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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-27 18:27:39	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 500 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-27 18:23:41	card stringlengths 11 1.01M
mlfoundations-dev/d1_math_longest_10k	mlfoundations-dev	2025-05-04T06:41:39Z	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	2025-05-03T13:10:09Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_math_longest_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. --> # d1_math_longest_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/d1_math_longest_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
AXERA-TECH/Real-ESRGAN	AXERA-TECH	2025-05-04T06:39:09Z	0	0	null	[ "onnx", "Real-ESRGAN", "SR", "Int8", "image-to-image", "en", "license:bsd-3-clause-clear", "region:us" ]	image-to-image	2025-05-04T04:06:35Z	--- license: bsd-3-clause-clear language: - en pipeline_tag: image-to-image tags: - Real-ESRGAN - SR - Int8 --- # Real-ESRGAN This version of Real-ESRGAN has been converted to run on the Axera NPU using w8a8 quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 3.4 ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through - [The repo of original](https://github.com/xinntao/Real-ESRGAN) - [The repo of AXera Platform](https://github.com/AXERA-TECH/realesrgan.axera), which you can get the detial of guide - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform - AX650 - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) - AX630C - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) \|Chips\| 64x64 -> 256x256 \| 256x256 -> 1024x1024 \| \|--\|--\|--\| \|AX650\| 15 ms \| 440 ms \| \|AX630C\| 76 ms \| 2030 ms \| ## How to use Download all files from this repository to the device ``` (axcl) axera@raspberrypi:~/samples/realesrgan.axera $ tree -L 2 . ├── ax630c │ ├── realesrgan-x4-256.axmodel │ └── realesrgan-x4.axmodel ├── ax650 │ ├── realesrgan-x4-256.axmodel │ └── realesrgan-x4.axmodel ├── config.json ├── main.py ├── onnx │ ├── realesrgan-x4-256.onnx │ └── realesrgan-x4.onnx ├── output_test_256.jpg ├── out_test-256.jpg └── test_256.jpeg 3 directories, 11 file ``` ### python env requirement #### pyaxengine https://github.com/AXERA-TECH/pyaxengine ``` wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3rc0/axengine-0.1.3-py3-none-any.whl pip install axengine-0.1.3-py3-none-any.whl ``` #### others ``` pip install argparse numpy opencv-python ``` ## Inference with AX630C Host, such as Module-LLM, LLM630 Compute Kit ``` root@ax630c:/mnt/qtang/realesrgan.axera# python3 main.py --input test_256.jpeg --output test_256_20e.jpeg --model ax630/realesrgan-x4-256.axmodel [INFO] Available providers: ['AxEngineExecutionProvider'] [INFO] Using provider: AxEngineExecutionProvider [INFO] Chip type: ChipType.MC20E [INFO] VNPU type: VNPUType.DISABLED [INFO] Engine version: 2.7.2a [INFO] Model type: 1 (full core) [INFO] Compiler version: 3.4 3dfd5692 input.1 [1, 256, 256, 3] uint8 1895 [1, 1024, 1024, 3] float32 Original Image Shape: (243, 243, 3) Preprocessed Image Shape: (1, 256, 256, 3) Inference Time: 2066.72 ms Output Shape: (1, 1024, 1024, 3) Final Output Image Shape: (1024, 1024, 3) root@ax630c:/mnt/qtang/realesrgan.axera# ``` ## Inference with M.2 Accelerator card [What is M.2 Accelerator card?](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html), Show this DEMO based on Raspberry PI 5. ``` (axcl) axera@raspberrypi:~/samples/realesrgan.axera $ python main.py --input test_256.jpeg --output output_test_256.jpg --model realesrgan-x4-256.axmodel [INFO] Available providers: ['AXCLRTExecutionProvider'] [INFO] Using provider: AXCLRTExecutionProvider [INFO] SOC Name: AX650N [INFO] VNPU type: VNPUType.DISABLED [INFO] Compiler version: 3.4 3dfd5692 input.1 [1, 256, 256, 3] uint8 <cdata 'char ' 0x262e54e0> [1, 1024, 1024, 3] float32 Original Image Shape: (243, 243, 3) Preprocessed Image Shape: (1, 256, 256, 3) Inference Time: 455.81 ms Output Shape: (1, 1024, 1024, 3) Final Output Image Shape: (1024, 1024, 3) (axcl) axera@raspberrypi:~/samples/realesrgan.axera $ ``` Input* ![](./test_256.jpeg) Output ![](./output_test_256.jpg)
ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.001	ASethi04	2025-05-04T06:38:52Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T02:49:17Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.001", 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/torchql-org/huggingface/runs/3r6mawt9) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - 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}} } ```
Membersuger/Euro_38	Membersuger	2025-05-04T06:34:50Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:43:16Z	--- 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]
masato-ka/akiba-stadion	masato-ka	2025-05-04T06:33:34Z	3	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-05-04T06:32:29Z	--- license: apache-2.0 ---
unfixbug/bert-finetuned-ner	unfixbug	2025-05-04T06:32:40Z	1	0	transformers	[ "transformers", "tf", "bert", "token-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-04T06:17:49Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: unfixbug/bert-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # unfixbug/bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0272 - Validation Loss: 0.0560 - Epoch: 2 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': np.float32(0.9), 'beta_2': np.float32(0.999), 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.1761 \| 0.0650 \| 0 \| \| 0.0480 \| 0.0576 \| 1 \| \| 0.0272 \| 0.0560 \| 2 \| ### Framework versions - Transformers 4.51.3 - TensorFlow 2.18.0 - Datasets 3.5.1 - Tokenizers 0.21.1
0xtinuviel/Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-subtle_rugged_snail	0xtinuviel	2025-05-04T06:31:09Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am subtle rugged snail", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-72B-Instruct-bnb-4bit", "base_model:finetune:Gensyn/Qwen2.5-72B-Instruct-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-05-02T00:56:10Z	--- base_model: Gensyn/Qwen2.5-72B-Instruct-bnb-4bit library_name: transformers model_name: Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-subtle_rugged_snail tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am subtle rugged snail - unsloth - trl licence: license --- # Model Card for Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-subtle_rugged_snail This model is a fine-tuned version of [Gensyn/Qwen2.5-72B-Instruct-bnb-4bit](https://huggingface.co/Gensyn/Qwen2.5-72B-Instruct-bnb-4bit). 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="0xtinuviel/Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-subtle_rugged_snail", 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: 3.5.1 - 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}} } ```
nanaseven7891/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_peckish_clam	nanaseven7891	2025-05-04T06:29:22Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am aquatic peckish clam", "unsloth", "trl", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-03T13:52:40Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_peckish_clam tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am aquatic peckish clam - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_peckish_clam This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="nanaseven7891/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_peckish_clam", 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}} } ```
DevQuasar/kyutai.helium-1-2b-stem-GGUF	DevQuasar	2025-05-04T06:28:43Z	10	0	null	[ "gguf", "text-generation", "base_model:kyutai/helium-1-2b-stem", "base_model:quantized:kyutai/helium-1-2b-stem", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T06:09:19Z	--- base_model: - kyutai/helium-1-2b-stem pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [kyutai/helium-1-2b-stem](https://huggingface.co/kyutai/helium-1-2b-stem) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
