | 0.9886 | 0.9896 | 0.9889 | nan | 0.9708 | 0.9678 | 0.9757 | 0.9777 | | 0.0154 | 44.0 | 2200 | 0.0407 | 0.9728 | 0.9864 | 0.9861 | nan | 0.9781 | 0.9900 | 0.9884 | 0.9891 | nan | 0.9702 | 0.9673 | 0.9762 | 0.9776 | | 0.0154 | 45.0 | 2250 | 0.0405 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9785 | 0.9901 | 0.9896 | 0.9877 | 0.0 | 0.9706 | 0.9675 | 0.9761 | 0.9776 | | 0.0151 | 46.0 | 2300 | 0.0411 | 0.7782 | 0.9864 | 0.9861 | nan | 0.9784 | 0.9903 | 0.9901 | 0.9866 | 0.0 | 0.9704 | 0.9673 | 0.9758 | 0.9775 | | 0.0151 | 47.0 | 2350 | 0.0394 | 0.9732 | 0.9866 | 0.9863 | nan | 0.9790 | 0.9896 | 0.9890 | 0.9886 | nan | 0.9709 | 0.9681 | 0.9759 | 0.9777 | | 0.015 | 48.0 | 2400 | 0.0405 | 0.7784 | 0.9865 | 0.9863 | nan | 0.9787 | 0.9885 | 0.9892 | 0.9898 | 0.0 | 0.9708 | 0.9677 | 0.9757 | 0.9780 | | 0.015 | 49.0 | 2450 | 0.0399 | 0.7786 | 0.9866 | 0.9863 | nan | 0.9787 | 0.9905 | 0.9882 | 0.9888 | 0.0 | 0.9707 | 0.9678 | 0.9764 | 0.9779 | | 0.0149 | 50.0 | 2500 | 0.0410 | 0.7783 | 0.9864 | 0.9861 | nan | 0.9781 | 0.9895 | 0.9889 | 0.9891 | 0.0 | 0.9705 | 0.9673 | 0.9761 | 0.9776 | | 0.0149 | 51.0 | 2550 | 0.0405 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9793 | 0.9898 | 0.9895 | 0.9872 | 0.0 | 0.9707 | 0.9676 | 0.9763 | 0.9776 | | 0.0145 | 52.0 | 2600 | 0.0402 | 0.7785 | 0.9866 | 0.9863 | nan | 0.9788 | 0.9895 | 0.9893 | 0.9887 | 0.0 | 0.9710 | 0.9678 | 0.9760 | 0.9778 | | 0.0145 | 53.0 | 2650 | 0.0401 | 0.7786 | 0.9866 | 0.9864 | nan | 0.9791 | 0.9889 | 0.9898 | 0.9887 | 0.0 | 0.9710 | 0.9680 | 0.9761 | 0.9780 | | 0.0144 | 54.0 | 2700 | 0.0392 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9795 | 0.9888 | 0.9887 | 0.9896 | 0.0 | 0.9714 | 0.9682 | 0.9761 | 0.9777 | | 0.0144 | 55.0 | 2750 | 0.0409 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9787 | 0.9886 | 0.9895 | 0.9891 | 0.0 | 0.9706 | 0.9675 | 0.9760 | 0.9777 | | 0.0141 | 56.0 | 2800 | 0.0410 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9779 | 0.9897 | 0.9897 | 0.9887 | 0.0 | 0.9707 | 0.9675 | 0.9759 | 0.9778 | | 0.0141 | 57.0 | 2850 | 0.0412 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9780 | 0.9898 | 0.9891 | 0.9891 | 0.0 | 0.9707 | 0.9676 | 0.9761 | 0.9776 | | 0.014 | 58.0 | 2900 | 0.0403 | 0.9732 | 0.9866 | 0.9863 | nan | 0.9794 | 0.9889 | 0.9889 | 0.9891 | nan | 0.9713 | 0.9680 | 0.9761 | 0.9775 | | 0.014 | 59.0 | 2950 | 0.0404 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9787 | 0.9899 | 0.9889 | 0.9892 | 0.0 | 0.9711 | 0.9680 | 0.9763 | 0.9779 | | 0.0139 | 60.0 | 3000 | 0.0412 | 0.7783 | 0.9865 | 0.9862 | nan | 0.9786 | 0.9893 | 0.9900 | 0.9879 | 0.0 | 0.9708 | 0.9675 | 0.9758 | 0.9775 | | 0.0139 | 61.0 | 3050 | 0.0410 | 0.7785 | 0.9866 | 0.9863 | nan | 0.9789 | 0.9893 | 0.9901 | 0.9879 | 0.0 | 0.9708 | 0.9676 | 0.9762 | 0.9780 | | 0.0138 | 62.0 | 3100 | 0.0413 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9778 | 0.9896 | 0.9893 | 0.9894 | 0.0 | 0.9705 | 0.9675 | 0.9763 | 0.9779 | | 0.0138 | 63.0 | 3150 | 0.0400 | 0.7786 | 0.9866 | 0.9863 | nan | 0.9794 | 0.9887 | 0.9908 | 0.9874 | 0.0 | 0.9715 | 0.9681 | 0.9757 | 0.9776 | | 0.0138 | 64.0 | 3200 | 0.0401 | 0.7786 | 0.9866 | 0.9864 | nan | 0.9800 | 0.9888 | 0.9904 | 0.9873 | 0.0 | 0.9715 | 0.9682 | 0.9758 | 0.9776 | | 0.0138 | 65.0 | 3250 | 0.0414 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9788 | 0.9888 | 0.9905 | 0.9879 | 0.0 | 0.9708 | 0.9675 | 0.9759 | 0.9776 | | 0.0136 | 66.0 | 3300 | 0.0397 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9796 | 0.9895 | 0.9897 | 0.9880 | 0.0 | 0.9714 | 0.9683 | 0.9763 | 0.9776 | | 0.0136 | 67.0 | 3350 | 0.0417 | 0.7783 | 0.9864 | 0.9861 | nan | 0.9777 | 0.9894 | 0.9903 | 0.9884 | 0.0 | 0.9702 | 0.9671 | 0.9761 | 0.9779 | | 0.0135 | 68.0 | 3400 | 0.0409 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9790 | 0.9898 | 0.9908 | 0.9862 | 0.0 | 0.9711 | 0.9678 | 0.9758 | 0.9773 | | 0.0135 | 69.0 | 3450 | 0.0399 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9796 | 0.9896 | 0.9887 | 0.9888 | 0.0 | 0.9714 | 0.9681 | 0.9764 | 0.9778 | | 0.0133 | 70.0 | 3500 | 0.0407 | 0.7785 | 0.9865 | 0.9863 | nan | 0.9792 | 0.9903 | 0.9901 | 0.9866 | 0.0 | 0.9713 | 0.9676 | 0.9761 | 0.9775 | | 0.0133 | 71.0 | 3550 | 0.0407 | 0.7786 | 0.9866 | 0.9864 | nan | 0.9787 | 0.9896 | 0.9892 | 0.9890 | 0.0 | 0.9712 | 0.9679 | 0.9761 | 0.9778 | | 0.0131 | 72.0 | 3600 | 0.0394 | 0.7789 | 0.9868 | 0.9865 | nan | 0.9790 | 0.9899 | 0.9895 | 0.9887 | 0.0 | 0.9714 | 0.9681 | 0.9766 | 0.9781 | | 0.0131 | 73.0 | 3650 | 0.0410 | 0.7785 | 0.9865 | 0.9863 | nan | 0.9796 | 0.9897 | 0.9903 | 0.9865 | 0.0 | 0.9713 | 0.9678 | 0.9759 | 0.9774 | | 0.0132 | 74.0 | 3700 | 0.0412 | 0.7785 | 0.9866 | 0.9863 | nan | 0.9791 | 0.9900 | 0.9901 | 0.9871 | 0.0 | 0.9713 | 0.9678 | 0.9761 | 0.9774 | | 0.0132 | 75.0 | 3750 | 0.0412 | 0.7786 | 0.9866 | 0.9863 | nan | 0.9785 | 0.9902 | 0.9898 | 0.9879 | 0.0 | 0.9711 | 0.9676 | 0.9763 | 0.9779 | | 0.0131 | 76.0 | 3800 | 0.0396 | 0.7786 | 0.9866 | 0.9864 | nan | 0.9798 | 0.9893 | 0.9904 | 0.9870 | 0.0 | 0.9716 | 0.9682 | 0.9760 | 0.9775 | | 0.0131 | 77.0 | 3850 | 0.0418 | 0.7784 | 0.9865 | 0.9862 | nan | 0.9789 | 0.9896 | 0.9905 | 0.9871 | 0.0 | 0.9711 | 0.9676 | 0.9760 | 0.9775 | | 0.013 | 78.0 | 3900 | 0.0396 | 0.7786 | 0.9866 | 0.9864 | nan | 0.9787 | 0.9899 | 0.9906 | 0.9872 | 0.0 | 0.9713 | 0.9678 | 0.9760 | 0.9779 | | 0.013 | 79.0 | 3950 | 0.0398 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9794 | 0.9898 | 0.9905 | 0.9869 | 0.0 | 0.9715 | 0.9680 | 0.9762 | 0.9777 | | 0.0128 | 80.0 | 4000 | 0.0402 | 0.7788 | 0.9867 | 0.9865 | nan | 0.9789 | 0.9898 | 0.9896 | 0.9885 | 0.0 | 0.9714 | 0.9680 | 0.9765 | 0.9779 | | 0.0128 | 81.0 | 4050 | 0.0404 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9787 | 0.9903 | 0.9902 | 0.9874 | 0.0 | 0.9713 | 0.9677 | 0.9763 | 0.9779 | | 0.0127 | 82.0 | 4100 | 0.0397 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9794 | 0.9896 | 0.9901 | 0.9877 | 0.0 | 0.9716 | 0.9681 | 0.9762 | 0.9778 | | 0.0127 | 83.0 | 4150 | 0.0411 | 0.7786 | 0.9866 | 0.9863 | nan | 0.9786 | 0.9898 | 0.9899 | 0.9881 | 0.0 | 0.9712 | 0.9677 | 0.9763 | 0.9778 | | 0.0127 | 84.0 | 4200 | 0.0406 | 0.7788 | 0.9867 | 0.9865 | nan | 0.9787 | 0.9903 | 0.9890 | 0.9889 | 0.0 | 0.9713 | 0.9680 | 0.9766 | 0.9781 | | 0.0127 | 85.0 | 4250 | 0.0413 | 0.7786 | 0.9866 | 0.9864 | nan | 0.9787 | 0.9900 | 0.9888 | 0.9891 | 0.0 | 0.9711 | 0.9677 | 0.9764 | 0.9779 | | 0.0126 | 86.0 | 4300 | 0.0400 | 0.7788 | 0.9867 | 0.9865 | nan | 0.9792 | 0.9904 | 0.9895 | 0.9878 | 0.0 | 0.9715 | 0.9681 | 0.9765 | 0.9778 | | 0.0126 | 87.0 | 4350 | 0.0397 | 0.7788 | 0.9868 | 0.9865 | nan | 0.9789 | 0.9898 | 0.9898 | 0.9885 | 0.0 | 0.9715 | 0.9682 | 0.9765 | 0.9780 | | 0.0125 | 88.0 | 4400 | 0.0398 | 0.7788 | 0.9868 | 0.9865 | nan | 0.9791 | 0.9903 | 0.9894 | 0.9883 | 0.0 | 0.9716 | 0.9681 | 0.9767 | 0.9779 | | 0.0125 | 89.0 | 4450 | 0.0400 | 0.7787 | 0.9867 | 0.9865 | nan | 0.9795 | 0.9898 | 0.9902 | 0.9872 | 0.0 | 0.9716 | 0.9682 | 0.9763 | 0.9776 | | 0.0125 | 90.0 | 4500 | 0.0397 | 0.7788 | 0.9867 | 0.9865 | nan | 0.9788 | 0.9902 | 0.9893 | 0.9887 | 0.0 | 0.9716 | 0.9680 | 0.9765 | 0.9779 | | 0.0125 | 91.0 | 4550 | 0.0400 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9790 | 0.9901 | 0.9903 | 0.9875 | 0.0 | 0.9715 | 0.9680 | 0.9762 | 0.9779 | | 0.0125 | 92.0 | 4600 | 0.0392 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9790 | 0.9903 | 0.9898 | 0.9878 | 0.0 | 0.9716 | 0.9680 | 0.9765 | 0.9777 | | 0.0125 | 93.0 | 4650 | 0.0403 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9791 | 0.9900 | 0.9905 | 0.9873 | 0.0 | 0.9716 | 0.9681 | 0.9763 | 0.9777 | | 0.0123 | 94.0 | 4700 | 0.0396 | 0.7789 | 0.9868 | 0.9865 | nan | 0.9797 | 0.9898 | 0.9903 | 0.9874 | 0.0 | 0.9718 | 0.9684 | 0.9764 | 0.9778 | | 0.0123 | 95.0 | 4750 | 0.0405 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9790 | 0.9901 | 0.9903 | 0.9874 | 0.0 | 0.9715 | 0.9679 | 0.9764 | 0.9778 | | 0.0122 | 96.0 | 4800 | 0.0394 | 0.7789 | 0.9868 | 0.9865 | nan | 0.9793 | 0.9896 | 0.9898 | 0.9884 | 0.0 | 0.9717 | 0.9682 | 0.9764 | 0.9780 | | 0.0122 | 97.0 | 4850 | 0.0396 | 0.7789 | 0.9868 | 0.9865 | nan | 0.9790 | 0.9900 | 0.9900 | 0.9882 | 0.0 | 0.9716 | 0.9681 | 0.9766 | 0.9780 | | 0.0122 | 98.0 | 4900 | 0.0399 | 0.7788 | 0.9867 | 0.9865 | nan | 0.9797 | 0.9900 | 0.9904 | 0.9870 | 0.0 | 0.9718 | 0.9682 | 0.9764 | 0.9776 | | 0.0122 | 99.0 | 4950 | 0.0394 | 0.7789 | 0.9868 | 0.9865 | nan | 0.9793 | 0.9896 | 0.9897 | 0.9885 | 0.0 | 0.9717 | 0.9682 | 0.9766 | 0.9780 | | 0.0122 | 100.0 | 5000 | 0.0383 | 0.7790 | 0.9868 | 0.9866 | nan | 0.9804 | 0.9899 | 0.9895 | 0.9876 | 0.0 | 0.9720 | 0.9686 | 0.9767 | 0.9777 | | 0.0122 | 101.0 | 5050 | 0.0399 | 0.7788 | 0.9867 | 0.9865 | nan | 0.9794 | 0.9904 | 0.9895 | 0.9877 | 0.0 | 0.9716 | 0.9680 | 0.9766 | 0.9779 | | 0.0121 | 102.0 | 5100 | 0.0392 | 0.7790 | 0.9868 | 0.9866 | nan | 0.9796 | 0.9898 | 0.9889 | 0.9890 | 0.0 | 0.9718 | 0.9685 | 0.9767 | 0.9779 | | 0.0121 | 103.0 | 5150 | 0.0393 | 0.7788 | 0.9867 | 0.9865 | nan | 0.9788 | 0.9901 | 0.9900 | 0.9881 | 0.0 | 0.9715 | 0.9679 | 0.9765 | 0.9781 | | 0.012 | 104.0 | 5200 | 0.0400 | 0.7788 | 0.9867 | 0.9865 | nan | 0.9790 | 0.9894 | 0.9904 | 0.9881 | 0.0 | 0.9716 | 0.9682 | 0.9763 | 0.9779 | | 0.012 | 105.0 | 5250 | 0.0393 | 0.7789 | 0.9868 | 0.9865 | nan | 0.9796 | 0.9894 | 0.9904 | 0.9878 | 0.0 | 0.9718 | 0.9683 | 0.9764 | 0.9780 | | 0.012 | 106.0 | 5300 | 0.0390 | 0.7789 | 0.9868 | 0.9866 | nan | 0.9794 | 0.9900 | 0.9890 | 0.9888 | 0.0 | 0.9719 | 0.9683 | 0.9766 | 0.9780 | | 0.012 | 107.0 | 5350 | 0.0383 | 0.7790 | 0.9868 | 0.9866 | nan | 0.9801 | 0.9899 | 0.9903 | 0.9870 | 0.0 | 0.9721 | 0.9684 | 0.9765 | 0.9779 | | 0.0119 | 108.0 | 5400 | 0.0380 | 0.7792 | 0.9870 | 0.9868 | nan | 0.9807 | 0.9892 | 0.9897 | 0.9883 | 0.0 | 0.9724 | 0.9690 | 0.9768 | 0.9780 | | 0.0119 | 109.0 | 5450 | 0.0400 | 0.7787 | 0.9867 | 0.9864 | nan | 0.9786 | 0.9902 | 0.9902 | 0.9876 | 0.0 | 0.9714 | 0.9677 | 0.9764 | 0.9778 | | 0.0119 | 110.0 | 5500 | 0.0385 | 0.7791 | 0.9869 | 0.9867 | nan | 0.9801 | 0.9894 | 0.9891 | 0.9889 | 0.0 | 0.9721 | 0.9686 | 0.9768 | 0.9780 | | 0.0119 | 111.0 | 5550 | 0.0385 | 0.7790 | 0.9869 | 0.9866 | nan | 0.9798 | 0.9896 | 0.9902 | 0.9879 | 0.0 | 0.9719 | 0.9685 | 0.9767 | 0.9781 | | 0.0118 | 112.0 | 5600 | 0.0377 | 0.7791 | 0.9869 | 0.9867 | nan | 0.9798 | 0.9891 | 0.9897 | 0.9891 | 0.0 | 0.9722 | 0.9687 | 0.9766 | 0.9782 | | 0.0118 | 113.0 | 5650 | 0.0388 | 0.7790 | 0.9869 | 0.9866 | nan | 0.9794 | 0.9899 | 0.9904 | 0.9878 | 0.0 | 0.9719 | 0.9683 | 0.9767 | 0.9781 | | 0.0118 | 114.0 | 5700 | 0.0391 | 0.7789 | 0.9868 | 0.9866 | nan | 0.9797 | 0.9891 | 0.9906 | 0.9880 | 0.0 | 0.9719 | 0.9683 | 0.9763 | 0.9781 | | 0.0118 | 115.0 | 5750 | 0.0390 | 0.7789 | 0.9868 | 0.9866 | nan | 0.9796 | 0.9902 | 0.9899 | 0.9876 | 0.0 | 0.9719 | 0.9683 | 0.9766 | 0.9779 | | 0.0118 | 116.0 | 5800 | 0.0390 | 0.7789 | 0.9868 | 0.9866 | nan | 0.9795 | 0.9899 | 0.9896 | 0.9882 | 0.0 | 0.9718 | 0.9682 | 0.9767 | 0.9779 | | 0.0118 | 117.0 | 5850 | 0.0394 | 0.7788 | 0.9867 | 0.9865 | nan | 0.9791 | 0.9899 | 0.9896 | 0.9883 | 0.0 | 0.9717 | 0.9679 | 0.9765 | 0.9778 | | 0.0117 | 118.0 | 5900 | 0.0386 | 0.7789 | 0.9868 | 0.9866 | nan | 0.9796 | 0.9898 | 0.9900 | 0.9879 | 0.0 | 0.9719 | 0.9682 | 0.9766 | 0.9779 | | 0.0117 | 119.0 | 5950 | 0.0386 | 0.7791 | 0.9869 | 0.9867 | nan | 0.9800 | 0.9895 | 0.9896 | 0.9885 | 0.0 | 0.9721 | 0.9686 | 0.9767 | 0.9781 | | 0.0117 | 120.0 | 6000 | 0.0388 | 0.7790 | 0.9869 | 0.9866 | nan | 0.9796 | 0.9899 | 0.9902 | 0.9878 | 0.0 | 0.9719 | 0.9684 | 0.9767 | 0.9781 | | 0.0117 | 121.0 | 6050 | 0.0389 | 0.7790 | 0.9868 | 0.9866 | nan | 0.9800 | 0.9896 | 0.9894 | 0.9883 | 0.0 | 0.9721 | 0.9684 | 0.9767 | 0.9778 | | 0.0116 | 122.0 | 6100 | 0.0384 | 0.7790 | 0.9869 | 0.9866 | nan | 0.9796 | 0.9896 | 0.9897 | 0.9886 | 0.0 | 0.9720 | 0.9684 | 0.9767 | 0.9780 | | 0.0116 | 