Kenazin/Llama-3.1-8B-peft-v6-10	Kenazin	2025-05-04T06:28:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T06:28:30Z	--- 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. 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YANG-12/detr-resnet-50-dc5-fashionpedia-finetuned	YANG-12	2025-05-04T06:28:19Z	0	0	transformers	[ "transformers", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50-dc5", "base_model:finetune:facebook/detr-resnet-50-dc5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2025-05-03T03:01:17Z	--- library_name: transformers license: apache-2.0 base_model: facebook/detr-resnet-50-dc5 tags: - generated_from_trainer model-index: - name: detr-resnet-50-dc5-fashionpedia-finetuned 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. --> # detr-resnet-50-dc5-fashionpedia-finetuned This model is a fine-tuned version of [facebook/detr-resnet-50-dc5](https://huggingface.co/facebook/detr-resnet-50-dc5) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3991 - Map: 0.0109 - Map 50: 0.0212 - Map 75: 0.0099 - Map Small: 0.0052 - Map Medium: 0.0152 - Map Large: 0.0094 - Mar 1: 0.0333 - Mar 10: 0.0612 - Mar 100: 0.0641 - Mar Small: 0.0219 - Mar Medium: 0.0593 - Mar Large: 0.0779 - Map Shirt, blouse: 0.0 - Mar 100 Shirt, blouse: 0.0 - Map Top, t-shirt, sweatshirt: 0.0267 - Mar 100 Top, t-shirt, sweatshirt: 0.3125 - Map Sweater: 0.0 - Mar 100 Sweater: 0.0 - Map Cardigan: 0.0 - Mar 100 Cardigan: 0.0 - Map Jacket: 0.0 - Mar 100 Jacket: 0.0 - Map Vest: 0.0 - Mar 100 Vest: 0.0 - Map Pants: 0.0805 - Mar 100 Pants: 0.6679 - Map Shorts: 0.0 - Mar 100 Shorts: 0.0 - Map Skirt: 0.0099 - Mar 100 Skirt: 0.0063 - Map Coat: 0.0 - Mar 100 Coat: 0.0 - Map Dress: 0.1037 - Mar 100 Dress: 0.7209 - Map Jumpsuit: 0.0 - Mar 100 Jumpsuit: 0.0 - Map Cape: 0.0 - Mar 100 Cape: 0.0 - Map Glasses: 0.0 - Mar 100 Glasses: 0.0 - Map Hat: 0.0 - Mar 100 Hat: 0.0 - Map Headband, head covering, hair accessory: 0.0 - Mar 100 Headband, head covering, hair accessory: 0.0 - Map Tie: 0.0 - Mar 100 Tie: 0.0 - Map Glove: 0.0 - Mar 100 Glove: 0.0 - Map Watch: 0.0 - Mar 100 Watch: 0.0 - Map Belt: 0.0 - Mar 100 Belt: 0.0 - Map Leg warmer: 0.0 - Mar 100 Leg warmer: 0.0 - Map Tights, stockings: 0.0 - Mar 100 Tights, stockings: 0.0 - Map Sock: 0.0 - Mar 100 Sock: 0.0 - Map Shoe: 0.1858 - Mar 100 Shoe: 0.4936 - Map Bag, wallet: 0.0003 - Mar 100 Bag, wallet: 0.0023 - Map Scarf: 0.0 - Mar 100 Scarf: 0.0 - Map Umbrella: 0.0 - Mar 100 Umbrella: 0.0 - Map Hood: 0.0 - Mar 100 Hood: 0.0 - Map Collar: 0.0 - Mar 100 Collar: 0.0 - Map Lapel: 0.0 - Mar 100 Lapel: 0.0 - Map Epaulette: 0.0 - Mar 100 Epaulette: 0.0 - Map Sleeve: 0.0607 - Mar 100 Sleeve: 0.4527 - Map Pocket: 0.0001 - Mar 100 Pocket: 0.0437 - Map Neckline: 0.0348 - Mar 100 Neckline: 0.2492 - Map Buckle: 0.0 - Mar 100 Buckle: 0.0 - Map Zipper: 0.0 - Mar 100 Zipper: 0.0 - Map Applique: 0.0 - Mar 100 Applique: 0.0 - Map Bead: 0.0 - Mar 100 Bead: 0.0 - Map Bow: 0.0 - Mar 100 Bow: 0.0 - Map Flower: 0.0 - Mar 100 Flower: 0.0 - Map Fringe: 0.0 - Mar 100 Fringe: 0.0 - Map Ribbon: 0.0 - Mar 100 Ribbon: 0.0 - Map Rivet: 0.0 - Mar 100 Rivet: 0.0 - Map Ruffle: 0.0 - Mar 100 Ruffle: 0.0 - Map Sequin: 0.0 - Mar 100 Sequin: 0.0 - Map Tassel: 0.0 - Mar 100 Tassel: 0.0 ## 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: 4 - 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: linear - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Shirt, blouse \| Mar 100 Shirt, blouse \| Map Top, t-shirt, sweatshirt \| Mar 100 Top, t-shirt, sweatshirt \| Map Sweater \| Mar 100 Sweater \| Map Cardigan \| Mar 100 Cardigan \| Map Jacket \| Mar 100 Jacket \| Map Vest \| Mar 100 Vest \| Map Pants \| Mar 100 Pants \| Map Shorts \| Mar 100 Shorts \| Map Skirt \| Mar 100 Skirt \| Map Coat \| Mar 100 Coat \| Map Dress \| Mar 100 Dress \| Map Jumpsuit \| Mar 100 Jumpsuit \| Map Cape \| Mar 100 Cape \| Map Glasses \| Mar 100 Glasses \| Map Hat \| Mar 100 Hat \| Map Headband, head covering, hair accessory \| Mar 100 Headband, head covering, hair accessory \| Map Tie \| Mar 100 Tie \| Map Glove \| Mar 100 Glove \| Map Watch \| Mar 100 Watch \| Map Belt \| Mar 100 Belt \| Map Leg warmer \| Mar 100 Leg warmer \| Map Tights, stockings \| Mar 100 Tights, stockings \| Map Sock \| Mar 100 Sock \| Map Shoe \| Mar 100 Shoe \| Map Bag, wallet \| Mar 100 Bag, wallet \| Map Scarf \| Mar 100 Scarf \| Map Umbrella \| Mar 100 Umbrella \| Map Hood \| Mar 100 Hood \| Map Collar \| Mar 100 Collar \| Map Lapel \| Mar 100 Lapel \| Map Epaulette \| Mar 100 Epaulette \| Map Sleeve \| Mar 100 Sleeve \| Map Pocket \| Mar 100 Pocket \| Map Neckline \| Mar 100 Neckline \| Map Buckle \| Mar 100 Buckle \| Map Zipper \| Mar 100 Zipper \| Map Applique \| Mar 100 Applique \| Map Bead \| Mar 100 Bead \| Map Bow \| Mar 100 Bow \| Map Flower \| Mar 100 Flower \| Map Fringe \| Mar 100 Fringe \| Map Ribbon \| Mar 100 Ribbon \| Map Rivet \| Mar 100 Rivet \| Map Ruffle \| Mar 100 Ruffle \| Map Sequin \| Mar 100 Sequin \| Map Tassel \| Mar 100 Tassel \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:-----------------:\|:---------------------:\|:----------------------------:\|:--------------------------------:\|:-----------:\|:---------------:\|:------------:\|:----------------:\|:----------:\|:--------------:\|:--------:\|:------------:\|:---------:\|:-------------:\|:----------:\|:--------------:\|:---------:\|:-------------:\|:--------:\|:------------:\|:---------:\|:-------------:\|:------------:\|:----------------:\|:--------:\|:------------:\|:-----------:\|:---------------:\|:-------:\|:-----------:\|:-------------------------------------------:\|:-----------------------------------------------:\|:-------:\|:-----------:\|:---------:\|:-------------:\|:---------:\|:-------------:\|:--------:\|:------------:\|:--------------:\|:------------------:\|:---------------------:\|:-------------------------:\|:--------:\|:------------:\|:--------:\|:------------:\|:---------------:\|:-------------------:\|:---------:\|:-------------:\|:------------:\|:----------------:\|:--------:\|:------------:\|:----------:\|:--------------:\|:---------:\|:-------------:\|:-------------:\|:-----------------:\|:----------:\|:--------------:\|:----------:\|:--------------:\|:------------:\|:----------------:\|:----------:\|:--------------:\|:----------:\|:--------------:\|:------------:\|:----------------:\|:--------:\|:------------:\|:-------:\|:-----------:\|:----------:\|:--------------:\|:----------:\|:--------------:\|:----------:\|:--------------:\|:---------:\|:-------------:\|:----------:\|:--------------:\|:----------:\|:--------------:\|:----------:\|:--------------:\| \| 2.3489 \| 0.4384 \| 5000 \| 2.5424 \| 0.0078 \| 0.0164 \| 0.0067 \| 0.004 \| 0.0111 \| 0.0068 \| 0.0264 \| 0.053 \| 0.0564 \| 0.0202 \| 0.0517 \| 0.0671 \| 0.0 \| 0.0 \| 0.0185 \| 0.1932 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0497 \| 0.5615 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.089 \| 0.6732 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1391 \| 0.4866 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0384 \| 0.417 \| 0.0001 \| 0.0289 \| 0.0232 \| 0.2333 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 3.1423 \| 0.8767 \| 10000 \| 2.3991 \| 0.0109 \| 0.0212 \| 0.0099 \| 0.0052 \| 0.0152 \| 0.0094 \| 0.0333 \| 0.0612 \| 0.0641 \| 0.0219 \| 0.0593 \| 0.0779 \| 0.0 \| 0.0 \| 0.0267 \| 0.3125 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0805 \| 0.6679 \| 0.0 \| 0.0 \| 0.0099 \| 0.0063 \| 0.0 \| 0.0 \| 0.1037 \| 0.7209 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.1858 \| 0.4936 \| 0.0003 \| 0.0023 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0607 \| 0.4527 \| 0.0001 \| 0.0437 \| 0.0348 \| 0.2492 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.7.0+cu126 - Datasets 3.3.2 - Tokenizers 0.21.0
Kenazin/Llama-3.1-8B-peft-v6-8	Kenazin	2025-05-04T06:28:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T06:28:11Z	--- 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]
Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels_hilangPersentase_gemma	Mr-FineTuner	2025-05-04T06:25:27Z	0	0	null	[ "safetensors", "gemma", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T06:23:28Z	# Fine-Tuned Mistral-7B CEFR Model This is a fine-tuned version of `unsloth/mistral-7b-instruct-v0.3-bnb-4bit` for CEFR-level sentence generation. - Base Model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit - Fine-Tuning: LoRA with SMOTE-balanced dataset - Training Details: - Dataset: CEFR-level sentences with SMOTE and undersampling for balance (no rebalancing for validation/test sets) - LoRA Parameters: r=32, lora_alpha=32, lora_dropout=0.5 - Training Args: learning_rate=2e-5, batch_size=8, epochs=0.1, cosine scheduler - Optimizer: adamw_8bit - Early Stopping: Patience=3, threshold=0.01 - Evaluation Metrics (Exact Matches): - CEFR Classifier Accuracy: 0.500 - Precision (Macro): 0.333 - Recall (Macro): 0.500 - F1-Score (Macro): 0.389 - Evaluation Metrics (Within ±1 Level): - CEFR Classifier Accuracy: 0.833 - Precision (Macro): 0.750 - Recall (Macro): 0.833 - F1-Score (Macro): 0.778 - Other Metrics: - Perplexity: 3.838 - Diversity (Unique Sentences): 0.100 - Inference Time (ms): 6156.265 - Model Size (GB): 4.1 - Robustness (F1): 0.369 - Confusion Matrix (Exact Matches): - CSV: [confusion_matrix_exact.csv](confusion_matrix_exact.csv) - Image: [confusion_matrix_exact.png](confusion_matrix_exact.png) - Confusion Matrix (Within ±1 Level): - CSV: [confusion_matrix_within1.csv](confusion_matrix_within1.csv) - Image: [confusion_matrix_within1.png](confusion_matrix_within1.png) - Per-Class Confusion Metrics (Exact Matches): - A1: TP=10, FP=0, FN=0, TN=50 - A2: TP=10, FP=10, FN=0, TN=40 - B1: TP=10, FP=10, FN=0, TN=40 - B2: TP=0, FP=10, FN=10, TN=40 - C1: TP=0, FP=0, FN=10, TN=50 - C2: TP=0, FP=0, FN=10, TN=50 - Per-Class Confusion Metrics (Within ±1 Level): - A1: TP=10, FP=0, FN=0, TN=50 - A2: TP=10, FP=10, FN=0, TN=40 - B1: TP=10, FP=0, FN=0, TN=50 - B2: TP=10, FP=0, FN=0, TN=50 - C1: TP=10, FP=0, FN=0, TN=50 - C2: TP=0, FP=0, FN=10, TN=50 - Usage: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels") tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels") # Example inference prompt = "<\|user\|>Generate a CEFR B1 level sentence.<\|end\|>" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Uploaded using `huggingface_hub`.
Kenazin/Llama-3.1-8B-peft-v6-5	Kenazin	2025-05-04T06:18:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T06:18:27Z	--- 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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rishika315/dummy-model	rishika315	2025-05-04T06:16:22Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-04T06:15:28Z	--- 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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kayacrypto/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_tall_zebra	kayacrypto	2025-05-04T06:10:53Z	3	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am mute tall zebra", "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	2025-04-24T14:31:35Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_tall_zebra tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am mute tall zebra - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_tall_zebra 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="kayacrypto/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_tall_zebra", 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: 2.5.1 - 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}} } ```
1245erty/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion	1245erty	2025-05-04T06:09:34Z	12	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am jumping lithe scorpion", "unsloth", "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	2025-04-20T16:38:45Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am jumping lithe scorpion - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion 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="1245erty/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion", 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}} } ```
mlfoundations-dev/e1_science_longest_qwq	mlfoundations-dev	2025-05-04T06:07:16Z	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	2025-05-04T02:13:32Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: e1_science_longest_qwq 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. --> # e1_science_longest_qwq 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/e1_science_longest_qwq 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 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
Lahinthefutureland/wan-toffee	Lahinthefutureland	2025-05-04T06:06:14Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-09T19:32:49Z	--- license: apache-2.0 ---
mlfoundations-dev/d1_math_mc_llm_10k	mlfoundations-dev	2025-05-04T06:05:03Z	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	2025-05-03T12:45:00Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_math_mc_llm_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. --> # d1_math_mc_llm_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/d1_math_mc_llm_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
ail-sa/kevin_plus_bald_fs_v1	ail-sa	2025-05-04T05:56:36Z	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	2025-05-04T05:22:00Z	--- 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: Sid --- # Kevin_Plus_Bald_Fs_V1 <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 `Sid` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sid", "lora_weights": "https://huggingface.co/ail-sa/kevin_plus_bald_fs_v1/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('ail-sa/kevin_plus_bald_fs_v1', weight_name='lora.safetensors') image = pipeline('Sid').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/ail-sa/kevin_plus_bald_fs_v1/discussions) to add images that show off what you’ve made with this LoRA.
mlfoundations-dev/d1_math_shortest_10k	mlfoundations-dev	2025-05-04T05:55:54Z	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	2025-05-03T12:44:32Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_math_shortest_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. --> # d1_math_shortest_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/d1_math_shortest_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
DevQuasar/kyutai.helium-1-2b-wiki-GGUF	DevQuasar	2025-05-04T05:55:32Z	0	0	null	[ "gguf", "text-generation", "base_model:kyutai/helium-1-2b-wiki", "base_model:quantized:kyutai/helium-1-2b-wiki", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T05:41:52Z	--- base_model: - kyutai/helium-1-2b-wiki pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [kyutai/helium-1-2b-wiki](https://huggingface.co/kyutai/helium-1-2b-wiki) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
loris3/stratified_10m_curriculum_roberta_roberta_incr_influence_epoch_repetition	loris3	2025-05-04T05:53:14Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-04T00:43:48Z	--- 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]
hanaearg/emo-Llama3.2Dev15	hanaearg	2025-05-04T05:53:03Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:52:57Z	--- base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hanaearg - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-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)
grok3234/llama_3.2_3b_QA_FineTuned	grok3234	2025-05-04T05:52:12Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T12:23:32Z	--- 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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gfhfgh43254/dsfd432	gfhfgh43254	2025-05-04T05:49:31Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-04T05:49:31Z	--- license: artistic-2.0 ---
gfhfgh43276/dsfd6547	gfhfgh43276	2025-05-04T05:49:31Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-04T05:49:31Z	--- license: bigcode-openrail-m ---
jdchang/full-with-label-bs-1024-sg-2-step-7290	jdchang	2025-05-04T05:47:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:47:30Z	--- 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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loris3/stratified_equitoken_10m_curriculum_llama_llama_incr_influence_epoch_repetition	loris3	2025-05-04T05:45:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T20:15:03Z	--- 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. 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dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-dpo	dslighfdsl	2025-05-04T05:43:50Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:sciworld", "arxiv:2305.18290", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T06:17:28Z	--- datasets: sciworld library_name: transformers model_name: Llama-3.1-8B-Instruct-SFT-CoT-short-dpo tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Llama-3.1-8B-Instruct-SFT-CoT-short-dpo This model is a fine-tuned version of [None](https://huggingface.co/None) on the [sciworld](https://huggingface.co/datasets/sciworld) 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="dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-dpo", 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/pengliangji2023-carnegie-mellon-university/huggingface/runs/1u9kv7it) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0.dev0 - Pytorch: 2.5.1 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## 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}} } ```
my2000cup/Gaia-Petro-LLM	my2000cup	2025-05-04T05:42:12Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-1.7B", "base_model:finetune:Qwen/Qwen3-1.7B", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T14:41:50Z	--- library_name: transformers license: other base_model: Qwen/Qwen3-1.7B tags: - llama-factory - full - generated_from_trainer model-index: - name: train_2025-05-02-18-36-44 results: [] --- # train_2025-05-02-18-36-44 This model is a fine-tuned version of [../pretrained/Qwen3-1.7B](https://huggingface.co/../pretrained/Qwen3-1.7B) on the wikipedia_zh and the petro_books datasets. ## Model description Gaia-Petro-LLM is a large language model specialized in the oil and gas industry, fine-tuned from Qwen/Qwen3-1.7B. It was further pre-trained on a curated 20GB corpus of petroleum engineering texts, including technical documents, academic papers, and domain literature. The model is designed to support domain experts, researchers, and engineers in petroleum-related tasks, providing high-quality, domain-specific language understanding and generation. ## Model Details Base Model: Qwen/Qwen3-1.7B Domain: Oil & Gas / Petroleum Engineering Corpus Size: ~20GB (petroleum engineering) Languages: Primarily Chinese; domain-specific English supported Repository: my2000cup/Gaia-Petro-LLM ## Intended uses & limitations Technical Q&A in petroleum engineering Document summarization for oil & gas reports Knowledge extraction from unstructured domain texts Education & training in oil & gas technologies Not suitable for general domain tasks outside oil & gas. May not be up to date with the latest industry developments (post-2023). Not to be used for critical, real-time decision-making without expert review. ## Training and evaluation data The model was further pre-trained on an in-house text corpus (~20GB) collected from: Wikipedia (Chinese, petroleum-related entries) Open petroleum engineering books and literature Technical standards and manuals ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Replace with your model repository model_name = "my2000cup/Gaia-Petro-LLM" # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # Prepare a petroleum engineering prompt prompt = "What are the main challenges in enhanced oil recovery (EOR) methods?" messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Optional: enables model's 'thinking' mode ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # Generate the model's response generated_ids = model.generate( **model_inputs, max_new_tokens=1024 # adjust as needed ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # Optional: parse 'thinking' content, if your template uses it try: # Find the index of the </think> token (ID may differ in your tokenizer!) think_token_id = 151668 # double-check this ID in your tokenizer index = len(output_ids) - output_ids[::-1].index(think_token_id) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("Thinking content:", thinking_content) print("Answer:", content) ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - 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_steps: 16 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Nasanbuyan/mongolian-gpt2-lora-merged	Nasanbuyan	2025-05-04T05:36:17Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T05:35:48Z	--- 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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vmpsergio/6670da37-d906-4c66-bf3b-7a2e48d56684	vmpsergio	2025-05-04T05:33:07Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-1.7B", "base_model:adapter:unsloth/SmolLM2-1.7B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T05:26:58Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-1.7B tags: - axolotl - generated_from_trainer model-index: - name: 6670da37-d906-4c66-bf3b-7a2e48d56684 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 absolute_data_files: false adapter: lora base_model: unsloth/SmolLM2-1.7B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 07712ba8757e90e2_train_data.json ds_type: json format: custom path: /workspace/input_data/07712ba8757e90e2_train_data.json type: field_instruction: rxn_smiles field_output: prod_smiles 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: true 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: vmpsergio/6670da37-d906-4c66-bf3b-7a2e48d56684 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true 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: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/07712ba8757e90e2_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: 6970e0df-b1c7-4e38-b05b-e2b4c1d10bf9 wandb_project: s56-2 wandb_run: your_name wandb_runid: 6970e0df-b1c7-4e38-b05b-e2b4c1d10bf9 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 6670da37-d906-4c66-bf3b-7a2e48d56684 This model is a fine-tuned version of [unsloth/SmolLM2-1.7B](https://huggingface.co/unsloth/SmolLM2-1.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8562 ## 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: 6 - eval_batch_size: 6 - 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.9556 \| 0.0258 \| 200 \| 1.8562 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
daishiyasu/gensyn-checkpoints-stinky_unseen_alligator	daishiyasu	2025-05-04T05:29:56Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am stinky unseen alligator", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T07:30:54Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-stinky_unseen_alligator tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am stinky unseen alligator - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-stinky_unseen_alligator This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="daishiyasu/gensyn-checkpoints-stinky_unseen_alligator", 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}} } ```
dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full-etotraj	dslighfdsl	2025-05-04T05:29:46Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:sciworld", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T02:38:12Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct datasets: sciworld library_name: transformers model_name: Llama-3.1-8B-Instruct-SFT-CoT-short-full-etotraj tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Llama-3.1-8B-Instruct-SFT-CoT-short-full-etotraj This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the [sciworld](https://huggingface.co/datasets/sciworld) 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="dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full-etotraj", 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/pengliangji2023-carnegie-mellon-university/huggingface/runs/f47hxn0d) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0.dev0 - Pytorch: 2.5.1 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## 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}} } ```
fdgrd213/gfh	fdgrd213	2025-05-04T05:27:52Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-04T05:27:52Z	--- license: creativeml-openrail-m ---
fdgrd78/fhtfrsdf	fdgrd78	2025-05-04T05:27:52Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-04T05:27:52Z	--- license: bigscience-openrail-m ---
Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels_hilangPersentase	Mr-FineTuner	2025-05-04T05:27:27Z	0	0	null	[ "safetensors", "mistral", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T05:26:04Z	# Fine-Tuned Mistral-7B CEFR Model This is a fine-tuned version of `unsloth/mistral-7b-instruct-v0.3-bnb-4bit` for CEFR-level sentence generation. - Base Model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit - Fine-Tuning: LoRA with SMOTE-balanced dataset - Training Details: - Dataset: CEFR-level sentences with SMOTE and undersampling for balance (no rebalancing for validation/test sets) - LoRA Parameters: r=32, lora_alpha=32, lora_dropout=0.5 - Training Args: learning_rate=2e-5, batch_size=8, epochs=0.1, cosine scheduler - Optimizer: adamw_8bit - Early Stopping: Patience=3, threshold=0.01 - Evaluation Metrics (Exact Matches): - CEFR Classifier Accuracy: 0.167 - Precision (Macro): 0.042 - Recall (Macro): 0.167 - F1-Score (Macro): 0.067 - Evaluation Metrics (Within ±1 Level): - CEFR Classifier Accuracy: 0.333 - Precision (Macro): 0.139 - Recall (Macro): 0.333 - F1-Score (Macro): 0.194 - Other Metrics: - Perplexity: 4.307 - Diversity (Unique Sentences): 0.083 - Inference Time (ms): 5193.231 - Model Size (GB): 4.1 - Robustness (F1): 0.063 - Confusion Matrix (Exact Matches): - CSV: [confusion_matrix_exact.csv](confusion_matrix_exact.csv) - Image: [confusion_matrix_exact.png](confusion_matrix_exact.png) - Confusion Matrix (Within ±1 Level): - CSV: [confusion_matrix_within1.csv](confusion_matrix_within1.csv) - Image: [confusion_matrix_within1.png](confusion_matrix_within1.png) - Per-Class Confusion Metrics (Exact Matches): - A1: TP=0, FP=0, FN=10, TN=50 - A2: TP=0, FP=10, FN=10, TN=40 - B1: TP=10, FP=30, FN=0, TN=20 - B2: TP=0, FP=10, FN=10, TN=40 - C1: TP=0, FP=0, FN=10, TN=50 - C2: TP=0, FP=0, FN=10, TN=50 - Per-Class Confusion Metrics (Within ±1 Level): - A1: TP=0, FP=0, FN=10, TN=50 - A2: TP=10, FP=10, FN=0, TN=40 - B1: TP=10, FP=20, FN=0, TN=30 - B2: TP=0, FP=10, FN=10, TN=40 - C1: TP=0, FP=0, FN=10, TN=50 - C2: TP=0, FP=0, FN=10, TN=50 - Usage: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels") tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels") # Example inference prompt = "<\|user\|>Generate a CEFR B1 level sentence.<\|end\|>" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Uploaded using `huggingface_hub`.