123.0 | 6150 | 0.0386 | 0.7789 | 0.9868 | 0.9865 | nan | 0.9793 | 0.9899 | 0.9901 | 0.9879 | 0.0 | 0.9718 | 0.9680 | 0.9765 | 0.9781 | | 0.0115 | 124.0 | 6200 | 0.0383 | 0.7792 | 0.9870 | 0.9867 | nan | 0.9802 | 0.9890 | 0.9900 | 0.9888 | 0.0 | 0.9722 | 0.9688 | 0.9767 | 0.9781 | | 0.0115 | 125.0 | 6250 | 0.0381 | 0.7790 | 0.9869 | 0.9866 | nan | 0.9796 | 0.9892 | 0.9900 | 0.9888 | 0.0 | 0.9721 | 0.9685 | 0.9766 | 0.9780 | | 0.0115 | 126.0 | 6300 | 0.0383 | 0.7791 | 0.9869 | 0.9867 | nan | 0.9797 | 0.9894 | 0.9894 | 0.9893 | 0.0 | 0.9720 | 0.9686 | 0.9767 | 0.9782 | | 0.0115 | 127.0 | 6350 | 0.0384 | 0.7790 | 0.9869 | 0.9866 | nan | 0.9797 | 0.9895 | 0.9901 | 0.9881 | 0.0 | 0.9719 | 0.9684 | 0.9766 | 0.9781 | | 0.0115 | 128.0 | 6400 | 0.0377 | 0.7792 | 0.9870 | 0.9867 | nan | 0.9801 | 0.9891 | 0.9896 | 0.9891 | 0.0 | 0.9722 | 0.9688 | 0.9767 | 0.9781 | | 0.0115 | 129.0 | 6450 | 0.0383 | 0.7791 | 0.9869 | 0.9867 | nan | 0.9800 | 0.9898 | 0.9899 | 0.9880 | 0.0 | 0.9721 | 0.9685 | 0.9768 | 0.9782 | | 0.0115 | 130.0 | 6500 | 0.0377 | 0.7791 | 0.9870 | 0.9867 | nan | 0.9797 | 0.9895 | 0.9901 | 0.9885 | 0.0 | 0.9723 | 0.9687 | 0.9767 | 0.9781 | | 0.0115 | 131.0 | 6550 | 0.0380 | 0.7791 | 0.9869 | 0.9867 | nan | 0.9800 | 0.9891 | 0.9897 | 0.9890 | 0.0 | 0.9722 | 0.9687 | 0.9767 | 0.9780 | | 0.0114 | 132.0 | 6600 | 0.0377 | 0.7792 | 0.9870 | 0.9868 | nan | 0.9799 | 0.9893 | 0.9901 | 0.9887 | 0.0 | 0.9724 | 0.9689 | 0.9766 | 0.9782 | | 0.0114 | 133.0 | 6650 | 0.0378 | 0.7792 | 0.9870 | 0.9867 | nan | 0.9801 | 0.9899 | 0.9897 | 0.9882 | 0.0 | 0.9722 | 0.9687 | 0.9769 | 0.9782 | | 0.0114 | 134.0 | 6700 | 0.0379 | 0.7791 | 0.9869 | 0.9867 | nan | 0.9801 | 0.9896 | 0.9902 | 0.9879 | 0.0 | 0.9723 | 0.9688 | 0.9767 | 0.9780 | | 0.0114 | 135.0 | 6750 | 0.0374 | 0.7793 | 0.9870 | 0.9868 | nan | 0.9803 | 0.9894 | 0.9899 | 0.9884 | 0.0 | 0.9724 | 0.9690 | 0.9768 | 0.9782 | | 0.0113 | 136.0 | 6800 | 0.0386 | 0.7790 | 0.9869 | 0.9866 | nan | 0.9796 | 0.9897 | 0.9903 | 0.9878 | 0.0 | 0.9720 | 0.9683 | 0.9766 | 0.9781 | | 0.0113 | 137.0 | 6850 | 0.0378 | 0.9739 | 0.9870 | 0.9867 | nan | 0.9802 | 0.9895 | 0.9900 | 0.9880 | nan | 0.9724 | 0.9688 | 0.9766 | 0.9779 | | 0.0114 | 138.0 | 6900 | 0.0378 | 0.9740 | 0.9870 | 0.9868 | nan | 0.9800 | 0.9895 | 0.9893 | 0.9893 | nan | 0.9722 | 0.9688 | 0.9769 | 0.9783 | | 0.0114 | 139.0 | 6950 | 0.0380 | 0.7791 | 0.9869 | 0.9867 | nan | 0.9797 | 0.9896 | 0.9897 | 0.9888 | 0.0 | 0.9722 | 0.9685 | 0.9767 | 0.9782 | | 0.0113 | 140.0 | 7000 | 0.0374 | 0.7793 | 0.9871 | 0.9868 | nan | 0.9803 | 0.9893 | 0.9899 | 0.9887 | 0.0 | 0.9725 | 0.9690 | 0.9768 | 0.9783 | | 0.0113 | 141.0 | 7050 | 0.0378 | 0.7792 | 0.9870 | 0.9868 | nan | 0.9801 | 0.9894 | 0.9900 | 0.9886 | 0.0 | 0.9724 | 0.9689 | 0.9767 | 0.9781 | | 0.0112 | 142.0 | 7100 | 0.0380 | 0.9740 | 0.9870 | 0.9868 | nan | 0.9801 | 0.9899 | 0.9897 | 0.9882 | nan | 0.9724 | 0.9687 | 0.9768 | 0.9782 | | 0.0112 | 143.0 | 7150 | 0.0380 | 0.9740 | 0.9870 | 0.9868 | nan | 0.9800 | 0.9897 | 0.9899 | 0.9883 | nan | 0.9724 | 0.9688 | 0.9768 | 0.9781 | | 0.0112 | 144.0 | 7200 | 0.0378 | 0.9741 | 0.9870 | 0.9868 | nan | 0.9802 | 0.9896 | 0.9897 | 0.9887 | nan | 0.9725 | 0.9690 | 0.9768 | 0.9781 | | 0.0112 | 145.0 | 7250 | 0.0376 | 0.7793 | 0.9870 | 0.9868 | nan | 0.9806 | 0.9892 | 0.9903 | 0.9880 | 0.0 | 0.9726 | 0.9690 | 0.9767 | 0.9782 | | 0.0112 | 146.0 | 7300 | 0.0380 | 0.7792 | 0.9870 | 0.9867 | nan | 0.9801 | 0.9899 | 0.9898 | 0.9880 | 0.0 | 0.9724 | 0.9687 | 0.9767 | 0.9780 | | 0.0112 | 147.0 | 7350 | 0.0381 | 0.9740 | 0.9870 | 0.9867 | nan | 0.9800 | 0.9900 | 0.9899 | 0.9880 | nan | 0.9723 | 0.9687 | 0.9767 | 0.9781 | | 0.0111 | 148.0 | 7400 | 0.0374 | 0.9742 | 0.9871 | 0.9868 | nan | 0.9805 | 0.9895 | 0.9900 | 0.9883 | nan | 0.9726 | 0.9690 | 0.9768 | 0.9782 | | 0.0111 | 149.0 | 7450 | 0.0378 | 0.9740 | 0.9870 | 0.9868 | nan | 0.9801 | 0.9897 | 0.9902 | 0.9879 | nan | 0.9724 | 0.9687 | 0.9767 | 0.9781 | | 0.0112 | 150.0 | 7500 | 0.0377 | 0.9741 | 0.9870 | 0.9868 | nan | 0.9800 | 0.9892 | 0.9897 | 0.9891 | nan | 0.9725 | 0.9690 | 0.9767 | 0.9781 | | 0.0112 | 151.0 | 7550 | 0.0377 | 0.9742 | 0.9871 | 0.9868 | nan | 0.9802 | 0.9893 | 0.9895 | 0.9893 | nan | 0.9725 | 0.9691 | 0.9768 | 0.9782 | | 0.0111 | 152.0 | 7600 | 0.0374 | 0.9741 | 0.9870 | 0.9868 | nan | 0.9804 | 0.9898 | 0.9898 | 0.9883 | nan | 0.9726 | 0.9690 | 0.9768 | 0.9782 | | 0.0111 | 153.0 | 7650 | 0.0380 | 0.9740 | 0.9870 | 0.9868 | nan | 0.9800 | 0.9898 | 0.9897 | 0.9884 | nan | 0.9725 | 0.9688 | 0.9767 | 0.9781 | | 0.0111 | 154.0 | 7700 | 0.0373 | 0.9742 | 0.9871 | 0.9869 | nan | 0.9805 | 0.9891 | 0.9901 | 0.9887 | nan | 0.9727 | 0.9692 | 0.9767 | 0.9782 | | 0.0111 | 155.0 | 7750 | 0.0375 | 0.9742 | 0.9871 | 0.9868 | nan | 0.9804 | 0.9896 | 0.9893 | 0.9891 | nan | 0.9727 | 0.9692 | 0.9768 | 0.9781 | | 0.0111 | 156.0 | 7800 | 0.0378 | 0.9741 | 0.9870 | 0.9868 | nan | 0.9801 | 0.9898 | 0.9897 | 0.9886 | nan | 0.9727 | 0.9689 | 0.9767 | 0.9781 | | 0.0111 | 157.0 | 7850 | 0.0376 | 0.9742 | 0.9871 | 0.9868 | nan | 0.9805 | 0.9891 | 0.9902 | 0.9885 | nan | 0.9727 | 0.9691 | 0.9766 | 0.9782 | | 0.0111 | 158.0 | 7900 | 0.0375 | 0.9742 | 0.9871 | 0.9868 | nan | 0.9804 | 0.9893 | 0.9899 | 0.9887 | nan | 0.9727 | 0.9691 | 0.9767 | 0.9782 | | 0.0111 | 159.0 | 7950 | 0.0372 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9805 | 0.9892 | 0.9904 | 0.9884 | nan | 0.9728 | 0.9693 | 0.9766 | 0.9783 | | 0.0111 | 160.0 | 8000 | 0.0367 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9809 | 0.9896 | 0.9898 | 0.9882 | nan | 0.9730 | 0.9693 | 0.9768 | 0.9782 | | 0.0111 | 161.0 | 8050 | 0.0370 | 0.9744 | 0.9871 | 0.9869 | nan | 0.9808 | 0.9898 | 0.9894 | 0.9886 | nan | 0.9728 | 0.9693 | 0.9770 | 0.9783 | | 0.0111 | 162.0 | 8100 | 0.0371 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9806 | 0.9892 | 0.9901 | 0.9885 | nan | 0.9729 | 0.9694 | 0.9767 | 0.9782 | | 0.0111 | 163.0 | 8150 | 0.0372 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9807 | 0.9894 | 0.9901 | 0.9882 | nan | 0.9729 | 0.9694 | 0.9767 | 0.9781 | | 0.011 | 164.0 | 8200 | 0.0373 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9805 | 0.9896 | 0.9894 | 0.9889 | nan | 0.9728 | 0.9693 | 0.9768 | 0.9781 | | 0.011 | 165.0 | 8250 | 0.0371 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9808 | 0.9897 | 0.9898 | 0.9882 | nan | 0.9729 | 0.9694 | 0.9768 | 0.9783 | | 0.011 | 166.0 | 8300 | 0.0372 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9806 | 0.9897 | 0.9898 | 0.9884 | nan | 0.9729 | 0.9693 | 0.9768 | 0.9781 | | 0.011 | 167.0 | 8350 | 0.0373 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9804 | 0.9896 | 0.9900 | 0.9885 | nan | 0.9728 | 0.9692 | 0.9768 | 0.9783 | | 0.011 | 168.0 | 8400 | 0.0369 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9807 | 0.9895 | 0.9899 | 0.9885 | nan | 0.9731 | 0.9695 | 0.9767 | 0.9782 | | 0.011 | 169.0 | 8450 | 0.0375 | 0.9742 | 0.9871 | 0.9869 | nan | 0.9802 | 0.9897 | 0.9898 | 0.9886 | nan | 0.9727 | 0.9691 | 0.9768 | 0.9782 | | 0.0109 | 170.0 | 8500 | 0.0363 | 0.9746 | 0.9873 | 0.9871 | nan | 0.9814 | 0.9892 | 0.9894 | 0.9891 | nan | 0.9734 | 0.9699 | 0.9769 | 0.9782 | | 0.0109 | 171.0 | 8550 | 0.0371 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9805 | 0.9895 | 0.9900 | 0.9885 | nan | 0.9729 | 0.9693 | 0.9767 | 0.9782 | | 0.011 | 172.0 | 8600 | 0.0371 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9807 | 0.9896 | 0.9898 | 0.9885 | nan | 0.9729 | 0.9693 | 0.9768 | 0.9782 | | 0.011 | 173.0 | 8650 | 0.0373 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9807 | 0.9893 | 0.9901 | 0.9885 | nan | 0.9728 | 0.9694 | 0.9767 | 0.9783 | | 0.0109 | 174.0 | 8700 | 0.0372 | 0.9744 | 0.9872 | 0.9869 | nan | 0.9806 | 0.9894 | 0.9898 | 0.9889 | nan | 0.9729 | 0.9694 | 0.9768 | 0.9783 | | 0.0109 | 175.0 | 8750 | 0.0373 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9806 | 0.9895 | 0.9899 | 0.9885 | nan | 0.9729 | 0.9694 | 0.9768 | 0.9782 | | 0.0109 | 176.0 | 8800 | 0.0371 | 0.9744 | 0.9872 | 0.9869 | nan | 0.9808 | 0.9894 | 0.9898 | 0.9886 | nan | 0.9730 | 0.9694 | 0.9768 | 0.9782 | | 0.0109 | 177.0 | 8850 | 0.0370 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9808 | 0.9897 | 0.9896 | 0.9886 | nan | 0.9730 | 0.9695 | 0.9768 | 0.9782 | | 0.0109 | 178.0 | 8900 | 0.0373 | 0.9744 | 0.9872 | 0.9869 | nan | 0.9808 | 0.9895 | 0.9899 | 0.9885 | nan | 0.9729 | 0.9694 | 0.9768 | 0.9783 | | 0.0109 | 179.0 | 8950 | 0.0372 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9806 | 0.9894 | 0.9897 | 0.9888 | nan | 0.9729 | 0.9694 | 0.9768 | 0.9782 | | 0.0109 | 180.0 | 9000 | 0.0368 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9811 | 0.9894 | 0.9897 | 0.9885 | nan | 0.9731 | 0.9696 | 0.9768 | 0.9781 | | 0.0109 | 181.0 | 9050 | 0.0371 | 0.9744 | 0.9872 | 0.9869 | nan | 0.9807 | 0.9894 | 0.9900 | 0.9886 | nan | 0.9730 | 0.9694 | 0.9768 | 0.9783 | | 0.0109 | 182.0 | 9100 | 0.0370 | 0.9744 | 0.9872 | 0.9869 | nan | 0.9808 | 0.9894 | 0.9898 | 0.9887 | nan | 0.9730 | 0.9695 | 0.9768 | 0.9782 | | 0.0109 | 183.0 | 9150 | 0.0368 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9810 | 0.9892 | 0.9901 | 0.9885 | nan | 0.9732 | 0.9697 | 0.9767 | 0.9782 | | 0.0108 | 184.0 | 9200 | 0.0373 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9805 | 0.9896 | 0.9897 | 0.9887 | nan | 0.9729 | 0.9693 | 0.9767 | 0.9782 | | 0.0108 | 185.0 | 9250 | 0.0371 | 0.9743 | 0.9872 | 0.9869 | nan | 0.9806 | 0.9895 | 0.9900 | 0.9885 | nan | 0.9730 | 0.9694 | 0.9767 | 0.9783 | | 0.0108 | 186.0 | 9300 | 0.0371 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9808 | 0.9897 | 0.9896 | 0.9886 | nan | 0.9731 | 0.9695 | 0.9769 | 0.9782 | | 0.0108 | 187.0 | 9350 | 0.0371 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9808 | 0.9895 | 0.9899 | 0.9886 | nan | 0.9731 | 0.9695 | 0.9768 | 0.9782 | | 0.0108 | 188.0 | 9400 | 0.0370 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9808 | 0.9893 | 0.9900 | 0.9886 | nan | 0.9730 | 0.9695 | 0.9768 | 0.9782 | | 0.0108 | 189.0 | 9450 | 0.0371 | 0.9743 | 0.9872 | 0.9869 | nan | 0.9807 | 0.9895 | 0.9901 | 0.9883 | nan | 0.9730 | 0.9694 | 0.9767 | 0.9782 | | 0.0108 | 190.0 | 9500 | 0.0370 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9808 | 0.9896 | 0.9899 | 0.9885 | nan | 0.9731 | 0.9695 | 0.9768 | 0.9782 | | 0.0108 | 191.0 | 9550 | 0.0373 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9806 | 0.9895 | 0.9899 | 0.9886 | nan | 0.9729 | 0.9693 | 0.9768 | 0.9782 | | 0.0108 | 192.0 | 9600 | 0.0371 | 0.9744 | 0.9872 | 0.9869 | nan | 0.9807 | 0.9894 | 0.9899 | 0.9887 | nan | 0.9730 | 0.9695 | 0.9768 | 0.9782 | | 0.0108 | 193.0 | 9650 | 0.0374 | 0.9743 | 0.9871 | 0.9869 | nan | 0.9805 | 0.9898 | 0.9897 | 0.9886 | nan | 0.9729 | 0.9693 | 0.9768 | 0.9782 | | 0.0108 | 194.0 | 9700 | 0.0371 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9807 | 0.9896 | 0.9899 | 0.9885 | nan | 0.9730 | 0.9695 | 0.9768 | 0.9783 | | 0.0108 | 195.0 | 9750 | 0.0370 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9809 | 0.9896 | 0.9898 | 0.9885 | nan | 0.9731 | 0.9696 | 0.9768 | 0.9782 | | 0.0108 | 196.0 | 9800 | 0.0370 | 0.9745 | 0.9872 | 0.9870 | nan | 0.9810 | 0.9894 | 0.9898 | 0.9887 | nan | 0.9732 | 0.9697 | 0.9768 | 0.9782 | | 0.0108 | 197.0 | 9850 | 0.0371 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9809 | 0.9896 | 0.9897 | 0.9886 | nan | 0.9731 | 0.9695 | 0.9768 | 0.9782 | | 0.0108 | 198.0 | 9900 | 0.0371 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9809 | 0.9896 | 0.9897 | 0.9886 | nan | 0.9731 | 0.9695 | 0.9768 | 0.9782 | | 0.0108 | 199.0 | 9950 | 0.0371 | 0.9744 | 0.9872 | 0.9870 | nan | 0.9809 | 0.9895 | 0.9898 | 0.9886 | nan | 0.9731 | 0.9696 | 0.9768 | 0.9782 | | 0.0108 | 200.0 | 10000 | 0.0372 | 0.9744 | 0.9872 | 0.9869 | nan | 0.9806 | 0.9893 | 0.9901 | 0.9887 | nan | 0.9730 | 0.9695 | 0.9767 | 0.9782 | ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