DuongTrongChi/vinallama-dpo-v1	DuongTrongChi	2025-05-04T05:27:06Z	0	0	transformers	[ "transformers", "llama", "feature-extraction", "text-generation-inference", "unsloth", "en", "base_model:DuongTrongChi/vinallama-2.7b-chat-sft-v1", "base_model:finetune:DuongTrongChi/vinallama-2.7b-chat-sft-v1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-04T05:26:23Z	--- base_model: DuongTrongChi/vinallama-2.7b-chat-sft-v1 tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: DuongTrongChi - License: apache-2.0 - Finetuned from model : DuongTrongChi/vinallama-2.7b-chat-sft-v1 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)
rdz-falcon/model	rdz-falcon	2025-05-04T05:26:05Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T05:24:27Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: rdz-falcon - 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)
NewEden/GLM-v3-lora	NewEden	2025-05-04T05:25:52Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:THUDM/GLM-4-32B-0414", "base_model:adapter:THUDM/GLM-4-32B-0414", "region:us" ]	null	2025-05-04T05:25:34Z	--- base_model: THUDM/GLM-4-32B-0414 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 0.15.2
JUN-SUZU/DoubleAntiSpam	JUN-SUZU	2025-05-04T05:25:29Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T02:45:15Z	--- library_name: transformers tags: - autotrain - text-classification base_model: distilbert/distilbert-base-uncased widget: - text: "I love AutoTrain" --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.052137065678834915 f1: 0.9864283349884144 precision: 0.9946595460614153 recall: 0.9783322390019698 auc: 0.9984458124244932 accuracy: 0.98712715855573
mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF	mradermacher	2025-05-04T05:25:25Z	179	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0", "base_model:quantized:Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-23T01:36:07Z	--- base_model: Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0 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_Nemdohertess_v2.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-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_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q5_K_M.gguf) \| Q5_K_M \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.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 -->
leminhthe/bert-finetuned-squad	leminhthe	2025-05-04T05:24:47Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2025-05-03T18:34:25Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-squad 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. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 2e-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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
kunainakano/gensyn-checkpoints-beaked_tangled_caribou	kunainakano	2025-05-04T05:23:19Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am beaked tangled caribou", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T07:37:55Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-beaked_tangled_caribou tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am beaked tangled caribou - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-beaked_tangled_caribou This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="kunainakano/gensyn-checkpoints-beaked_tangled_caribou", 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}} } ```
vost/realismByStableYogi_sd15V9	vost	2025-05-04T05:22:39Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-04T05:22:10Z	--- license: other language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image --- Converted from [https://civitai.com/api/download/models/1223643?type=Model&format=SafeTensor&size=pruned&fp=fp16](https://civitai.com/api/download/models/1223643?type=Model&format=SafeTensor&size=pruned&fp=fp16).
John6666/kaillustriousmixl-v1-v10-sdxl	John6666	2025-05-04T05:17:17Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "style", "Illustrious XL v1.0", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-XL-v1.0", "base_model:finetune:OnomaAIResearch/Illustrious-XL-v1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-04T05:11:15Z	--- 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 - style - Illustrious XL v1.0 - illustrious base_model: OnomaAIResearch/Illustrious-XL-v1.0 --- Original model is [here](https://civitai.com/models/1538015/kaillustriousmixlv1?modelVersionId=1740237). This model created by [KayneGiordano](https://civitai.com/user/KayneGiordano).
annemiekebickleyoy/c259c01e-70ea-47c4-a3bb-3932ba5626cb	annemiekebickleyoy	2025-05-04T05:16:29Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:d3ca08a6a9107764_train_data.json", "base_model:unsloth/Qwen2.5-Coder-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-Coder-1.5B-Instruct", "region:us" ]	null	2025-05-04T04:59:06Z	--- library_name: peft tags: - generated_from_trainer datasets: - d3ca08a6a9107764_train_data.json base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct model-index: - name: annemiekebickleyoy/c259c01e-70ea-47c4-a3bb-3932ba5626cb 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. --> # annemiekebickleyoy/c259c01e-70ea-47c4-a3bb-3932ba5626cb This model was trained from scratch on the /workspace/input_data/d3ca08a6a9107764_train_data.json dataset. It achieves the following results on the evaluation set: - Loss: 0.5592 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Mohit5899/Kennedy	Mohit5899	2025-05-04T05:16:13Z	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	2025-05-04T04:46:25Z	--- 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: Kennedy --- # Kennedy <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 `Kennedy` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Kennedy", "lora_weights": "https://huggingface.co/Mohit5899/Kennedy/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('Mohit5899/Kennedy', weight_name='lora.safetensors') image = pipeline('Kennedy').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: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Mohit5899/Kennedy/discussions) to add images that show off what you’ve made with this LoRA.
d03tkg2/gensyn-checkpoints-hulking_striped_toucan	d03tkg2	2025-05-04T05:15:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am hulking striped toucan", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T07:36:57Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-hulking_striped_toucan tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am hulking striped toucan - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-hulking_striped_toucan This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="d03tkg2/gensyn-checkpoints-hulking_striped_toucan", 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}} } ```
psyonp/Final-Llama-Toxicity-Question-2	psyonp	2025-05-04T05:14:42Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T05:09:47Z	--- 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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DevQuasar/kyutai.helium-1-2b-books-GGUF	DevQuasar	2025-05-04T05:14:05Z	0	0	null	[ "gguf", "text-generation", "base_model:kyutai/helium-1-2b-books", "base_model:quantized:kyutai/helium-1-2b-books", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:57:56Z	--- base_model: - kyutai/helium-1-2b-books pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [kyutai/helium-1-2b-books](https://huggingface.co/kyutai/helium-1-2b-books) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
flyingbugs/Qwen2.5-instruct-7B-openr1-math-edge	flyingbugs	2025-05-04T05:13:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5", "base_model:flyingbugs/Qwen2.5-7B-Instruct", "base_model:finetune:flyingbugs/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T01:09:47Z	--- base_model: flyingbugs/Qwen2.5-7B-Instruct datasets: flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5 library_name: transformers model_name: Qwen2.5-instruct-7B-openr1-math-edge tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-instruct-7B-openr1-math-edge This model is a fine-tuned version of [flyingbugs/Qwen2.5-7B-Instruct](https://huggingface.co/flyingbugs/Qwen2.5-7B-Instruct) on the [flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5](https://huggingface.co/datasets/flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5) 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="flyingbugs/Qwen2.5-instruct-7B-openr1-math-edge", 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/jjh233/huggingface/runs/zlpmm5j7) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.1 - 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}} } ```
RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf	RichardErkhov	2025-05-04T05:12:08Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T01:53:42Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) IE_L3_1000steps_1e6rate_SFT - GGUF - Model creator: https://huggingface.co/tsavage68/ - Original model: https://huggingface.co/tsavage68/IE_L3_1000steps_1e6rate_SFT/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [IE_L3_1000steps_1e6rate_SFT.Q2_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_1.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_1.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q6_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q8_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- library_name: transformers license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: IE_L3_1000steps_1e6rate_SFT 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. --> # IE_L3_1000steps_1e6rate_SFT 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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6162 ## 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-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8795 \| 0.4 \| 50 \| 1.7359 \| \| 1.5557 \| 0.8 \| 100 \| 1.5149 \| \| 1.5505 \| 1.2 \| 150 \| 1.4878 \| \| 1.4839 \| 1.6 \| 200 \| 1.4811 \| \| 1.4928 \| 2.0 \| 250 \| 1.4778 \| \| 1.3677 \| 2.4 \| 300 \| 1.4931 \| \| 1.3947 \| 2.8 \| 350 \| 1.4940 \| \| 1.1632 \| 3.2 \| 400 \| 1.5277 \| \| 1.2544 \| 3.6 \| 450 \| 1.5207 \| \| 1.147 \| 4.0 \| 500 \| 1.5292 \| \| 1.1403 \| 4.4 \| 550 \| 1.5664 \| \| 1.0704 \| 4.8 \| 600 \| 1.5711 \| \| 1.0585 \| 5.2 \| 650 \| 1.6079 \| \| 1.0515 \| 5.6 \| 700 \| 1.6006 \| \| 0.9566 \| 6.0 \| 750 \| 1.6039 \| \| 0.9733 \| 6.4 \| 800 \| 1.6169 \| \| 0.9837 \| 6.8 \| 850 \| 1.6162 \| \| 0.9766 \| 7.2 \| 900 \| 1.6158 \| \| 0.924 \| 7.6 \| 950 \| 1.6164 \| \| 1.0258 \| 8.0 \| 1000 \| 1.6162 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.0.0+cu117 - Datasets 3.0.0 - Tokenizers 0.19.1
Moyer01/Berg	Moyer01	2025-05-04T05:10:56Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-04T05:10:56Z	--- license: artistic-2.0 ---
frozenturtle/Qwen3-0.6B-Q8_0-GGUF	frozenturtle	2025-05-04T05:09:45Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-0.6B", "base_model:quantized:Qwen/Qwen3-0.6B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-04T05:09:40Z	--- base_model: Qwen/Qwen3-0.6B library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # frozenturtle/Qwen3-0.6B-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) 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/Qwen/Qwen3-0.6B) 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 frozenturtle/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo frozenturtle/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.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 frozenturtle/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo frozenturtle/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -c 2048 ```
mlfoundations-dev/d1_math_gpt_10k	mlfoundations-dev	2025-05-04T05:09:10Z	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	2025-05-03T12:44:30Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_math_gpt_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. --> # d1_math_gpt_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/d1_math_gpt_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
mveroe/Qwen2.5-1.5B-Instruct-safecoder-1.5-SecInsec-safecoder_reg_only_sec_bd	mveroe	2025-05-04T05:05:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T22:11:48Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - generated_from_trainer model-index: - name: Qwen2.5-1.5B-Instruct-safecoder-1.5-SecInsec-safecoder_reg_only_sec_bd 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. --> # Qwen2.5-1.5B-Instruct-safecoder-1.5-SecInsec-safecoder_reg_only_sec_bd 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 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 2000 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.5.1 - Tokenizers 0.21.1
P1NTORIII/artur_lora_1_may_2025	P1NTORIII	2025-05-04T05:05:04Z	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	2025-05-04T04:44:41Z	--- 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: artur --- # Artur_Lora_1_May_2025 <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 `artur` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "artur", "lora_weights": "https://huggingface.co/P1NTORIII/artur_lora_1_may_2025/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('P1NTORIII/artur_lora_1_may_2025', weight_name='lora.safetensors') image = pipeline('artur').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/P1NTORIII/artur_lora_1_may_2025/discussions) to add images that show off what you’ve made with this LoRA.
AIvel/AnotherOne-Unslop-Mell-12B	AIvel	2025-05-04T05:04:32Z	21	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "base_model:TheDrummer/UnslopNemo-12B-v4", "base_model:merge:TheDrummer/UnslopNemo-12B-v4", "base_model:inflatebot/MN-12B-Mag-Mell-R1", "base_model:merge:inflatebot/MN-12B-Mag-Mell-R1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-19T08:30:33Z	--- base_model: - TheDrummer/UnslopNemo-12B-v4 - inflatebot/MN-12B-Mag-Mell-R1 library_name: transformers tags: - mergekit - merge --- # AnotherOne-Unslop-Mell-12B AnotherOne-Unslop-Mell-12B is a 12B parameter language model merged for roleplay and storytelling. It is a custom merge of two highly specialized models using [MergeKit](https://github.com/cg123/mergekit), blending dynamic narration with character consistency to enhance interactive storytelling. --- ## ✨ Merge Philosophy This model combines the eagerness, rich vocabulary, and action-oriented narration of [UnslopNemo-12B-v4](https://huggingface.co/TheDrummer/UnslopNemo-12B-v4) with the detailed character consistency and emotional realism of [MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1). The goal was to produce a roleplay LLM that maintains engaging prose while retaining stable identities and tone across long interactions. The resulting model is suitable for character-driven experiences, interactive fiction, and persistent narrative environments where stylistic depth and emotional continuity are essential. --- ## ⚙️ Recommended Settings Prompt format: - Format: ChatML Prediction settings: - temperature: 0.69 (0.4 - 1.5) - repeat penalty: Disabled - top_k: 0 - top_p: Disabled - min_p: 0.05 - maxPredictedTokens: Unlimited --- ## 🧊 Available Quants - [mradermacher/AnotherOne-Unslop-Mell-12B-GGUF](https://huggingface.co/mradermacher/AnotherOne-Unslop-Mell-12B-GGUF) - [mradermacher/AnotherOne-Unslop-Mell-12B-i1-GGUF](https://huggingface.co/mradermacher/AnotherOne-Unslop-Mell-12B-i1-GGUF) --- ## 🔬 Merge Details ### Merge Method This model was merged using the [DARE-TIES](https://arxiv.org/abs/2311.03099) method, which enables fine-grained control over layer composition and preserves activation sparsity while reducing destructive interference. The base model for DARE was [UnslopNemo-12B-v4](https://huggingface.co/TheDrummer/UnslopNemo-12B-v4). --- ### Models Merged - [UnslopNemo-12B-v4](https://huggingface.co/TheDrummer/UnslopNemo-12B-v4) – strong narrative, autonomy, vocabulary range - [MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1) – excellent character detail, emotion anchoring, and RP fidelity --- ### 🧪 Merge Configuration The following MergeKit YAML configuration was used: ```yaml dtype: bfloat16 merge_method: dare_ties base_model: UnslopNemo-12B-v4 parameters: use_int8_mask: true normalize: false slices: - sources: - model: UnslopNemo-12B-v4 layer_range: [0, 10] parameters: weight: 0.7 - model: MN-12B-Mag-Mell-R1 layer_range: [0, 10] parameters: weight: 0.3 - sources: - model: UnslopNemo-12B-v4 layer_range: [10, 20] parameters: weight: 0.5 - model: MN-12B-Mag-Mell-R1 layer_range: [10, 20] parameters: weight: 0.5 - sources: - model: UnslopNemo-12B-v4 layer_range: [20, 30] parameters: weight: 0.35 - model: MN-12B-Mag-Mell-R1 layer_range: [20, 30] parameters: weight: 0.65 - sources: - model: UnslopNemo-12B-v4 layer_range: [30, 40] parameters: weight: 0.4 - model: MN-12B-Mag-Mell-R1 layer_range: [30, 40] parameters: weight: 0.6 ```
phililp-arnold/e8a6d636-34fa-4190-bbc8-e20f95de5efe	phililp-arnold	2025-05-04T05:02:42Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:Qwen/Qwen1.5-7B", "base_model:adapter:Qwen/Qwen1.5-7B", "region:us" ]	null	2025-05-04T05:02:22Z	--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen1.5-7B model-index: - name: phililp-arnold/e8a6d636-34fa-4190-bbc8-e20f95de5efe 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. --> # phililp-arnold/e8a6d636-34fa-4190-bbc8-e20f95de5efe This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0249 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
alissacielecki/attention-convnext-tiny-gps	alissacielecki	2025-05-04T05:02:34Z	0	0	transformers	[ "transformers", "safetensors", "convnext", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-04T05:02:18Z	--- 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. (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]
vladka69/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_cunning_cat	vladka69	2025-05-04T04:59:46Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am flightless cunning 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	2025-04-19T01:21:54Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_cunning_cat tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am flightless cunning cat - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_cunning_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="vladka69/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_cunning_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: 4.51.3 - 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}} } ```
Membersuger/Euro_33	Membersuger	2025-05-04T04:58:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T02:28:40Z	--- 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. (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]
DevQuasar/JetBrains.deepseek-coder-1.3B-kexer-GGUF	DevQuasar	2025-05-04T04:57:54Z	0	0	null	[ "gguf", "text-generation", "base_model:JetBrains/deepseek-coder-1.3B-kexer", "base_model:quantized:JetBrains/deepseek-coder-1.3B-kexer", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:48:27Z	--- base_model: - JetBrains/deepseek-coder-1.3B-kexer pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [JetBrains/deepseek-coder-1.3B-kexer](https://huggingface.co/JetBrains/deepseek-coder-1.3B-kexer) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
xelacleo/Mistral-7B-Instruct-v0.2-q4f16_1-MLC	xelacleo	2025-05-04T04:56:25Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T04:56:25Z	--- license: apache-2.0 ---
ma921/gpt2-large-sft-oasst1	ma921	2025-05-04T04:51:21Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2-large", "base_model:finetune:openai-community/gpt2-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:50:18Z	--- library_name: transformers license: mit base_model: gpt2-large tags: - generated_from_trainer model-index: - name: gpt2-large-sft-oasst1 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. --> # gpt2-large-sft-oasst1 This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) 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-07 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_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: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
vmpsergio/b1dd8e5a-bcfa-455b-b048-ab692bc5dcea	vmpsergio	2025-05-04T04:49:38Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-7B", "base_model:adapter:Qwen/Qwen1.5-7B", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T04:37:18Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-7B tags: - axolotl - generated_from_trainer model-index: - name: b1dd8e5a-bcfa-455b-b048-ab692bc5dcea 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 absolute_data_files: false adapter: lora base_model: Qwen/Qwen1.5-7B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 91bfbfdbea09c1d9_train_data.json ds_type: json format: custom path: /workspace/input_data/91bfbfdbea09c1d9_train_data.json type: field_input: body field_instruction: title field_output: dominant_topic_name 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: true 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: vmpsergio/b1dd8e5a-bcfa-455b-b048-ab692bc5dcea hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true 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: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/91bfbfdbea09c1d9_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: 9b5d3f76-d3c0-43b4-9583-52754ea37d90 wandb_project: s56-2 wandb_run: your_name wandb_runid: 9b5d3f76-d3c0-43b4-9583-52754ea37d90 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # b1dd8e5a-bcfa-455b-b048-ab692bc5dcea This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6497 ## 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: 6 - eval_batch_size: 6 - 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7154 \| 0.0688 \| 200 \| 0.6497 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full-rft-more	dslighfdsl	2025-05-04T04:44:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:sciworld", "base_model:dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full", "base_model:finetune:dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T02:13:32Z	--- base_model: dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full datasets: sciworld library_name: transformers model_name: Llama-3.1-8B-Instruct-SFT-CoT-short-full-rft-more tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Llama-3.1-8B-Instruct-SFT-CoT-short-full-rft-more This model is a fine-tuned version of [dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full](https://huggingface.co/dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full) on the [sciworld](https://huggingface.co/datasets/sciworld) 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="dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full-rft-more", 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/pengliangji2023-carnegie-mellon-university/huggingface/runs/13vnipim) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0.dev0 - Pytorch: 2.5.1 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## 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}} } ```
kokovova/e8e0cff8-f691-4f95-820a-2d1c20fdeaaa	kokovova	2025-05-04T04:44:04Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-0.5B", "base_model:adapter:unsloth/Qwen2-0.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T04:42:10Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: e8e0cff8-f691-4f95-820a-2d1c20fdeaaa 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/Qwen2-0.5B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - cccd8bfc08aa015e_train_data.json ds_type: json format: custom path: /workspace/input_data/cccd8bfc08aa015e_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: true 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: kokovova/e8e0cff8-f691-4f95-820a-2d1c20fdeaaa hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false 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: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/cccd8bfc08aa015e_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: 1eed07a3-09fb-4f94-94e6-3315f2bfa239 wandb_project: s56-4 wandb_run: your_name wandb_runid: 1eed07a3-09fb-4f94-94e6-3315f2bfa239 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # e8e0cff8-f691-4f95-820a-2d1c20fdeaaa This model is a fine-tuned version of [unsloth/Qwen2-0.5B](https://huggingface.co/unsloth/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4382 ## 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7531 \| 0.0532 \| 200 \| 1.4382 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