Sachleen/fintune_new

Sachleen

"2025-04-19T10:05:33Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/orpheus-3b-0.1-pretrained", "base_model:adapter:unsloth/orpheus-3b-0.1-pretrained", "region:us" ]

null

"2025-04-19T10:01:11Z"

--- base_model: unsloth/orpheus-3b-0.1-pretrained library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1

mlfoundations-dev/b1_code_top_16

mlfoundations-dev

"2025-04-19T10:05:00Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T03:15:19Z"

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b1_code_top_16 results: [] ---  # b1_code_top_16 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b1_code_top_16 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.0.2 - Tokenizers 0.20.3

hanzogak/comradeshipLU

hanzogak

"2025-04-19T09:51:27Z"

0

null

[ "anime", "merge", "text-to-image", "base_model:OnomaAIResearch/Illustrious-Lumina-v0.03", "base_model:finetune:OnomaAIResearch/Illustrious-Lumina-v0.03", "license:apache-2.0", "region:us" ]

text-to-image

"2025-04-19T09:36:59Z"

--- license: apache-2.0 base_model: - OnomaAIResearch/Illustrious-Lumina-v0.03 pipeline_tag: text-to-image tags: - anime - merge --- Comradeship LU ============= 전우애는 계속되어야 한다. ## comradeshipLU-v1T2 This is a merged anime model based on Illustrious-Lumina-v0.03. This is a test merged model. Illustrious-Lumina-v0.03 + ((LeX-Lumina - Lumina-Image-2.0) × 0.6) = Comradeship LU v1T2

linoyts/dog-hidream-lora

linoyts

"2025-04-19T09:50:12Z"

0

diffusers

[ "diffusers", "text-to-image", "diffusers-training", "lora", "hidream", "hidream-diffusers", "template:sd-lora", "base_model:HiDream-ai/HiDream-I1-Full", "base_model:adapter:HiDream-ai/HiDream-I1-Full", "license:mit", "region:us" ]

text-to-image

"2025-04-19T06:42:42Z"

--- base_model: HiDream-ai/HiDream-I1-Full library_name: diffusers license: mit instance_prompt: a photo of sks dog widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - hidream - hidream-diffusers - template:sd-lora - text-to-image - diffusers-training - diffusers - lora - hidream - hidream-diffusers - template:sd-lora ---  # HiDream Image DreamBooth LoRA - linoyts/dog-hidream-lora <Gallery /> ## Model description These are linoyts/dog-hidream-lora DreamBooth LoRA weights for HiDream-ai/HiDream-I1-Full. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [HiDream Image diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_hidream.md). ## Trigger words You should use `a photo of sks dog` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](linoyts/dog-hidream-lora/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py >>> import torch >>> from transformers import PreTrainedTokenizerFast, LlamaForCausalLM >>> from diffusers import UniPCMultistepScheduler, HiDreamImagePipeline >>> scheduler = UniPCMultistepScheduler( ... flow_shift=3.0, prediction_type="flow_prediction", use_flow_sigmas=True ... ) >>> tokenizer_4 = PreTrainedTokenizerFast.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") >>> text_encoder_4 = LlamaForCausalLM.from_pretrained( ... "meta-llama/Meta-Llama-3.1-8B-Instruct", ... output_hidden_states=True, ... output_attentions=True, ... torch_dtype=torch.bfloat16, ... ) >>> pipe = HiDreamImagePipeline.from_pretrained( ... "HiDream-ai/HiDream-I1-Full", ... scheduler=scheduler, ... tokenizer_4=tokenizer_4, ... text_encoder_4=text_encoder_4, ... torch_dtype=torch.bfloat16, ... ) >>> pipe.enable_model_cpu_offload() >>> pipe.load_lora_weights(f"linoyts/dog-hidream-lora") >>> image = pipe(f"a photo of sks dog").images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

LlamaQwenDeepSeek/Qwen2.5-1.5B-scierc

LlamaQwenDeepSeek

"2025-04-19T09:49:23Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-04-19T09:47:40Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