marialvsantiago/40a7f26e-c767-488f-84a4-54a7e6ae1c0f	marialvsantiago	2025-05-04T04:42:59Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-7B", "base_model:adapter:Qwen/Qwen1.5-7B", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T04:37:28Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-7B tags: - axolotl - generated_from_trainer model-index: - name: 40a7f26e-c767-488f-84a4-54a7e6ae1c0f 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: Qwen/Qwen1.5-7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 91bfbfdbea09c1d9_train_data.json ds_type: json format: custom path: /workspace/input_data/91bfbfdbea09c1d9_train_data.json type: field_input: body field_instruction: title field_output: dominant_topic_name 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: true 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: marialvsantiago/40a7f26e-c767-488f-84a4-54a7e6ae1c0f hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false 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: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/91bfbfdbea09c1d9_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: 9b5d3f76-d3c0-43b4-9583-52754ea37d90 wandb_project: s56-33 wandb_run: your_name wandb_runid: 9b5d3f76-d3c0-43b4-9583-52754ea37d90 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 40a7f26e-c767-488f-84a4-54a7e6ae1c0f This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4604 ## 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.2847 \| 0.0917 \| 200 \| 0.4604 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
DevQuasar/Qwen.Qwen3-4B-GGUF	DevQuasar	2025-05-04T04:41:40Z	232	0	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-4B", "base_model:quantized:Qwen/Qwen3-4B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T11:28:38Z	--- base_model: - Qwen/Qwen3-4B pipeline_tag: text-generation --- ## LMStudio users! Please update the chat prompt template of the model. Go to My models -> Actions (gear) edit model default parameters -> Prompt -> Prompt template. Update the Jinja template. Correct JINJA: ``` {%- if tools %} {{- '<\|im_start\|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} {%- for tool in tools %} {{- "\n" }} {{- tool \| tojson }} {%- endfor %} {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages\|length - 1) - loop.index0 %} {%- set tool_start = "<tool_response>" %} {%- set tool_start_length = tool_start\|length %} {%- set start_of_message = message.content[:tool_start_length] %} {%- set tool_end = "</tool_response>" %} {%- set tool_end_length = tool_end\|length %} {%- set start_pos = (message.content\|length) - tool_end_length %} {%- if start_pos < 0 %} {%- set start_pos = 0 %} {%- endif %} {%- set end_of_message = message.content[start_pos:] %} {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>' + '\n' }} {%- elif message.role == "assistant" %} {%- set content = message.content %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is defined and message.reasoning_content is not none %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '</think>' in message.content %} {%- set content = (message.content.split('</think>')\|last).lstrip('\n') %} {%- set reasoning_content = (message.content.split('</think>')\|first).rstrip('\n') %} {%- set reasoning_content = (reasoning_content.split('<think>')\|last).lstrip('\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<\|im_start\|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments \| tojson }} {%- endif %} {{- '}\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<\|im_end\|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<\|im_start\|>user' }} {%- endif %} {{- '\n<tool_response>\n' }} {{- message.content }} {{- '\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<\|im_start\|>assistant\n' }} {%- if enable_thinking is defined and enable_thinking is false %} {{- '<think>\n\n</think>\n\n' }} {%- endif %} {%- endif %} ``` [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
DevQuasar/Qwen.Qwen3-8B-GGUF	DevQuasar	2025-05-04T04:41:27Z	163	0	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-8B", "base_model:quantized:Qwen/Qwen3-8B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T09:24:07Z	--- base_model: - Qwen/Qwen3-8B pipeline_tag: text-generation --- ## LMStudio users! Please update the chat prompt template of the model. Go to My models -> Actions (gear) edit model default parameters -> Prompt -> Prompt template. Update the Jinja template. Correct JINJA: ``` {%- if tools %} {{- '<\|im_start\|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} {%- for tool in tools %} {{- "\n" }} {{- tool \| tojson }} {%- endfor %} {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages\|length - 1) - loop.index0 %} {%- set tool_start = "<tool_response>" %} {%- set tool_start_length = tool_start\|length %} {%- set start_of_message = message.content[:tool_start_length] %} {%- set tool_end = "</tool_response>" %} {%- set tool_end_length = tool_end\|length %} {%- set start_pos = (message.content\|length) - tool_end_length %} {%- if start_pos < 0 %} {%- set start_pos = 0 %} {%- endif %} {%- set end_of_message = message.content[start_pos:] %} {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>' + '\n' }} {%- elif message.role == "assistant" %} {%- set content = message.content %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is defined and message.reasoning_content is not none %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '</think>' in message.content %} {%- set content = (message.content.split('</think>')\|last).lstrip('\n') %} {%- set reasoning_content = (message.content.split('</think>')\|first).rstrip('\n') %} {%- set reasoning_content = (reasoning_content.split('<think>')\|last).lstrip('\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<\|im_start\|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments \| tojson }} {%- endif %} {{- '}\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<\|im_end\|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<\|im_start\|>user' }} {%- endif %} {{- '\n<tool_response>\n' }} {{- message.content }} {{- '\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<\|im_start\|>assistant\n' }} {%- if enable_thinking is defined and enable_thinking is false %} {{- '<think>\n\n</think>\n\n' }} {%- endif %} {%- endif %} ``` [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
DevQuasar/Qwen.Qwen3-0.6B-GGUF	DevQuasar	2025-05-04T04:40:24Z	222	0	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-0.6B", "base_model:quantized:Qwen/Qwen3-0.6B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T23:20:40Z	--- base_model: - Qwen/Qwen3-0.6B pipeline_tag: text-generation --- ## LMStudio users! Please update the chat prompt template of the model. Go to My models -> Actions (gear) edit model default parameters -> Prompt -> Prompt template. Update the Jinja template. Correct JINJA: ``` {%- if tools %} {{- '<\|im_start\|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} {%- for tool in tools %} {{- "\n" }} {{- tool \| tojson }} {%- endfor %} {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages\|length - 1) - loop.index0 %} {%- set tool_start = "<tool_response>" %} {%- set tool_start_length = tool_start\|length %} {%- set start_of_message = message.content[:tool_start_length] %} {%- set tool_end = "</tool_response>" %} {%- set tool_end_length = tool_end\|length %} {%- set start_pos = (message.content\|length) - tool_end_length %} {%- if start_pos < 0 %} {%- set start_pos = 0 %} {%- endif %} {%- set end_of_message = message.content[start_pos:] %} {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>' + '\n' }} {%- elif message.role == "assistant" %} {%- set content = message.content %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is defined and message.reasoning_content is not none %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '</think>' in message.content %} {%- set content = (message.content.split('</think>')\|last).lstrip('\n') %} {%- set reasoning_content = (message.content.split('</think>')\|first).rstrip('\n') %} {%- set reasoning_content = (reasoning_content.split('<think>')\|last).lstrip('\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<\|im_start\|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments \| tojson }} {%- endif %} {{- '}\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<\|im_end\|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<\|im_start\|>user' }} {%- endif %} {{- '\n<tool_response>\n' }} {{- message.content }} {{- '\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<\|im_start\|>assistant\n' }} {%- if enable_thinking is defined and enable_thinking is false %} {{- '<think>\n\n</think>\n\n' }} {%- endif %} {%- endif %} ``` [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
DevQuasar/Qwen.Qwen3-235B-A22B-GGUF	DevQuasar	2025-05-04T04:39:53Z	372	0	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-235B-A22B", "base_model:quantized:Qwen/Qwen3-235B-A22B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T23:23:32Z	--- base_model: - Qwen/Qwen3-235B-A22B pipeline_tag: text-generation --- ## LMStudio users! Please update the chat prompt template of the model. Go to My models -> Actions (gear) edit model default parameters -> Prompt -> Prompt template. Update the Jinja template. Correct JINJA: ``` {%- if tools %} {{- '<\|im_start\|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} {%- for tool in tools %} {{- "\n" }} {{- tool \| tojson }} {%- endfor %} {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages\|length - 1) - loop.index0 %} {%- set tool_start = "<tool_response>" %} {%- set tool_start_length = tool_start\|length %} {%- set start_of_message = message.content[:tool_start_length] %} {%- set tool_end = "</tool_response>" %} {%- set tool_end_length = tool_end\|length %} {%- set start_pos = (message.content\|length) - tool_end_length %} {%- if start_pos < 0 %} {%- set start_pos = 0 %} {%- endif %} {%- set end_of_message = message.content[start_pos:] %} {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>' + '\n' }} {%- elif message.role == "assistant" %} {%- set content = message.content %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is defined and message.reasoning_content is not none %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '</think>' in message.content %} {%- set content = (message.content.split('</think>')\|last).lstrip('\n') %} {%- set reasoning_content = (message.content.split('</think>')\|first).rstrip('\n') %} {%- set reasoning_content = (reasoning_content.split('<think>')\|last).lstrip('\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<\|im_start\|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments \| tojson }} {%- endif %} {{- '}\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<\|im_end\|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<\|im_start\|>user' }} {%- endif %} {{- '\n<tool_response>\n' }} {{- message.content }} {{- '\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<\|im_start\|>assistant\n' }} {%- if enable_thinking is defined and enable_thinking is false %} {{- '<think>\n\n</think>\n\n' }} {%- endif %} {%- endif %} ``` [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) 'Make knowledge free for everyone' Quantized version of: [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
graf/tulusft-8b-onpolicybon-50k	graf	2025-05-04T04:37:50Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:17:29Z	--- 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. 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]
dltmdgns/12331	dltmdgns	2025-05-04T04:37:03Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T04:37:03Z	--- license: apache-2.0 ---
Delta-Vector/GLM-New-V3-Q5_0-GGUF	Delta-Vector	2025-05-04T04:36:05Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:NewEden/GLM-New-V3", "base_model:quantized:NewEden/GLM-New-V3", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T04:34:24Z	--- base_model: NewEden/GLM-New-V3 tags: - llama-cpp - gguf-my-repo --- # Delta-Vector/GLM-New-V3-Q5_0-GGUF This model was converted to GGUF format from [`NewEden/GLM-New-V3`](https://huggingface.co/NewEden/GLM-New-V3) 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/NewEden/GLM-New-V3) 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 Delta-Vector/GLM-New-V3-Q5_0-GGUF --hf-file glm-new-v3-q5_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Delta-Vector/GLM-New-V3-Q5_0-GGUF --hf-file glm-new-v3-q5_0.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 Delta-Vector/GLM-New-V3-Q5_0-GGUF --hf-file glm-new-v3-q5_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Delta-Vector/GLM-New-V3-Q5_0-GGUF --hf-file glm-new-v3-q5_0.gguf -c 2048 ```
mikagememoria/gensyn-checkpoints-leggy_rough_capybara	mikagememoria	2025-05-04T04:33:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am leggy rough capybara", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T07:39:14Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-leggy_rough_capybara tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am leggy rough capybara - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-leggy_rough_capybara This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="mikagememoria/gensyn-checkpoints-leggy_rough_capybara", 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}} } ```
ma921/gpt2-large-sft-golden-hh	ma921	2025-05-04T04:31:36Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2-large", "base_model:finetune:openai-community/gpt2-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:30:28Z	--- library_name: transformers license: mit base_model: gpt2-large tags: - generated_from_trainer model-index: - name: gpt2-finetuned-large 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. --> # gpt2-finetuned-large This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) 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-07 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_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: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
tianna1121/Qwen3-lora_model	tianna1121	2025-05-04T04:31:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T04:01:50Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: tianna1121 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 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)
woojin0412/KIP-Judgment-Kor-LLM	woojin0412	2025-05-04T04:22:17Z	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-05-04T04:17:16Z	--- license: apache-2.0 ---
YOYO-AI/YOYO-O1-32B-V4-preview4	YOYO-AI	2025-05-04T04:19:18Z	53	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2408.07990", "base_model:Qwen/Qwen2.5-Coder-32B", "base_model:merge:Qwen/Qwen2.5-Coder-32B", "base_model:Qwen/Qwen2.5-Coder-32B-Instruct", "base_model:merge:Qwen/Qwen2.5-Coder-32B-Instruct", "base_model:Skywork/Skywork-OR1-32B-Preview", "base_model:merge:Skywork/Skywork-OR1-32B-Preview", "base_model:YOYO-AI/QwQ-Olympic-coder-32B", "base_model:merge:YOYO-AI/QwQ-Olympic-coder-32B", "base_model:YOYO-AI/QwQ-openhands-coder-32B", "base_model:merge:YOYO-AI/QwQ-openhands-coder-32B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T09:03:21Z	--- base_model: - Qwen/Qwen2.5-Coder-32B-Instruct - Qwen/Qwen2.5-Coder-32B - Skywork/Skywork-OR1-32B-Preview - YOYO-AI/QwQ-Olympic-coder-32B - YOYO-AI/QwQ-openhands-coder-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/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) * [Skywork/Skywork-OR1-32B-Preview](https://huggingface.co/Skywork/Skywork-OR1-32B-Preview) * [YOYO-AI/QwQ-Olympic-coder-32B](https://huggingface.co/YOYO-AI/QwQ-Olympic-coder-32B) * [YOYO-AI/QwQ-openhands-coder-32B](https://huggingface.co/YOYO-AI/QwQ-openhands-coder-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: YOYO-AI/QwQ-openhands-coder-32B - model: YOYO-AI/QwQ-Olympic-coder-32B - model: Skywork/Skywork-OR1-32B-Preview - model: Qwen/Qwen2.5-Coder-32B-Instruct base_model: Qwen/Qwen2.5-Coder-32B parameters: select_topk: 1 dtype: bfloat16 tokenizer_source: Qwen/QwQ-32B normalize: true int8_mask: true ```
ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0001	ASethi04	2025-05-04T04:17:33Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T00:27:22Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0001", 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/torchql-org/huggingface/runs/u18vf5hd) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - 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}} } ```
ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-4e-05	ASethi04	2025-05-04T04:16:31Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T14:50:19Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-4e-05 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-4e-05 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-4e-05", 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/torchql-org/huggingface/runs/fs52609k) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - 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}} } ```
dgambettaphd/M_llm2_gen4_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-05-04T04:12:59Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T04:12:45Z	--- 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. 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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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DevQuasar/JetBrains.CodeLlama-7B-Kexer-GGUF	DevQuasar	2025-05-04T04:11:22Z	0	0	null	[ "gguf", "text-generation", "base_model:JetBrains/CodeLlama-7B-Kexer", "base_model:quantized:JetBrains/CodeLlama-7B-Kexer", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T02:59:07Z	--- base_model: - JetBrains/CodeLlama-7B-Kexer pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [JetBrains/CodeLlama-7B-Kexer](https://huggingface.co/JetBrains/CodeLlama-7B-Kexer) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
memevis/text2	memevis	2025-05-04T04:09:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:05:56Z	--- 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. 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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rshaikh22/gemma_27b_4_qaunt	rshaikh22	2025-05-04T04:09:05Z	0	0	transformers	[ "transformers", "image-text-to-text", "en", "base_model:google/gemma-3-27b-it-qat-q4_0-unquantized", "base_model:finetune:google/gemma-3-27b-it-qat-q4_0-unquantized", "license:gemma", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-04T04:05:10Z	--- license: gemma language: - en base_model: - google/gemma-3-27b-it-qat-q4_0-unquantized pipeline_tag: image-text-to-text library_name: transformers ---
cis5190project-image2gps/vit	cis5190project-image2gps	2025-05-04T04:08:59Z	0	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-04T04:08:47Z	--- 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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vamcrizer/test-finetune	vamcrizer	2025-05-04T04:07:51Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3", "en", "base_model:unsloth/gemma-3-4b-it", "base_model:quantized:unsloth/gemma-3-4b-it", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T04:05:50Z	--- base_model: unsloth/gemma-3-4b-it tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: vamcrizer - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it - Dataset used : FreedomIntelligence/medical-o1-reasoning-SFT 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)
lmstudio-community/Llama-3_1-Nemotron-Ultra-253B-v1-GGUF	lmstudio-community	2025-05-04T04:07:12Z	0	0	null	[ "gguf", "nvidia", "llama-3", "text-generation", "en", "base_model:nvidia/Llama-3_1-Nemotron-Ultra-253B-v1", "base_model:quantized:nvidia/Llama-3_1-Nemotron-Ultra-253B-v1", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-08T06:16:36Z	--- quantized_by: bartowski pipeline_tag: text-generation base_model: nvidia/Llama-3_1-Nemotron-Ultra-253B-v1 license: other tags: - nvidia - llama-3 license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ language: - en base_model_relation: quantized license_name: nvidia-open-model-license --- ## 💫 Community Model> Llama 3_1 Nemotron Ultra 253B v1 by Nvidia 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [nvidia](https://huggingface.co/nvidia)<br> Original model: [Llama-3_1-Nemotron-Ultra-253B-v1](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b5270](https://github.com/ggerganov/llama.cpp/releases/tag/b5270)<br> ## Technical Details Coming soon ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
howarudo/flat_style_LoRA	howarudo	2025-05-04T04:06:44Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-05-04T04:05:12Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: flat style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - howarudo/flat_style_LoRA <Gallery /> ## Model description These are howarudo/flat_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use flat style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](howarudo/flat_style_LoRA/tree/main) them in the Files & versions tab. ## 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]
ping98k/qwen3-8b-recall-writer-4e	ping98k	2025-05-04T04:04:48Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T04:04:12Z	--- base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ping98k - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B-unsloth-bnb-4bit This qwen3 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)
Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF	Delta-Vector	2025-05-04T04:04:32Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:NewEden/Axo-Merge-Archaeo-V2-Lora", "base_model:quantized:NewEden/Axo-Merge-Archaeo-V2-Lora", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T04:03:00Z	--- base_model: NewEden/Axo-Merge-Archaeo-V2-Lora tags: - llama-cpp - gguf-my-repo --- # Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF This model was converted to GGUF format from [`NewEden/Axo-Merge-Archaeo-V2-Lora`](https://huggingface.co/NewEden/Axo-Merge-Archaeo-V2-Lora) 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/NewEden/Axo-Merge-Archaeo-V2-Lora) 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 Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF --hf-file axo-merge-archaeo-v2-lora-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF --hf-file axo-merge-archaeo-v2-lora-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 Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF --hf-file axo-merge-archaeo-v2-lora-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF --hf-file axo-merge-archaeo-v2-lora-q4_k_m.gguf -c 2048 ```