TareksTesting/Alkahest-V6-LLaMa-70B

TareksTesting

"2025-04-19T09:47:32Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "base_model:TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B", "base_model:merge:TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B", "base_model:TareksLab/Malediction-V2-LLaMa-70B", "base_model:merge:TareksLab/Malediction-V2-LLaMa-70B", "base_model:TareksLab/Stylizer-V2b-LLaMa-70B", "base_model:merge:TareksLab/Stylizer-V2b-LLaMa-70B", "base_model:TareksLab/Wordsmith-V9-LLaMa-70B", "base_model:merge:TareksLab/Wordsmith-V9-LLaMa-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T09:12:45Z"

--- base_model: - TareksLab/Malediction-V2-LLaMa-70B - TareksLab/Wordsmith-V9-LLaMa-70B - TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B - TareksLab/Stylizer-V2b-LLaMa-70B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [TareksLab/Stylizer-V2b-LLaMa-70B](https://huggingface.co/TareksLab/Stylizer-V2b-LLaMa-70B) as a base. ### Models Merged The following models were included in the merge: * [TareksLab/Malediction-V2-LLaMa-70B](https://huggingface.co/TareksLab/Malediction-V2-LLaMa-70B) * [TareksLab/Wordsmith-V9-LLaMa-70B](https://huggingface.co/TareksLab/Wordsmith-V9-LLaMa-70B) * [TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B](https://huggingface.co/TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TareksLab/Wordsmith-V9-LLaMa-70B parameters: weight: 0.25 density: 0.5 - model: TareksLab/Malediction-V2-LLaMa-70B parameters: weight: 0.25 density: 0.5 - model: TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B parameters: weight: 0.25 density: 0.5 - model: TareksLab/Stylizer-V2b-LLaMa-70B parameters: weight: 0.25 density: 0.5 merge_method: dare_ties base_model: TareksLab/Stylizer-V2b-LLaMa-70B parameters: normalize: false out_dtype: bfloat16 chat_template: llama3 tokenizer: source: base ```

sunnnil/Smanhwa

sunnnil

"2025-04-19T09:46:29Z"

0

null

[ "license:other", "region:us" ]

null

"2025-04-18T10:20:39Z"

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

dazzlinggopi/gopikPEFT_expo

dazzlinggopi

"2025-04-19T09:46:14Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "dataset:samsum", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2025-04-19T09:44:32Z"

--- library_name: transformers license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer datasets: - samsum model-index: - name: gopikPEFT_expo results: [] ---  # gopikPEFT_expo This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 0.2669 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9187 | 1.0 | 19 | 0.2777 | | 0.1883 | 2.0 | 38 | 0.2669 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

mlfoundations-dev/b1_science_top_8_10k

mlfoundations-dev

"2025-04-19T09:41:16Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T04:45:18Z"

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b1_science_top_8_10k results: [] ---  # b1_science_top_8_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b1_science_top_8_10k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

raveas/klue-roberta-base-klue-sts

raveas

"2025-04-19T09:37:22Z"

0

sentence-transformers

[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

"2025-04-19T09:37:05Z"

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 657 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors

Hartunka/tiny_bert_km_20_v2

Hartunka

"2025-04-19T09:28:12Z"

0

null

[ "safetensors", "distilbert", "generated_from_trainer", "dataset:Hartunka/processed_wikitext-103-raw-v1-km-20_v2", "model-index", "region:us" ]

null

"2025-04-13T16:24:27Z"

--- tags: - generated_from_trainer datasets: - Hartunka/processed_wikitext-103-raw-v1-km-20_v2 metrics: - accuracy model-index: - name: tiny_bert_km_20_v2 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: Hartunka/processed_wikitext-103-raw-v1-km-20_v2 type: Hartunka/processed_wikitext-103-raw-v1-km-20_v2 metrics: - name: Accuracy type: accuracy value: 0.15406566084647116 ---  # tiny_bert_km_20_v2 This model is a fine-tuned version of [](https://huggingface.co/) on the Hartunka/processed_wikitext-103-raw-v1-km-20_v2 dataset. It achieves the following results on the evaluation set: - Loss: 6.5066 - Accuracy: 0.1541 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 96 - eval_batch_size: 96 - seed: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:-----:|:---------------:|:--------:| | 6.6514 | 4.1982 | 10000 | 6.7571 | 0.1485 | | 6.326 | 8.3963 | 20000 | 6.5497 | 0.1526 | | 6.1671 | 12.5945 | 30000 | 6.6530 | 0.1544 | | 6.0706 | 16.7926 | 40000 | 6.6305 | 0.1514 | | 6.0123 | 20.9908 | 50000 | 6.7075 | 0.1511 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.19.1

sony-fashion-photography/riq

sony-fashion-photography

"2025-04-19T09:27:14Z"

5

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

"2025-04-07T11:10:21Z"

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Liza --- # Riq <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Liza` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Liza", "lora_weights": "https://huggingface.co/sony-fashion-photography/riq/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('sony-fashion-photography/riq', weight_name='lora.safetensors') image = pipeline('Liza').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/sony-fashion-photography/riq/discussions) to add images that show off what you’ve made with this LoRA.

mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF

mradermacher

"2025-04-19T09:26:22Z"

207

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.x_70b_SmarTracks_v1.30_flat", "base_model:quantized:Nexesenex/Llama_3.x_70b_SmarTracks_v1.30_flat", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-01T09:32:37Z"

--- base_model: Nexesenex/Llama_3.x_70b_SmarTracks_v1.30_flat language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/Nexesenex/Llama_3.x_70b_SmarTracks_v1.30_flat  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q2_K.gguf) | Q2_K | 26.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q3_K_S.gguf) | Q3_K_S | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q3_K_L.gguf) | Q3_K_L | 37.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.IQ4_XS.gguf) | IQ4_XS | 38.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q5_K_S.gguf) | Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q5_K_M.gguf) | Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality | | [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_SmarTracks_v1.30_flat-GGUF/resolve/main/Llama_3.x_70b_SmarTracks_v1.30_flat.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

rbelanec/train_sst2_1744902628

rbelanec

"2025-04-19T09:23:18Z"

0

peft

[ "peft", "safetensors", "llama-factory", "lntuning", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]

null

"2025-04-18T23:52:50Z"

--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - llama-factory - lntuning - generated_from_trainer model-index: - name: train_sst2_1744902628 results: [] ---  # train_sst2_1744902628 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0728 - Num Input Tokens Seen: 33458560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:-------:|:-----:|:---------------:|:-----------------:| | 0.1539 | 0.0528 | 200 | 0.1842 | 166688 | | 0.1944 | 0.1056 | 400 | 0.1516 | 334048 | | 0.0708 | 0.1584 | 600 | 0.1392 | 500448 | | 0.098 | 0.2112 | 800 | 0.1304 | 667872 | | 0.1267 | 0.2640 | 1000 | 0.1269 | 834848 | | 0.1179 | 0.3167 | 1200 | 0.1228 | 1002816 | | 0.0794 | 0.3695 | 1400 | 0.1210 | 1169088 | | 0.2142 | 0.4223 | 1600 | 0.1180 | 1337088 | | 0.1079 | 0.4751 | 1800 | 0.1159 | 1505536 | | 0.1278 | 0.5279 | 2000 | 0.1137 | 1673024 | | 0.1395 | 0.5807 | 2200 | 0.1122 | 1842304 | | 0.1163 | 0.6335 | 2400 | 0.1105 | 2007328 | | 0.0823 | 0.6863 | 2600 | 0.1093 | 2174880 | | 0.1669 | 0.7391 | 2800 | 0.1076 | 2341280 | | 0.1197 | 0.7919 | 3000 | 0.1067 | 2509440 | | 0.1089 | 0.8447 | 3200 | 0.1046 | 2674784 | | 0.088 | 0.8975 | 3400 | 0.1030 | 2843680 | | 0.1537 | 0.9502 | 3600 | 0.1062 | 3011904 | | 0.0942 | 1.0029 | 3800 | 0.1014 | 3178064 | | 0.1005 | 1.0557 | 4000 | 0.1001 | 3345904 | | 0.0968 | 1.1085 | 4200 | 0.0992 | 3514608 | | 0.0908 | 1.1613 | 4400 | 0.0987 | 3680560 | | 0.0681 | 1.2141 | 4600 | 0.0979 | 3849328 | | 0.1266 | 1.2669 | 4800 | 0.0978 | 4017200 | | 0.08 | 1.3197 | 5000 | 0.0961 | 4187184 | | 0.0749 | 1.3724 | 5200 | 0.0955 | 4354416 | | 0.1066 | 1.4252 | 5400 | 0.0947 | 4519856 | | 0.1032 | 1.4780 | 5600 | 0.0941 | 4687280 | | 0.0682 | 1.5308 | 5800 | 0.0934 | 4856112 | | 0.05 | 1.5836 | 6000 | 0.0938 | 5022736 | | 0.1597 | 1.6364 | 6200 | 0.0919 | 5188656 | | 0.0547 | 1.6892 | 6400 | 0.0913 | 5356208 | | 0.1362 | 1.7420 | 6600 | 0.0909 | 5523952 | | 0.1347 | 1.7948 | 6800 | 0.0901 | 5690672 | | 0.0949 | 1.8476 | 7000 | 0.0896 | 5857072 | | 0.0551 | 1.9004 | 7200 | 0.0889 | 6024976 | | 0.0835 | 1.9531 | 7400 | 0.0890 | 6191664 | | 0.0984 | 2.0058 | 7600 | 0.0882 | 6357472 | | 0.1026 | 2.0586 | 7800 | 0.0882 | 6525984 | | 0.0955 | 2.1114 | 8000 | 0.0871 | 6692320 | | 0.0731 | 2.1642 | 8200 | 0.0870 | 6860064 | | 0.1487 | 2.2170 | 8400 | 0.0870 | 7026528 | | 0.0494 | 2.2698 | 8600 | 0.0868 | 7192384 | | 0.1057 | 2.3226 | 8800 | 0.0859 | 7358816 | | 0.0801 | 2.3753 | 9000 | 0.0857 | 7526496 | | 0.0757 | 2.4281 | 9200 | 0.0852 | 7696064 | | 0.0889 | 2.4809 | 9400 | 0.0851 | 7863456 | | 0.1005 | 2.5337 | 9600 | 0.0845 | 8031776 | | 0.0785 | 2.5865 | 9800 | 0.0843 | 8199584 | | 0.0925 | 2.6393 | 10000 | 0.0836 | 8366016 | | 0.0698 | 2.6921 | 10200 | 0.0839 | 8531808 | | 0.0657 | 2.7449 | 10400 | 0.0830 | 8702976 | | 0.0471 | 2.7977 | 10600 | 0.0829 | 8870944 | | 0.0946 | 2.8505 | 10800 | 0.0830 | 9039680 | | 0.1393 | 2.9033 | 11000 | 0.0829 | 9206880 | | 0.0655 | 2.9561 | 11200 | 0.0823 | 9372128 | | 0.0833 | 3.0087 | 11400 | 0.0818 | 9538768 | | 0.0782 | 3.0615 | 11600 | 0.0815 | 9705232 | | 0.0579 | 3.1143 | 11800 | 0.0814 | 9871632 | | 0.0707 | 3.1671 | 12000 | 0.0814 | 10039472 | | 0.0276 | 3.2199 | 12200 | 0.0809 | 10206320 | | 0.0665 | 3.2727 | 12400 | 0.0807 | 10376240 | | 0.0816 | 3.3255 | 12600 | 0.0807 | 10544464 | | 0.0344 | 3.3782 | 12800 | 0.0801 | 10712240 | | 0.044 | 3.4310 | 13000 | 0.0801 | 10879120 | | 0.0479 | 3.4838 | 13200 | 0.0800 | 11045072 | | 0.0631 | 3.5366 | 13400 | 0.0800 | 11211312 | | 0.0876 | 3.5894 | 13600 | 0.0795 | 11378128 | | 0.0434 | 3.6422 | 13800 | 0.0800 | 11544592 | | 0.1373 | 3.6950 | 14000 | 0.0790 | 11713040 | | 0.1293 | 3.7478 | 14200 | 0.0794 | 11880432 | | 0.115 | 3.8006 | 14400 | 0.0788 | 12048176 | | 0.0573 | 3.8534 | 14600 | 0.0790 | 12215792 | | 0.0487 | 3.9062 | 14800 | 0.0789 | 12383792 | | 0.0535 | 3.9590 | 15000 | 0.0786 | 12549680 | | 0.0956 | 4.0116 | 15200 | 0.0782 | 12716448 | | 0.0484 | 4.0644 | 15400 | 0.0781 | 12882752 | | 0.0618 | 4.1172 | 15600 | 0.0780 | 13051200 | | 0.0581 | 4.1700 | 15800 | 0.0777 | 13217024 | | 0.0639 | 4.2228 | 16000 | 0.0776 | 13382784 | | 0.0619 | 4.2756 | 16200 | 0.0781 | 13549216 | | 0.0358 | 4.3284 | 16400 | 0.0772 | 13719072 | | 0.1304 | 4.3812 | 16600 | 0.0771 | 13884928 | | 0.0876 | 4.4339 | 16800 | 0.0768 | 14051584 | | 0.0492 | 4.4867 | 17000 | 0.0781 | 14220704 | | 0.045 | 4.5395 | 17200 | 0.0766 | 14387008 | | 0.1129 | 4.5923 | 17400 | 0.0768 | 14555808 | | 0.0602 | 4.6451 | 17600 | 0.0766 | 14723456 | | 0.1302 | 4.6979 | 17800 | 0.0764 | 14890880 | | 0.0634 | 4.7507 | 18000 | 0.0769 | 15059744 | | 0.0675 | 4.8035 | 18200 | 0.0763 | 15224512 | | 0.12 | 4.8563 | 18400 | 0.0761 | 15392960 | | 0.0468 | 4.9091 | 18600 | 0.0759 | 15561696 | | 0.0968 | 4.9619 | 18800 | 0.0763 | 15728800 | | 0.0793 | 5.0145 | 19000 | 0.0758 | 15897552 | | 0.0726 | 5.0673 | 19200 | 0.0757 | 16064688 | | 0.0647 | 5.1201 | 19400 | 0.0754 | 16231120 | | 0.0802 | 5.1729 | 19600 | 0.0755 | 16397744 | | 0.1297 | 5.2257 | 19800 | 0.0753 | 16564176 | | 0.0624 | 5.2785 | 20000 | 0.0752 | 16731600 | | 0.0329 | 5.3313 | 20200 | 0.0756 | 16898064 | | 0.0914 | 5.3841 | 20400 | 0.0753 | 17064080 | | 0.0583 | 5.4368 | 20600 | 0.0751 | 17231888 | | 0.0622 | 5.4896 | 20800 | 0.0750 | 17399184 | | 0.0676 | 5.5424 | 21000 | 0.0758 | 17566160 | | 0.0667 | 5.5952 | 21200 | 0.0748 | 17732304 | | 0.0507 | 5.6480 | 21400 | 0.0750 | 17900880 | | 0.0453 | 5.7008 | 21600 | 0.0747 | 18070192 | | 0.0833 | 5.7536 | 21800 | 0.0748 | 18237168 | | 0.0535 | 5.8064 | 22000 | 0.0748 | 18403856 | | 0.1257 | 5.8592 | 22200 | 0.0745 | 18571248 | | 0.0289 | 5.9120 | 22400 | 0.0747 | 18738672 | | 0.0504 | 5.9648 | 22600 | 0.0747 | 18905744 | | 0.0855 | 6.0174 | 22800 | 0.0744 | 19073440 | | 0.0699 | 6.0702 | 23000 | 0.0744 | 19241920 | | 0.1241 | 6.1230 | 23200 | 0.0745 | 19409408 | | 0.077 | 6.1758 | 23400 | 0.0751 | 19577024 | | 0.0498 | 6.2286 | 23600 | 0.0741 | 19744608 | | 0.0814 | 6.2814 | 23800 | 0.0742 | 19911488 | | 0.0741 | 6.3342 | 24000 | 0.0741 | 20078944 | | 0.0561 | 6.3870 | 24200 | 0.0740 | 20244928 | | 0.0998 | 6.4398 | 24400 | 0.0741 | 20411232 | | 0.0599 | 6.4925 | 24600 | 0.0740 | 20578080 | | 0.0745 | 6.5453 | 24800 | 0.0737 | 20746592 | | 0.1089 | 6.5981 | 25000 | 0.0741 | 20913344 | | 0.0357 | 6.6509 | 25200 | 0.0738 | 21081952 | | 0.0983 | 6.7037 | 25400 | 0.0739 | 21248384 | | 0.0928 | 6.7565 | 25600 | 0.0738 | 21415872 | | 0.0561 | 6.8093 | 25800 | 0.0740 | 21584000 | | 0.1221 | 6.8621 | 26000 | 0.0736 | 21751168 | | 0.0501 | 6.9149 | 26200 | 0.0737 | 21918816 | | 0.0735 | 6.9677 | 26400 | 0.0735 | 22084384 | | 0.073 | 7.0203 | 26600 | 0.0737 | 22251776 | | 0.0831 | 7.0731 | 26800 | 0.0736 | 22418080 | | 0.0576 | 7.1259 | 27000 | 0.0735 | 22587392 | | 0.0622 | 7.1787 | 27200 | 0.0736 | 22753056 | | 0.0731 | 7.2315 | 27400 | 0.0738 | 22920768 | | 0.0822 | 7.2843 | 27600 | 0.0734 | 23087296 | | 0.0392 | 7.3371 | 27800 | 0.0735 | 23254400 | | 0.0331 | 7.3899 | 28000 | 0.0733 | 23422752 | | 0.0444 | 7.4427 | 28200 | 0.0734 | 23588352 | | 0.0614 | 7.4954 | 28400 | 0.0737 | 23755840 | | 0.0677 | 7.5482 | 28600 | 0.0734 | 23923680 | | 0.0489 | 7.6010 | 28800 | 0.0734 | 24091168 | | 0.0393 | 7.6538 | 29000 | 0.0735 | 24258016 | | 0.0912 | 7.7066 | 29200 | 0.0733 | 24427808 | | 0.0217 | 7.7594 | 29400 | 0.0734 | 24596288 | | 0.0513 | 7.8122 | 29600 | 0.0730 | 24764192 | | 0.0658 | 7.8650 | 29800 | 0.0732 | 24932000 | | 0.0394 | 7.9178 | 30000 | 0.0731 | 25100224 | | 0.0558 | 7.9706 | 30200 | 0.0735 | 25267808 | | 0.0496 | 8.0232 | 30400 | 0.0733 | 25433440 | | 0.0734 | 8.0760 | 30600 | 0.0733 | 25600672 | | 0.0612 | 8.1288 | 30800 | 0.0731 | 25769408 | | 0.0413 | 8.1816 | 31000 | 0.0728 | 25936160 | | 0.0626 | 8.2344 | 31200 | 0.0731 | 26103744 | | 0.0785 | 8.2872 | 31400 | 0.0731 | 26270560 | | 0.0716 | 8.3400 | 31600 | 0.0732 | 26437536 | | 0.0325 | 8.3928 | 31800 | 0.0732 | 26604480 | | 0.0484 | 8.4456 | 32000 | 0.0732 | 26771680 | | 0.1124 | 8.4984 | 32200 | 0.0730 | 26940256 | | 0.0738 | 8.5511 | 32400 | 0.0730 | 27107680 | | 0.0793 | 8.6039 | 32600 | 0.0731 | 27274048 | | 0.1138 | 8.6567 | 32800 | 0.0731 | 27440544 | | 0.1 | 8.7095 | 33000 | 0.0731 | 27608000 | | 0.03 | 8.7623 | 33200 | 0.0732 | 27776704 | | 0.0178 | 8.8151 | 33400 | 0.0728 | 27942752 | | 0.0735 | 8.8679 | 33600 | 0.0729 | 28108864 | | 0.032 | 8.9207 | 33800 | 0.0728 | 28275296 | | 0.0549 | 8.9735 | 34000 | 0.0730 | 28443520 | | 0.0322 | 9.0261 | 34200 | 0.0730 | 28609776 | | 0.0633 | 9.0789 | 34400 | 0.0729 | 28777712 | | 0.1346 | 9.1317 | 34600 | 0.0729 | 28944144 | | 0.0417 | 9.1845 | 34800 | 0.0732 | 29111152 | | 0.0991 | 9.2373 | 35000 | 0.0731 | 29278000 | | 0.043 | 9.2901 | 35200 | 0.0729 | 29443792 | | 0.0353 | 9.3429 | 35400 | 0.0729 | 29609072 | | 0.1 | 9.3957 | 35600 | 0.0730 | 29776592 | | 0.0774 | 9.4485 | 35800 | 0.0730 | 29941616 | | 0.0649 | 9.5013 | 36000 | 0.0729 | 30110160 | | 0.0702 | 9.5540 | 36200 | 0.0730 | 30277744 | | 0.1259 | 9.6068 | 36400 | 0.0729 | 30447152 | | 0.0281 | 9.6596 | 36600 | 0.0729 | 30612976 | | 0.0457 | 9.7124 | 36800 | 0.0730 | 30780240 | | 0.0235 | 9.7652 | 37000 | 0.0728 | 30948048 | | 0.0478 | 9.8180 | 37200 | 0.0730 | 31116368 | | 0.0338 | 9.8708 | 37400 | 0.0729 | 31283888 | | 0.0387 | 9.9236 | 37600 | 0.0730 | 31452560 | | 0.0603 | 9.9764 | 37800 | 0.0728 | 31620720 | | 0.1036 | 10.0290 | 38000 | 0.0730 | 31786016 | | 0.0954 | 10.0818 | 38200 | 0.0728 | 31952768 | | 0.0774 | 10.1346 | 38400 | 0.0728 | 32120320 | | 0.0159 | 10.1874 | 38600 | 0.0728 | 32287584 | | 0.0459 | 10.2402 | 38800 | 0.0730 | 32455072 | | 0.0525 | 10.2930 | 39000 | 0.0729 | 32621184 | | 0.0389 | 10.3458 | 39200 | 0.0730 | 32788960 | | 0.0456 | 10.3986 | 39400 | 0.0729 | 32955776 | | 0.0359 | 10.4514 | 39600 | 0.0728 | 33122816 | | 0.0964 | 10.5042 | 39800 | 0.0729 | 33291072 | | 0.0656 | 10.5569 | 40000 | 0.0728 | 33458560 | ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

MercuraTech/v4_articles_single_base

MercuraTech

"2025-04-19T09:19:44Z"

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-04-19T04:28:45Z"

--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: v4_articles_single_base results: [] ---  # v4_articles_single_base This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7167 - Accuracy: 0.4397 - F1: 0.4350 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:| | 9.9789 | 0.1895 | 500 | 9.9718 | 0.0001 | 0.0000 | | 9.8369 | 0.3791 | 1000 | 9.7965 | 0.0094 | 0.0002 | | 9.5592 | 0.5686 | 1500 | 9.5071 | 0.0094 | 0.0002 | | 9.237 | 0.7582 | 2000 | 9.1586 | 0.0094 | 0.0002 | | 8.8972 | 0.9477 | 2500 | 8.8566 | 0.0094 | 0.0002 | | 8.6564 | 1.1372 | 3000 | 8.6478 | 0.0160 | 0.0019 | | 8.4797 | 1.3268 | 3500 | 8.4819 | 0.0246 | 0.0046 | | 8.3484 | 1.5163 | 4000 | 8.3282 | 0.0317 | 0.0064 | | 8.2146 | 1.7058 | 4500 | 8.1572 | 0.0340 | 0.0068 | | 8.0264 | 1.8954 | 5000 | 7.9514 | 0.0455 | 0.0112 | | 7.7606 | 2.0849 | 5500 | 7.7213 | 0.0583 | 0.0177 | | 7.4704 | 2.2745 | 6000 | 7.4514 | 0.0838 | 0.0287 | | 7.2206 | 2.4640 | 6500 | 7.1248 | 0.1120 | 0.0452 | | 6.8785 | 2.6535 | 7000 | 6.7901 | 0.1367 | 0.0610 | | 6.5434 | 2.8431 | 7500 | 6.4326 | 0.1625 | 0.0772 | | 6.1765 | 3.0326 | 8000 | 6.0874 | 0.1800 | 0.0911 | | 5.8391 | 3.2221 | 8500 | 5.7638 | 0.2015 | 0.1086 | | 5.4916 | 3.4117 | 9000 | 5.4800 | 0.2161 | 0.1224 | | 5.3123 | 3.6012 | 9500 | 5.2343 | 0.2268 | 0.1327 | | 5.0068 | 3.7908 | 10000 | 5.0158 | 0.2416 | 0.1460 | | 4.8917 | 3.9803 | 10500 | 4.8260 | 0.2518 | 0.1572 | | 4.5999 | 4.1698 | 11000 | 4.6644 | 0.2668 | 0.1714 | | 4.5399 | 4.3594 | 11500 | 4.5127 | 0.2752 | 0.1824 | | 4.2681 | 4.5489 | 12000 | 4.3847 | 0.2841 | 0.1909 | | 4.2411 | 4.7384 | 12500 | 4.2655 | 0.2917 | 0.1999 | | 4.0436 | 4.9280 | 13000 | 4.1556 | 0.2996 | 0.2096 | | 3.8549 | 5.1175 | 13500 | 4.0580 | 0.3090 | 0.2198 | | 3.8365 | 5.3071 | 14000 | 3.9771 | 0.3157 | 0.2266 | | 3.7002 | 5.4966 | 14500 | 3.8831 | 0.3225 | 0.2368 | | 3.6145 | 5.6861 | 15000 | 3.8118 | 0.3313 | 0.2461 | | 3.5779 | 5.8757 | 15500 | 3.7317 | 0.3384 | 0.2570 | | 3.4283 | 6.0652 | 16000 | 3.6797 | 0.3390 | 0.2603 | | 3.3538 | 6.2547 | 16500 | 3.6148 | 0.3463 | 0.2692 | | 3.3319 | 6.4443 | 17000 | 3.5629 | 0.3511 | 0.2749 | | 3.226 | 6.6338 | 17500 | 3.5067 | 0.3564 | 0.2814 | | 3.2061 | 6.8234 | 18000 | 3.4567 | 0.3604 | 0.2869 | | 3.1053 | 7.0129 | 18500 | 3.4042 | 0.3675 | 0.2957 | | 3.0195 | 7.2024 | 19000 | 3.3702 | 0.3704 | 0.3014 | | 2.9741 | 7.3920 | 19500 | 3.3274 | 0.3755 | 0.3074 | | 2.9456 | 7.5815 | 20000 | 3.2985 | 0.3761 | 0.3086 | | 2.9216 | 7.7710 | 20500 | 3.2658 | 0.3772 | 0.3119 | | 2.8645 | 7.9606 | 21000 | 3.2231 | 0.3847 | 0.3226 | | 2.7615 | 8.1501 | 21500 | 3.2023 | 0.3899 | 0.3265 | | 2.7581 | 8.3397 | 22000 | 3.1769 | 0.3878 | 0.3273 | | 2.7612 | 8.5292 | 22500 | 3.1357 | 0.3936 | 0.3370 | | 2.656 | 8.7187 | 23000 | 3.1208 | 0.3893 | 0.3372 | | 2.6204 | 8.9083 | 23500 | 3.0876 | 0.3973 | 0.3440 | | 2.5629 | 9.0978 | 24000 | 3.0708 | 0.3957 | 0.3435 | | 2.5407 | 9.2873 | 24500 | 3.0475 | 0.4014 | 0.3564 | | 2.501 | 9.4769 | 25000 | 3.0425 | 0.4007 | 0.3512 | | 2.4615 | 9.6664 | 25500 | 3.0077 | 0.4064 | 0.3592 | | 2.4667 | 9.8560 | 26000 | 2.9950 | 0.4061 | 0.3634 | | 2.3594 | 10.0455 | 26500 | 2.9875 | 0.4048 | 0.3661 | | 2.3613 | 10.2350 | 27000 | 2.9587 | 0.4056 | 0.3727 | | 2.3253 | 10.4246 | 27500 | 2.9467 | 0.4110 | 0.3751 | | 2.332 | 10.6141 | 28000 | 2.9342 | 0.4114 | 0.3734 | | 2.2866 | 10.8036 | 28500 | 2.9034 | 0.4138 | 0.3830 | | 2.2932 | 10.9932 | 29000 | 2.8993 | 0.4117 | 0.3822 | | 2.2165 | 11.1827 | 29500 | 2.8904 | 0.4199 | 0.3865 | | 2.1911 | 11.3723 | 30000 | 2.8893 | 0.4145 | 0.3858 | | 2.1368 | 11.5618 | 30500 | 2.8658 | 0.4212 | 0.3951 | | 2.147 | 11.7513 | 31000 | 2.8640 | 0.4144 | 0.3911 | | 2.0725 | 11.9409 | 31500 | 2.8407 | 0.4203 | 0.3978 | | 2.071 | 12.1304 | 32000 | 2.8350 | 0.4237 | 0.4005 | | 2.0455 | 12.3199 | 32500 | 2.8318 | 0.4233 | 0.3999 | | 2.02 | 12.5095 | 33000 | 2.8176 | 0.4256 | 0.4033 | | 2.0375 | 12.6990 | 33500 | 2.8144 | 0.4264 | 0.4063 | | 1.9853 | 12.8886 | 34000 | 2.7982 | 0.4290 | 0.4075 | | 1.9396 | 13.0781 | 34500 | 2.7921 | 0.4271 | 0.4120 | | 1.9214 | 13.2676 | 35000 | 2.7846 | 0.4261 | 0.4100 | | 1.9103 | 13.4572 | 35500 | 2.7845 | 0.4246 | 0.4099 | | 1.9422 | 13.6467 | 36000 | 2.7822 | 0.4285 | 0.4112 | | 1.9098 | 13.8362 | 36500 | 2.7708 | 0.4290 | 0.4130 | | 1.8087 | 14.0258 | 37000 | 2.7687 | 0.4320 | 0.4177 | | 1.7799 | 14.2153 | 37500 | 2.7529 | 0.4326 | 0.4176 | | 1.7517 | 14.4049 | 38000 | 2.7543 | 0.4345 | 0.4218 | | 1.8091 | 14.5944 | 38500 | 2.7533 | 0.4347 | 0.4215 | | 1.8129 | 14.7839 | 39000 | 2.7444 | 0.4330 | 0.4230 | | 1.777 | 14.9735 | 39500 | 2.7382 | 0.4370 | 0.4284 | | 1.6449 | 15.1630 | 40000 | 2.7459 | 0.4344 | 0.4240 | | 1.7006 | 15.3525 | 40500 | 2.7225 | 0.4375 | 0.4312 | | 1.7103 | 15.5421 | 41000 | 2.7314 | 0.4402 | 0.4308 | | 1.7152 | 15.7316 | 41500 | 2.7247 | 0.4401 | 0.4331 | | 1.7274 | 15.9212 | 42000 | 2.7218 | 0.4388 | 0.4310 | | 1.6366 | 16.1107 | 42500 | 2.7167 | 0.4397 | 0.4350 | | 1.6787 | 16.3002 | 43000 | 2.6995 | 0.4425 | 0.4375 | | 1.5951 | 16.4898 | 43500 | 2.7195 | 0.4390 | 0.4313 | | 1.6202 | 16.6793 | 44000 | 2.7076 | 0.4406 | 0.4358 | | 1.6674 | 16.8688 | 44500 | 2.7015 | 0.4414 | 0.4360 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

summerstars/Solara

summerstars

"2025-04-19T09:17:30Z"

2

0

transformers.js

[ "transformers.js", "safetensors", "llama", "text-generation", "onnx", "conversational", "en", "base_model:HuggingFaceTB/SmolLM2-360M-Instruct", "base_model:finetune:HuggingFaceTB/SmolLM2-360M-Instruct", "license:apache-2.0", "region:us" ]

text-generation

"2025-04-19T05:17:38Z"

--- license: apache-2.0 base_model: - HuggingFaceTB/SmolLM2-360M-Instruct language: - en pipeline_tag: text-generation tags: - safetensors - onnx - transformers.js --- # 🌞 Solara — summerstars/Solara ## **Created by a High School Student | Built on Google Colab (T4 GPU)** ## **高校生によって開発 | Google Colab（T4 GPU）で作成** **Solara** is a lightweight, instruction-tuned language model based on [`HuggingFaceTB/SmolLM2-360M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct). It was developed by a high school student using Google Colab with a T4 GPU. Despite its compact size, Solara delivers quick responses and handles everyday tasks efficiently. **Solara（ソララ）** は、[`HuggingFaceTB/SmolLM2-360M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) をベースとした軽量な指示応答型言語モデルです。 Google Colab（T4 GPU）を使用して高校生が開発しました。小型ながら、日常のタスクや会話を効率的かつ高速に処理します。 --- ## 📌 Model Details / モデル詳細 - **Base Model / ベースモデル**: HuggingFaceTB/SmolLM2-360M-Instruct - **Parameters / パラメータ数**: 360M - **Architecture / アーキテクチャ**: Decoder-only Transformer / デコーダ専用トランスフォーマー - **Languages / 対応言語**: English / 英語 - **License / ライセンス**: Apache 2.0 --- ## 🚀 Use Cases / 主な用途 - Lightweight chatbots / 軽量チャットボット - Inference on CPUs or mobile devices / CPU・モバイル端末での推論 - Educational or hobbyist projects / 教育・趣味用途 - Instruction-following tasks / 指示応答タスク --- ## 🛠️ How to Use / 使用方法 ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "summerstars/Solara" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) prompt = "Please explain black holes in simple terms." inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=128) # Print the result / 結果を表示 print(tokenizer.decode(outputs[0], skip_special_tokens=True))

shibajustfor/802ae234-5e4c-4ba5-8809-0a981f51f7ba

shibajustfor

"2025-04-19T09:11:32Z"

0

transformers

[ "transformers", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

"2025-04-19T09:11:18Z"

--- library_name: transformers model_name: shibajustfor/802ae234-5e4c-4ba5-8809-0a981f51f7ba tags: - generated_from_trainer licence: license --- # Model Card for shibajustfor/802ae234-5e4c-4ba5-8809-0a981f51f7ba This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ### Framework versions - TRL: 0.12.0 - Transformers: 4.46.3 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

ahmet71cakir/phi4-turbochat-full

ahmet71cakir

"2025-04-19T09:11:28Z"

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-18T18:14:56Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF

mradermacher

"2025-04-19T09:10:26Z"

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:NexesMess/Llama_3.x_70b_Triads_V1", "base_model:quantized:NexesMess/Llama_3.x_70b_Triads_V1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2025-04-18T08:45:09Z"

--- base_model: NexesMess/Llama_3.x_70b_Triads_V1 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      weighted/imatrix quants of https://huggingface.co/NexesMess/Llama_3.x_70b_Triads_V1  static quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 24.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q4_1.gguf) | i1-Q4_1 | 44.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.1 | | | [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Triads_V1-i1-GGUF/resolve/main/Llama_3.x_70b_Triads_V1.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

umar141/llama3.2_Baro_v2

umar141

"2025-04-19T09:07:17Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2025-04-19T09:07:06Z"

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** umar141 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

rbelanec/train_qnli_1744902616

rbelanec

"2025-04-19T09:05:59Z"

3

0

peft

[ "peft", "safetensors", "llama-factory", "lntuning", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]

null

"2025-04-18T18:05:23Z"

--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - llama-factory - lntuning - generated_from_trainer model-index: - name: train_qnli_1744902616 results: [] ---  # train_qnli_1744902616 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the qnli dataset. It achieves the following results on the evaluation set: - Loss: 0.0513 - Num Input Tokens Seen: 74724160 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:------:|:-----:|:---------------:|:-----------------:| | 0.1529 | 0.0339 | 200 | 0.1279 | 375872 | | 0.1128 | 0.0679 | 400 | 0.1065 | 754656 | | 0.0909 | 0.1018 | 600 | 0.1009 | 1127296 | | 0.1101 | 0.1358 | 800 | 0.0961 | 1500832 | | 0.0937 | 0.1697 | 1000 | 0.0936 | 1870752 | | 0.0823 | 0.2037 | 1200 | 0.0897 | 2248448 | | 0.1123 | 0.2376 | 1400 | 0.0871 | 2622784 | | 0.0786 | 0.2716 | 1600 | 0.0847 | 2995616 | | 0.0674 | 0.3055 | 1800 | 0.0829 | 3370144 | | 0.0745 | 0.3395 | 2000 | 0.0811 | 3747936 | | 0.074 | 0.3734 | 2200 | 0.0795 | 4126560 | | 0.0795 | 0.4073 | 2400 | 0.0788 | 4497920 | | 0.068 | 0.4413 | 2600 | 0.0770 | 4870432 | | 0.0948 | 0.4752 | 2800 | 0.0759 | 5242976 | | 0.0751 | 0.5092 | 3000 | 0.0748 | 5615808 | | 0.0722 | 0.5431 | 3200 | 0.0738 | 5984672 | | 0.0915 | 0.5771 | 3400 | 0.0733 | 6356832 | | 0.0585 | 0.6110 | 3600 | 0.0729 | 6732928 | | 0.0577 | 0.6450 | 3800 | 0.0723 | 7111456 | | 0.0511 | 0.6789 | 4000 | 0.0714 | 7481824 | | 0.0664 | 0.7129 | 4200 | 0.0702 | 7857440 | | 0.0703 | 0.7468 | 4400 | 0.0699 | 8229632 | | 0.0676 | 0.7808 | 4600 | 0.0689 | 8601824 | | 0.0665 | 0.8147 | 4800 | 0.0683 | 8974688 | | 0.0851 | 0.8486 | 5000 | 0.0682 | 9345088 | | 0.0874 | 0.8826 | 5200 | 0.0673 | 9720928 | | 0.063 | 0.9165 | 5400 | 0.0667 | 10090976 | | 0.0652 | 0.9505 | 5600 | 0.0681 | 10461824 | | 0.0807 | 0.9844 | 5800 | 0.0660 | 10837568 | | 0.0644 | 1.0183 | 6000 | 0.0662 | 11211008 | | 0.0679 | 1.0523 | 6200 | 0.0649 | 11582528 | | 0.0594 | 1.0862 | 6400 | 0.0647 | 11958208 | | 0.0808 | 1.1202 | 6600 | 0.0645 | 12334752 | | 0.0535 | 1.1541 | 6800 | 0.0650 | 12710176 | | 0.0547 | 1.1881 | 7000 | 0.0650 | 13083200 | | 0.0692 | 1.2220 | 7200 | 0.0632 | 13458944 | | 0.0571 | 1.2560 | 7400 | 0.0629 | 13836256 | | 0.0874 | 1.2899 | 7600 | 0.0630 | 14209248 | | 0.0752 | 1.3238 | 7800 | 0.0626 | 14585344 | | 0.0605 | 1.3578 | 8000 | 0.0623 | 14955328 | | 0.0412 | 1.3917 | 8200 | 0.0616 | 15331776 | | 0.0396 | 1.4257 | 8400 | 0.0615 | 15706624 | | 0.0307 | 1.4596 | 8600 | 0.0619 | 16075392 | | 0.0412 | 1.4936 | 8800 | 0.0610 | 16445568 | | 0.0981 | 1.5275 | 9000 | 0.0609 | 16819648 | | 0.0615 | 1.5615 | 9200 | 0.0605 | 17191872 | | 0.0829 | 1.5954 | 9400 | 0.0611 | 17561280 | | 0.0505 | 1.6294 | 9600 | 0.0598 | 17936128 | | 0.0755 | 1.6633 | 9800 | 0.0598 | 18307616 | | 0.0499 | 1.6972 | 10000 | 0.0593 | 18683168 | | 0.0665 | 1.7312 | 10200 | 0.0595 | 19053408 | | 0.0485 | 1.7651 | 10400 | 0.0591 | 19427296 | | 0.0618 | 1.7991 | 10600 | 0.0589 | 19802400 | | 0.0495 | 1.8330 | 10800 | 0.0586 | 20173056 | | 0.054 | 1.8670 | 11000 | 0.0585 | 20550720 | | 0.0792 | 1.9009 | 11200 | 0.0589 | 20920224 | | 0.0688 | 1.9349 | 11400 | 0.0581 | 21289344 | | 0.0484 | 1.9688 | 11600 | 0.0580 | 21666048 | | 0.0698 | 2.0027 | 11800 | 0.0603 | 22041760 | | 0.0501 | 2.0367 | 12000 | 0.0576 | 22412256 | | 0.0569 | 2.0706 | 12200 | 0.0573 | 22782848 | | 0.048 | 2.1046 | 12400 | 0.0574 | 23151392 | | 0.0453 | 2.1385 | 12600 | 0.0571 | 23523648 | | 0.0656 | 2.1724 | 12800 | 0.0569 | 23892992 | | 0.0461 | 2.2064 | 13000 | 0.0571 | 24264192 | | 0.0471 | 2.2403 | 13200 | 0.0567 | 24635264 | | 0.0702 | 2.2743 | 13400 | 0.0564 | 25009664 | | 0.0558 | 2.3082 | 13600 | 0.0563 | 25382432 | | 0.0769 | 2.3422 | 13800 | 0.0568 | 25755616 | | 0.0487 | 2.3761 | 14000 | 0.0560 | 26131424 | | 0.0775 | 2.4101 | 14200 | 0.0560 | 26504960 | | 0.0526 | 2.4440 | 14400 | 0.0568 | 26877888 | | 0.0483 | 2.4780 | 14600 | 0.0558 | 27248384 | | 0.0695 | 2.5119 | 14800 | 0.0556 | 27625376 | | 0.0663 | 2.5458 | 15000 | 0.0561 | 28005696 | | 0.0542 | 2.5798 | 15200 | 0.0555 | 28379936 | | 0.0754 | 2.6137 | 15400 | 0.0557 | 28749536 | | 0.0406 | 2.6477 | 15600 | 0.0553 | 29128672 | | 0.0476 | 2.6816 | 15800 | 0.0552 | 29503456 | | 0.0725 | 2.7156 | 16000 | 0.0549 | 29874176 | | 0.07 | 2.7495 | 16200 | 0.0549 | 30251904 | | 0.0544 | 2.7835 | 16400 | 0.0552 | 30626560 | | 0.0545 | 2.8174 | 16600 | 0.0547 | 30999968 | | 0.0425 | 2.8514 | 16800 | 0.0546 | 31376704 | | 0.0646 | 2.8853 | 17000 | 0.0562 | 31749472 | | 0.0542 | 2.9193 | 17200 | 0.0546 | 32128320 | | 0.0445 | 2.9532 | 17400 | 0.0547 | 32501056 | | 0.065 | 2.9871 | 17600 | 0.0541 | 32872640 | | 0.0465 | 3.0210 | 17800 | 0.0543 | 33243744 | | 0.0475 | 3.0550 | 18000 | 0.0546 | 33619808 | | 0.0886 | 3.0889 | 18200 | 0.0543 | 33994048 | | 0.0389 | 3.1229 | 18400 | 0.0544 | 34361920 | | 0.0716 | 3.1568 | 18600 | 0.0537 | 34735392 | | 0.065 | 3.1908 | 18800 | 0.0537 | 35107872 | | 0.0658 | 3.2247 | 19000 | 0.0536 | 35486976 | | 0.063 | 3.2587 | 19200 | 0.0539 | 35862880 | | 0.0491 | 3.2926 | 19400 | 0.0536 | 36237280 | | 0.0656 | 3.3266 | 19600 | 0.0535 | 36614176 | | 0.0568 | 3.3605 | 19800 | 0.0534 | 36987200 | | 0.058 | 3.3944 | 20000 | 0.0537 | 37357312 | | 0.0471 | 3.4284 | 20200 | 0.0533 | 37728448 | | 0.0463 | 3.4623 | 20400 | 0.0535 | 38104736 | | 0.0691 | 3.4963 | 20600 | 0.0534 | 38477696 | | 0.0437 | 3.5302 | 20800 | 0.0531 | 38847808 | | 0.0465 | 3.5642 | 21000 | 0.0529 | 39222464 | | 0.0529 | 3.5981 | 21200 | 0.0530 | 39595392 | | 0.0699 | 3.6321 | 21400 | 0.0530 | 39971968 | | 0.063 | 3.6660 | 21600 | 0.0529 | 40341952 | | 0.0664 | 3.7000 | 21800 | 0.0530 | 40713376 | | 0.0464 | 3.7339 | 22000 | 0.0535 | 41085856 | | 0.0474 | 3.7679 | 22200 | 0.0527 | 41461568 | | 0.0436 | 3.8018 | 22400 | 0.0526 | 41833280 | | 0.0458 | 3.8357 | 22600 | 0.0526 | 42205152 | | 0.0419 | 3.8697 | 22800 | 0.0526 | 42578144 | | 0.0587 | 3.9036 | 23000 | 0.0527 | 42956608 | | 0.0522 | 3.9376 | 23200 | 0.0526 | 43327904 | | 0.0315 | 3.9715 | 23400 | 0.0524 | 43700960 | | 0.04 | 4.0054 | 23600 | 0.0524 | 44077568 | | 0.051 | 4.0394 | 23800 | 0.0528 | 44449632 | | 0.0667 | 4.0733 | 24000 | 0.0524 | 44825184 | | 0.0606 | 4.1073 | 24200 | 0.0522 | 45195872 | | 0.0362 | 4.1412 | 24400 | 0.0525 | 45566816 | | 0.0487 | 4.1752 | 24600 | 0.0523 | 45945824 | | 0.0492 | 4.2091 | 24800 | 0.0525 | 46322304 | | 0.0365 | 4.2431 | 25000 | 0.0522 | 46694976 | | 0.0683 | 4.2770 | 25200 | 0.0521 | 47069472 | | 0.0513 | 4.3109 | 25400 | 0.0522 | 47444064 | | 0.0546 | 4.3449 | 25600 | 0.0522 | 47819744 | | 0.0593 | 4.3788 | 25800 | 0.0522 | 48190912 | | 0.0514 | 4.4128 | 26000 | 0.0528 | 48563040 | | 0.0454 | 4.4467 | 26200 | 0.0520 | 48936320 | | 0.0486 | 4.4807 | 26400 | 0.0519 | 49306944 | | 0.0393 | 4.5146 | 26600 | 0.0521 | 49683712 | | 0.0322 | 4.5486 | 26800 | 0.0519 | 50057824 | | 0.042 | 4.5825 | 27000 | 0.0518 | 50431552 | | 0.058 | 4.6165 | 27200 | 0.0518 | 50808576 | | 0.0489 | 4.6504 | 27400 | 0.0518 | 51182144 | | 0.0376 | 4.6843 | 27600 | 0.0517 | 51554016 | | 0.0524 | 4.7183 | 27800 | 0.0518 | 51925888 | | 0.05 | 4.7522 | 28000 | 0.0519 | 52295168 | | 0.0391 | 4.7862 | 28200 | 0.0519 | 52664096 | | 0.038 | 4.8201 | 28400 | 0.0517 | 53038784 | | 0.0566 | 4.8541 | 28600 | 0.0517 | 53412352 | | 0.0506 | 4.8880 | 28800 | 0.0517 | 53788608 | | 0.0616 | 4.9220 | 29000 | 0.0518 | 54166176 | | 0.0675 | 4.9559 | 29200 | 0.0518 | 54541216 | | 0.066 | 4.9899 | 29400 | 0.0517 | 54916928 | | 0.0629 | 5.0238 | 29600 | 0.0516 | 55288160 | | 0.0287 | 5.0577 | 29800 | 0.0516 | 55662784 | | 0.0421 | 5.0917 | 30000 | 0.0519 | 56034432 | | 0.0298 | 5.1256 | 30200 | 0.0518 | 56405792 | | 0.0739 | 5.1595 | 30400 | 0.0516 | 56777504 | | 0.046 | 5.1935 | 30600 | 0.0516 | 57149760 | | 0.0529 | 5.2274 | 30800 | 0.0515 | 57521536 | | 0.0289 | 5.2614 | 31000 | 0.0514 | 57889408 | | 0.0424 | 5.2953 | 31200 | 0.0519 | 58258624 | | 0.0427 | 5.3293 | 31400 | 0.0517 | 58635520 | | 0.0425 | 5.3632 | 31600 | 0.0519 | 59006592 | | 0.0518 | 5.3972 | 31800 | 0.0515 | 59381312 | | 0.0716 | 5.4311 | 32000 | 0.0514 | 59761568 | | 0.059 | 5.4651 | 32200 | 0.0516 | 60138720 | | 0.0601 | 5.4990 | 32400 | 0.0516 | 60511168 | | 0.0695 | 5.5329 | 32600 | 0.0514 | 60884448 | | 0.0269 | 5.5669 | 32800 | 0.0513 | 61259680 | | 0.0423 | 5.6008 | 33000 | 0.0514 | 61636416 | | 0.0843 | 5.6348 | 33200 | 0.0514 | 62013760 | | 0.0657 | 5.6687 | 33400 | 0.0516 | 62389440 | | 0.0834 | 5.7027 | 33600 | 0.0514 | 62764512 | | 0.0725 | 5.7366 | 33800 | 0.0514 | 63139872 | | 0.0354 | 5.7706 | 34000 | 0.0514 | 63517632 | | 0.0817 | 5.8045 | 34200 | 0.0515 | 63889248 | | 0.0493 | 5.8385 | 34400 | 0.0513 | 64262048 | | 0.0603 | 5.8724 | 34600 | 0.0513 | 64632256 | | 0.0322 | 5.9064 | 34800 | 0.0513 | 65006944 | | 0.08 | 5.9403 | 35000 | 0.0513 | 65382656 | | 0.0451 | 5.9742 | 35200 | 0.0514 | 65756992 | | 0.0516 | 6.0081 | 35400 | 0.0513 | 66125280 | | 0.0647 | 6.0421 | 35600 | 0.0513 | 66493536 | | 0.0448 | 6.0760 | 35800 | 0.0514 | 66867936 | | 0.0546 | 6.1100 | 36000 | 0.0515 | 67243328 | | 0.0449 | 6.1439 | 36200 | 0.0516 | 67616992 | | 0.0329 | 6.1779 | 36400 | 0.0516 | 67995520 | | 0.035 | 6.2118 | 36600 | 0.0514 | 68370624 | | 0.0461 | 6.2458 | 36800 | 0.0514 | 68746880 | | 0.0456 | 6.2797 | 37000 | 0.0515 | 69119328 | | 0.0573 | 6.3137 | 37200 | 0.0514 | 69490336 | | 0.0501 | 6.3476 | 37400 | 0.0515 | 69862688 | | 0.0323 | 6.3816 | 37600 | 0.0513 | 70238592 | | 0.0381 | 6.4155 | 37800 | 0.0514 | 70612608 | | 0.054 | 6.4494 | 38000 | 0.0514 | 70985568 | | 0.0242 | 6.4834 | 38200 | 0.0515 | 71360704 | | 0.0399 | 6.5173 | 38400 | 0.0515 | 71738432 | | 0.0286 | 6.5513 | 38600 | 0.0514 | 72112640 | | 0.0532 | 6.5852 | 38800 | 0.0514 | 72484256 | | 0.0447 | 6.6192 | 39000 | 0.0515 | 72858912 | | 0.046 | 6.6531 | 39200 | 0.0513 | 73232576 | | 0.0673 | 6.6871 | 39400 | 0.0516 | 73604352 | | 0.0389 | 6.7210 | 39600 | 0.0514 | 73975648 | | 0.0397 | 6.7550 | 39800 | 0.0514 | 74349632 | | 0.0395 | 6.7889 | 40000 | 0.0514 | 74724160 | ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

dzanbek/0039ab53-95ed-4f05-9bd4-15486ae682bc

dzanbek

"2025-04-19T09:05:59Z"

0

peft

[ "peft", "safetensors", "opt", "axolotl", "generated_from_trainer", "base_model:facebook/opt-1.3b", "base_model:adapter:facebook/opt-1.3b", "license:other", "8-bit", "bitsandbytes", "region:us" ]

null

"2025-04-19T08:54:46Z"

--- library_name: peft license: other base_model: facebook/opt-1.3b tags: - axolotl - generated_from_trainer model-index: - name: 0039ab53-95ed-4f05-9bd4-15486ae682bc results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: facebook/opt-1.3b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 45af3457229c3363_train_data.json ds_type: json format: custom path: /workspace/input_data/45af3457229c3363_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/0039ab53-95ed-4f05-9bd4-15486ae682bc hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/45af3457229c3363_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 047ce7a0-d57c-486c-ba3f-1f0804c929df wandb_project: 01-31 wandb_run: your_name wandb_runid: 047ce7a0-d57c-486c-ba3f-1f0804c929df warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 0039ab53-95ed-4f05-9bd4-15486ae682bc This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.964 | 0.0114 | 150 | 3.0908 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

John6666/noborumix-illustrious-xl-20-merged-v20-illustrious20-sdxl

John6666

"2025-04-19T09:04:12Z"

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "Illustrious XL v2.0", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-XL-v2.0", "base_model:finetune:OnomaAIResearch/Illustrious-XL-v2.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

"2025-04-19T08:58:45Z"

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - Illustrious XL v2.0 - illustrious base_model: OnomaAIResearch/Illustrious-XL-v2.0 --- Original model is [here](https://civitai.com/models/1439680?modelVersionId=1681362). This model created by [noboru6703](https://civitai.com/user/noboru6703).

mlfoundations-dev/b1_science_top_16_10k

mlfoundations-dev

"2025-04-19T08:51:23Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-19T04:45:08Z"

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b1_science_top_16_10k results: [] ---  # b1_science_top_16_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b1_science_top_16_10k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

vuongpro/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_skilled_owl

vuongpro

"2025-04-19T08:45:50Z"

4

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am scavenging skilled owl", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-14T21:16:54Z"

--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_skilled_owl tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am scavenging skilled owl - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_skilled_owl This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vuongpro/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_skilled_owl", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

ahmet71cakir/phi4-turbochat

ahmet71cakir

"2025-04-19T08:42:25Z"

21

0

peft

[ "peft", "safetensors", "phi3", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-4-mini-instruct", "base_model:adapter:microsoft/Phi-4-mini-instruct", "license:mit", "region:us" ]

null

"2025-04-18T17:12:05Z"

--- library_name: peft license: mit base_model: microsoft/Phi-4-mini-instruct tags: - generated_from_trainer model-index: - name: phi4-turbochat results: [] ---  # phi4-turbochat This model is a fine-tuned version of [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

rbelanec/train_sst2_1744902625

rbelanec

"2025-04-19T08:41:51Z"

0

peft

[ "peft", "safetensors", "llama-factory", "ia3", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]

null

"2025-04-18T22:40:11Z"

--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - llama-factory - ia3 - generated_from_trainer model-index: - name: train_sst2_1744902625 results: [] ---  # train_sst2_1744902625 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0713 - Num Input Tokens Seen: 33458560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - training_steps: 40000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:-------:|:-----:|:---------------:|:-----------------:| | 0.1318 | 0.0528 | 200 | 0.1586 | 166688 | | 0.1951 | 0.1056 | 400 | 0.1397 | 334048 | | 0.0693 | 0.1584 | 600 | 0.1319 | 500448 | | 0.0969 | 0.2112 | 800 | 0.1235 | 667872 | | 0.1196 | 0.2640 | 1000 | 0.1205 | 834848 | | 0.1125 | 0.3167 | 1200 | 0.1172 | 1002816 | | 0.0759 | 0.3695 | 1400 | 0.1145 | 1169088 | | 0.2051 | 0.4223 | 1600 | 0.1123 | 1337088 | | 0.1022 | 0.4751 | 1800 | 0.1098 | 1505536 | | 0.1273 | 0.5279 | 2000 | 0.1084 | 1673024 | | 0.1299 | 0.5807 | 2200 | 0.1064 | 1842304 | | 0.1179 | 0.6335 | 2400 | 0.1051 | 2007328 | | 0.0799 | 0.6863 | 2600 | 0.1043 | 2174880 | | 0.1575 | 0.7391 | 2800 | 0.1027 | 2341280 | | 0.116 | 0.7919 | 3000 | 0.1017 | 2509440 | | 0.0956 | 0.8447 | 3200 | 0.1001 | 2674784 | | 0.0767 | 0.8975 | 3400 | 0.0986 | 2843680 | | 0.1587 | 0.9502 | 3600 | 0.1008 | 3011904 | | 0.0879 | 1.0029 | 3800 | 0.0968 | 3178064 | | 0.1003 | 1.0557 | 4000 | 0.0961 | 3345904 | | 0.0937 | 1.1085 | 4200 | 0.0947 | 3514608 | | 0.0834 | 1.1613 | 4400 | 0.0943 | 3680560 | | 0.0678 | 1.2141 | 4600 | 0.0933 | 3849328 | | 0.1148 | 1.2669 | 4800 | 0.0934 | 4017200 | | 0.0771 | 1.3197 | 5000 | 0.0917 | 4187184 | | 0.0694 | 1.3724 | 5200 | 0.0915 | 4354416 | | 0.0979 | 1.4252 | 5400 | 0.0907 | 4519856 | | 0.1024 | 1.4780 | 5600 | 0.0901 | 4687280 | | 0.0657 | 1.5308 | 5800 | 0.0896 | 4856112 | | 0.0484 | 1.5836 | 6000 | 0.0896 | 5022736 | | 0.1531 | 1.6364 | 6200 | 0.0881 | 5188656 | | 0.0504 | 1.6892 | 6400 | 0.0878 | 5356208 | | 0.1301 | 1.7420 | 6600 | 0.0874 | 5523952 | | 0.1359 | 1.7948 | 6800 | 0.0867 | 5690672 | | 0.0949 | 1.8476 | 7000 | 0.0864 | 5857072 | | 0.0511 | 1.9004 | 7200 | 0.0864 | 6024976 | | 0.0878 | 1.9531 | 7400 | 0.0859 | 6191664 | | 0.0956 | 2.0058 | 7600 | 0.0850 | 6357472 | | 0.1002 | 2.0586 | 7800 | 0.0851 | 6525984 | | 0.0851 | 2.1114 | 8000 | 0.0841 | 6692320 | | 0.0766 | 2.1642 | 8200 | 0.0838 | 6860064 | | 0.1541 | 2.2170 | 8400 | 0.0846 | 7026528 | | 0.0482 | 2.2698 | 8600 | 0.0834 | 7192384 | | 0.1002 | 2.3226 | 8800 | 0.0832 | 7358816 | | 0.0775 | 2.3753 | 9000 | 0.0827 | 7526496 | | 0.0724 | 2.4281 | 9200 | 0.0824 | 7696064 | | 0.0841 | 2.4809 | 9400 | 0.0831 | 7863456 | | 0.1016 | 2.5337 | 9600 | 0.0818 | 8031776 | | 0.0756 | 2.5865 | 9800 | 0.0819 | 8199584 | | 0.0949 | 2.6393 | 10000 | 0.0810 | 8366016 | | 0.0677 | 2.6921 | 10200 | 0.0812 | 8531808 | | 0.0611 | 2.7449 | 10400 | 0.0807 | 8702976 | | 0.0474 | 2.7977 | 10600 | 0.0804 | 8870944 | | 0.0933 | 2.8505 | 10800 | 0.0812 | 9039680 | | 0.1127 | 2.9033 | 11000 | 0.0813 | 9206880 | | 0.0633 | 2.9561 | 11200 | 0.0802 | 9372128 | | 0.0816 | 3.0087 | 11400 | 0.0794 | 9538768 | | 0.0781 | 3.0615 | 11600 | 0.0791 | 9705232 | | 0.0599 | 3.1143 | 11800 | 0.0793 | 9871632 | | 0.0713 | 3.1671 | 12000 | 0.0794 | 10039472 | | 0.0291 | 3.2199 | 12200 | 0.0789 | 10206320 | | 0.0547 | 3.2727 | 12400 | 0.0785 | 10376240 | | 0.0882 | 3.3255 | 12600 | 0.0787 | 10544464 | | 0.0322 | 3.3782 | 12800 | 0.0781 | 10712240 | | 0.0395 | 3.4310 | 13000 | 0.0778 | 10879120 | | 0.0472 | 3.4838 | 13200 | 0.0779 | 11045072 | | 0.0689 | 3.5366 | 13400 | 0.0781 | 11211312 | | 0.09 | 3.5894 | 13600 | 0.0774 | 11378128 | | 0.0392 | 3.6422 | 13800 | 0.0780 | 11544592 | | 0.1368 | 3.6950 | 14000 | 0.0771 | 11713040 | | 0.1223 | 3.7478 | 14200 | 0.0774 | 11880432 | | 0.106 | 3.8006 | 14400 | 0.0765 | 12048176 | | 0.049 | 3.8534 | 14600 | 0.0771 | 12215792 | | 0.0427 | 3.9062 | 14800 | 0.0769 | 12383792 | | 0.052 | 3.9590 | 15000 | 0.0764 | 12549680 | | 0.0927 | 4.0116 | 15200 | 0.0763 | 12716448 | | 0.0437 | 4.0644 | 15400 | 0.0767 | 12882752 | | 0.0549 | 4.1172 | 15600 | 0.0764 | 13051200 | | 0.0587 | 4.1700 | 15800 | 0.0761 | 13217024 | | 0.0562 | 4.2228 | 16000 | 0.0757 | 13382784 | | 0.0657 | 4.2756 | 16200 | 0.0764 | 13549216 | | 0.0374 | 4.3284 | 16400 | 0.0752 | 13719072 | | 0.1196 | 4.3812 | 16600 | 0.0752 | 13884928 | | 0.0847 | 4.4339 | 16800 | 0.0751 | 14051584 | | 0.0485 | 4.4867 | 17000 | 0.0769 | 14220704 | | 0.0352 | 4.5395 | 17200 | 0.0749 | 14387008 | | 0.1084 | 4.5923 | 17400 | 0.0749 | 14555808 | | 0.0591 | 4.6451 | 17600 | 0.0755 | 14723456 | | 0.116 | 4.6979 | 17800 | 0.0749 | 14890880 | | 0.0692 | 4.7507 | 18000 | 0.0755 | 15059744 | | 0.0686 | 4.8035 | 18200 | 0.0746 | 15224512 | | 0.1239 | 4.8563 | 18400 | 0.0744 | 15392960 | | 0.0474 | 4.9091 | 18600 | 0.0744 | 15561696 | | 0.0925 | 4.9619 | 18800 | 0.0744 | 15728800 | | 0.0724 | 5.0145 | 19000 | 0.0741 | 15897552 | | 0.0674 | 5.0673 | 19200 | 0.0740 | 16064688 | | 0.0695 | 5.1201 | 19400 | 0.0740 | 16231120 | | 0.0706 | 5.1729 | 19600 | 0.0737 | 16397744 | | 0.1331 | 5.2257 | 19800 | 0.0738 | 16564176 | | 0.0663 | 5.2785 | 20000 | 0.0737 | 16731600 | | 0.0327 | 5.3313 | 20200 | 0.0748 | 16898064 | | 0.0879 | 5.3841 | 20400 | 0.0738 | 17064080 | | 0.0532 | 5.4368 | 20600 | 0.0736 | 17231888 | | 0.0614 | 5.4896 | 20800 | 0.0735 | 17399184 | | 0.0563 | 5.5424 | 21000 | 0.0745 | 17566160 | | 0.0631 | 5.5952 | 21200 | 0.0736 | 17732304 | | 0.0431 | 5.6480 | 21400 | 0.0733 | 17900880 | | 0.0466 | 5.7008 | 21600 | 0.0733 | 18070192 | | 0.0843 | 5.7536 | 21800 | 0.0732 | 18237168 | | 0.0494 | 5.8064 | 22000 | 0.0731 | 18403856 | | 0.1229 | 5.8592 | 22200 | 0.0732 | 18571248 | | 0.0307 | 5.9120 | 22400 | 0.0731 | 18738672 | | 0.0534 | 5.9648 | 22600 | 0.0730 | 18905744 | | 0.0806 | 6.0174 | 22800 | 0.0731 | 19073440 | | 0.0733 | 6.0702 | 23000 | 0.0732 | 19241920 | | 0.1169 | 6.1230 | 23200 | 0.0732 | 19409408 | | 0.0757 | 6.1758 | 23400 | 0.0731 | 19577024 | | 0.0495 | 6.2286 | 23600 | 0.0728 | 19744608 | | 0.0752 | 6.2814 | 23800 | 0.0727 | 19911488 | | 0.0694 | 6.3342 | 24000 | 0.0726 | 20078944 | | 0.0617 | 6.3870 | 24200 | 0.0727 | 20244928 | | 0.093 | 6.4398 | 24400 | 0.0725 | 20411232 | | 0.0579 | 6.4925 | 24600 | 0.0728 | 20578080 | | 0.0712 | 6.5453 | 24800 | 0.0725 | 20746592 | | 0.1026 | 6.5981 | 25000 | 0.0727 | 20913344 | | 0.0384 | 6.6509 | 25200 | 0.0725 | 21081952 | | 0.0928 | 6.7037 | 25400 | 0.0724 | 21248384 | | 0.0907 | 6.7565 | 25600 | 0.0723 | 21415872 | | 0.0511 | 6.8093 | 25800 | 0.0729 | 21584000 | | 0.1154 | 6.8621 | 26000 | 0.0723 | 21751168 | | 0.0398 | 6.9149 | 26200 | 0.0722 | 21918816 | | 0.0674 | 6.9677 | 26400 | 0.0723 | 22084384 | | 0.0688 | 7.0203 | 26600 | 0.0722 | 22251776 | | 0.0766 | 7.0731 | 26800 | 0.0722 | 22418080 | | 0.0622 | 7.1259 | 27000 | 0.0721 | 22587392 | | 0.0562 | 7.1787 | 27200 | 0.0721 | 22753056 | | 0.0631 | 7.2315 | 27400 | 0.0724 | 22920768 | | 0.0828 | 7.2843 | 27600 | 0.0718 | 23087296 | | 0.0412 | 7.3371 | 27800 | 0.0721 | 23254400 | | 0.0324 | 7.3899 | 28000 | 0.0721 | 23422752 | | 0.0441 | 7.4427 | 28200 | 0.0721 | 23588352 | | 0.0616 | 7.4954 | 28400 | 0.0723 | 23755840 | | 0.0565 | 7.5482 | 28600 | 0.0721 | 23923680 | | 0.0559 | 7.6010 | 28800 | 0.0719 | 24091168 | | 0.0394 | 7.6538 | 29000 | 0.0721 | 24258016 | | 0.0899 | 7.7066 | 29200 | 0.0718 | 24427808 | | 0.0231 | 7.7594 | 29400 | 0.0718 | 24596288 | | 0.0492 | 7.8122 | 29600 | 0.0718 | 24764192 | | 0.0627 | 7.8650 | 29800 | 0.0719 | 24932000 | | 0.0346 | 7.9178 | 30000 | 0.0718 | 25100224 | | 0.0597 | 7.9706 | 30200 | 0.0722 | 25267808 | | 0.0569 | 8.0232 | 30400 | 0.0720 | 25433440 | | 0.0757 | 8.0760 | 30600 | 0.0717 | 25600672 | | 0.0524 | 8.1288 | 30800 | 0.0718 | 25769408 | | 0.0424 | 8.1816 | 31000 | 0.0717 | 25936160 | | 0.0652 | 8.2344 | 31200 | 0.0718 | 26103744 | | 0.0822 | 8.2872 | 31400 | 0.0715 | 26270560 | | 0.0691 | 8.3400 | 31600 | 0.0719 | 26437536 | | 0.031 | 8.3928 | 31800 | 0.0719 | 26604480 | | 0.0484 | 8.4456 | 32000 | 0.0716 | 26771680 | | 0.1148 | 8.4984 | 32200 | 0.0716 | 26940256 | | 0.073 | 8.5511 | 32400 | 0.0715 | 27107680 | | 0.0813 | 8.6039 | 32600 | 0.0718 | 27274048 | | 0.1232 | 8.6567 | 32800 | 0.0717 | 27440544 | | 0.0994 | 8.7095 | 33000 | 0.0716 | 27608000 | | 0.0363 | 8.7623 | 33200 | 0.0715 | 27776704 | | 0.016 | 8.8151 | 33400 | 0.0717 | 27942752 | | 0.0744 | 8.8679 | 33600 | 0.0716 | 28108864 | | 0.0325 | 8.9207 | 33800 | 0.0714 | 28275296 | | 0.0517 | 8.9735 | 34000 | 0.0716 | 28443520 | | 0.028 | 9.0261 | 34200 | 0.0716 | 28609776 | | 0.061 | 9.0789 | 34400 | 0.0716 | 28777712 | | 0.1408 | 9.1317 | 34600 | 0.0717 | 28944144 | | 0.0362 | 9.1845 | 34800 | 0.0716 | 29111152 | | 0.0993 | 9.2373 | 35000 | 0.0716 | 29278000 | | 0.0391 | 9.2901 | 35200 | 0.0716 | 29443792 | | 0.0398 | 9.3429 | 35400 | 0.0716 | 29609072 | | 0.0981 | 9.3957 | 35600 | 0.0715 | 29776592 | | 0.0716 | 9.4485 | 35800 | 0.0716 | 29941616 | | 0.066 | 9.5013 | 36000 | 0.0717 | 30110160 | | 0.0694 | 9.5540 | 36200 | 0.0716 | 30277744 | | 0.1284 | 9.6068 | 36400 | 0.0716 | 30447152 | | 0.028 | 9.6596 | 36600 | 0.0713 | 30612976 | | 0.0429 | 9.7124 | 36800 | 0.0714 | 30780240 | | 0.0227 | 9.7652 | 37000 | 0.0715 | 30948048 | | 0.05 | 9.8180 | 37200 | 0.0715 | 31116368 | | 0.0342 | 9.8708 | 37400 | 0.0715 | 31283888 | | 0.0368 | 9.9236 | 37600 | 0.0716 | 31452560 | | 0.0681 | 9.9764 | 37800 | 0.0714 | 31620720 | | 0.0867 | 10.0290 | 38000 | 0.0713 | 31786016 | | 0.0869 | 10.0818 | 38200 | 0.0715 | 31952768 | | 0.0735 | 10.1346 | 38400 | 0.0714 | 32120320 | | 0.0173 | 10.1874 | 38600 | 0.0715 | 32287584 | | 0.0469 | 10.2402 | 38800 | 0.0716 | 32455072 | | 0.0459 | 10.2930 | 39000 | 0.0713 | 32621184 | | 0.0397 | 10.3458 | 39200 | 0.0714 | 32788960 | | 0.0401 | 10.3986 | 39400 | 0.0716 | 32955776 | | 0.0332 | 10.4514 | 39600 | 0.0716 | 33122816 | | 0.0907 | 10.5042 | 39800 | 0.0716 | 33291072 | | 0.0616 | 10.5569 | 40000 | 0.0716 | 33458560 | ### Framework versions - PEFT 0.15.1 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf

RichardErkhov

"2025-04-19T08:41:02Z"

0

null

[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-19T05:27:18Z"

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mp_mistral7bv3_sft_dpo_beta2e-1_epoch2 - GGUF - Model creator: https://huggingface.co/yjwon/ - Original model: https://huggingface.co/yjwon/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2/ | Name | Quant method | Size | | ---- | ---- | ---- | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q2_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q2_K.gguf) | Q2_K | 2.54GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_XS.gguf) | IQ3_XS | 2.82GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_S.gguf) | IQ3_S | 2.97GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ3_M.gguf) | IQ3_M | 3.06GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K.gguf) | Q3_K | 3.28GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ4_XS.gguf) | IQ4_XS | 3.68GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_0.gguf) | Q4_0 | 3.83GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K.gguf) | Q4_K | 4.07GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_1.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q4_1.gguf) | Q4_1 | 4.24GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_0.gguf) | Q5_0 | 4.66GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K_S.gguf) | Q5_K_S | 4.66GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K.gguf) | Q5_K | 4.78GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_1.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q5_1.gguf) | Q5_1 | 5.07GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q6_K.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q6_K.gguf) | Q6_K | 5.54GB | | [mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q8_0.gguf](https://huggingface.co/RichardErkhov/yjwon_-_mp_mistral7bv3_sft_dpo_beta2e-1_epoch2-gguf/blob/main/mp_mistral7bv3_sft_dpo_beta2e-1_epoch2.Q8_0.gguf) | Q8_0 | 7.17GB | Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details 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