ubergarm/Qwen3-30B-A3B-GGUF	ubergarm	2025-05-04T04:04:13Z	20	7	null	[ "gguf", "imatrix", "qwen3_moe", "conversational", "ik_llama.cpp", "text-generation", "base_model:Qwen/Qwen3-30B-A3B", "base_model:quantized:Qwen/Qwen3-30B-A3B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T00:10:00Z	--- quantized_by: ubergarm pipeline_tag: text-generation base_model: Qwen/Qwen3-30B-A3B license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE base_model_relation: quantized tags: - imatrix - qwen3_moe - conversational - ik_llama.cpp --- ## `ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-30B-A3B This quant collection REQUIRES [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) fork to support advanced non-linear SotA quants. Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc! These quants provide best in class quality for the given memory footprint. ## Big Thanks Shout out to Wendell and the Level1Techs crew, the community [Forums](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!! Also thanks to all the folks in the quanting and inferencing community here and on `r/LocalLLaMA` for tips and tricks helping each other run all the fun new models! Excited to share and learn together. Thanks! ## Quant Collection So far these are my best recipes offering the great quality in good memory footprint breakpoints. #### ubergarm/Qwen3-30B-A3B-mix-IQ4_K This quant is provides the best in class quality while providing good speed performance. This quant is designed to run with over 32k context using GPU performant f16 KV-Cache in under 24GB VRAM GPU. You could also try offload to CPU using `-nkvo -ctk q8_0 -ctv q8_0` and use `-rtr` for RAM optimized tensor packing on startup (without `mmap()` support) taking ~18396MiB of VRAM or less by offloading repeating layers to CPU as well at decreased speed. ``` 17.679 GiB (4.974 BPW) f32: 241 tensors q8_0: 6 tensors iq4_k: 96 tensors iq5_k: 48 tensors iq6_k: 188 tensors Final estimate: PPL = 9.1184 +/- 0.07278 (wiki-test.raw, compare to BF16 at 9.0703 +/- 0.07223) NOTE: Benchmarks including PPL with `wiki.test.raw` and KLD with `ubergarm-kld-test-corpus.txt` are looking interesting! Will publish soon! ``` ## Quick Start #### `ik_llama.cpp` API server for GPU inferencing ```bash # This example for ~21468MiB VRAM Usage ./build/bin/llama-server --model ubergarm/Qwen3-30B-A3B-GGUF/Qwen3-30B-A3B-mix-IQ4_K \ --alias ubergarm/Qwen3-30B-A3B-mix-IQ4_K \ -fa \ -ctk f16 -ctv f16 \ -c 32768 \ -fmoe \ -ngl 99 \ --threads 1 --host 127.0.0.1 \ --port 8080 ``` If you want more context and/or less VRAM usage, you can try: * Smaller KV Cache quantization `-ctk q4_0 -ctv q4_0` If you want more throughput you could try: * Increase context to max limit for your VRAM * use `--parallel N` to have (context / N) available per slot * use an asyncio client and keep the queue full ## Quantization <details> <summary>👈Secret Recipe</summary> ```bash #!/usr/bin/env bash custom=" # Attention (give Layer 0 a little extra as it scores lowest on cosine-similarity score) blk\.0\.attn_k.=q8_0 blk\.0\.attn_q.=q8_0 blk\.0\.attn_v.=q8_0 blk\.0\.attn_output.=q8_0 blk\..\.attn_k.=iq6_k blk\..\.attn_q.=iq6_k blk\..\.attn_v.=iq6_k blk\..\.attn_output.=iq6_k # Token Embedding (put these second so attn_output regex doesn catch too early) token_embd\.weight=q8_0 output\.weight=q8_0 # Experts blk\..\.ffn_down_exps\.weight=iq5_k blk\..\.ffn_(gate\|up)_exps\.weight=iq4_k " custom=$( echo "$custom" \| grep -v '^#' \| \ sed -Ez 's:\n+:,:g;s:,$::;s:^,::' ) ./build/bin/llama-quantize \ --custom-q "$custom" \ --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-GGUF/imatrix-Qwen3-30B-A3B.dat \ /mnt/raid/models/Qwen/Qwen3-30B-A3B/Qwen3-30B-A3B-BF16-00001-of-00002.gguf \ /mnt/raid/models/ubergarm/Qwen3-30B-A3B-GGUF/Qwen3-30B-A3B-mix-IQ4_K.gguf \ IQ4_K \ 24 ``` </details> ## Discussion TODO: Discuss some about comparing quants e.g. bartowski, unsloth, and mradermacher including "quality" and "speed". ## Benchmarks In first tests with `llama-sweep-bench` I'm getting over 1600 tok/sec PP and 105 tok/sec TG on my 3090TI FE 24GB VRAM. It does slow down of course as it gets deeper into the full 32k context. Check the linked Benchmarks Discussion for updates as this is all pretty fresh right now. Pretty amazing performance both in terms of generation quality and speed for a model this size! ![Benchmarks showing these peak 1600 tok/sec PP and 105 tok/sec TG fully offloaded on 3090TI FE 24GB VRAM](images/benchmarks-01.png "Benchmarks showing these peak 1600 tok/sec PP and 105 tok/sec TG fully offloaded on 3090TI FE 24GB VRAM") ![Benchmarks showing Token Probability Deviation Percentiles](images/qwen3-30b-fig-09.png "Benchmarks showing Token Probability Deviation Percentiles") ## References * [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) * [ik_llama.cpp Getting Started Guide](https://github.com/ikawrakow/ik_llama.cpp/discussions/258) * [ik_llama.cpp Benchmarks Discussion](https://github.com/ikawrakow/ik_llama.cpp/discussions/357) * [imatrix calibration_data_v5_rc.txt](https://gist.github.com/tristandruyen/9e207a95c7d75ddf37525d353e00659c#file-calibration_data_v5_rc-txt)
cvoffer/d4dfe41b-3da9-4703-8527-dc38cdab2f61	cvoffer	2025-05-04T04:03:33Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:adapter:NousResearch/Hermes-2-Pro-Mistral-7B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T03:29:41Z	--- library_name: peft license: apache-2.0 base_model: NousResearch/Hermes-2-Pro-Mistral-7B tags: - axolotl - generated_from_trainer model-index: - name: d4dfe41b-3da9-4703-8527-dc38cdab2f61 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 absolute_data_files: false adapter: lora base_model: NousResearch/Hermes-2-Pro-Mistral-7B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ee162119980ef3aa_train_data.json ds_type: json format: custom path: /workspace/input_data/ee162119980ef3aa_train_data.json type: field_input: Complex_CoT field_instruction: Question field_output: Response 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: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: cvoffer/d4dfe41b-3da9-4703-8527-dc38cdab2f61 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true 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: 10 mixed_precision: bf16 mlflow_experiment_name: /tmp/ee162119980ef3aa_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: 2048 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: 0fc83b9b-bde4-4e80-bc05-f9499d9e8685 wandb_project: s56-28 wandb_run: your_name wandb_runid: 0fc83b9b-bde4-4e80-bc05-f9499d9e8685 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # d4dfe41b-3da9-4703-8527-dc38cdab2f61 This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9280 ## 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-06 - train_batch_size: 10 - eval_batch_size: 10 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8952 \| 0.0844 \| 150 \| 0.9280 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
minions-stanford/minion-llama-3.1-8B-instruct	minions-stanford	2025-05-04T03:58:45Z	0	0	null	[ "safetensors", "llama", "facebook", "meta", "pytorch", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3.1", "region:us" ]	text-generation	2025-05-04T03:54:43Z	--- language: - en - de - fr - it - pt - hi - es - th license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\ \ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\ \ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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Fail to appropriately disclose to\ \ end users any known dangers of your AI system\nPlease report any violation of\ \ this Policy, software “bug,” or other problems that could lead to a violation\ \ of this Policy through one of the following means:\n * Reporting issues with\ \ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\ \ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\ \ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\ \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. Model developer: Meta Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Input modalities</strong> </td> <td><strong>Output modalities</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="3" >Llama 3.1 (text only) </td> <td rowspan="3" >A new mix of publicly available online data. </td> <td>8B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> <td rowspan="3" >15T+ </td> <td rowspan="3" >December 2023 </td> </tr> <tr> <td>70B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> </tr> <tr> <td>405B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> </tr> </table> Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Llama 3.1 family of models. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: July 23, 2024. Status: This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License: A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card. <span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner. ## How to use This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipeline( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Tool use with transformers LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/). Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. Here is a quick example showing a single simple tool: ```python # First, define a tool def get_current_temperature(location: str) -> float: """ Get the current temperature at a location. Args: location: The location to get the temperature for, in the format "City, Country" Returns: The current temperature at the specified location in the specified units, as a float. """ return 22. # A real function should probably actually get the temperature! # Next, create a chat and apply the chat template messages = [ {"role": "system", "content": "You are a bot that responds to weather queries."}, {"role": "user", "content": "Hey, what's the temperature in Paris right now?"} ] inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True) ``` You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so: ```python tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}} messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]}) ``` and then call the tool and append the result, with the `tool` role, like so: ```python messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"}) ``` After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information, see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling). ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/" --local-dir Meta-Llama-3.1-8B-Instruct ``` ## Hardware and Software Training Factors* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. Training utilized a cumulative of 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions Estimated total location-based greenhouse gas emissions were 11,390 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq. <table> <tr> <td> </td> <td><strong>Training Time (GPU hours)</strong> </td> <td><strong>Training Power Consumption (W)</strong> </td> <td><strong>Training Location-Based Greenhouse Gas Emissions</strong> <p> <strong>(tons CO2eq)</strong> </td> <td><strong>Training Market-Based Greenhouse Gas Emissions</strong> <p> <strong>(tons CO2eq)</strong> </td> </tr> <tr> <td>Llama 3.1 8B </td> <td>1.46M </td> <td>700 </td> <td>420 </td> <td>0 </td> </tr> <tr> <td>Llama 3.1 70B </td> <td>7.0M </td> <td>700 </td> <td>2,040 </td> <td>0 </td> </tr> <tr> <td>Llama 3.1 405B </td> <td>30.84M </td> <td>700 </td> <td>8,930 </td> <td>0 </td> </tr> <tr> <td>Total </td> <td>39.3M <td> <ul> </ul> </td> <td>11,390 </td> <td>0 </td> </tr> </table> The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples. Data Freshness: The pretraining data has a cutoff of December 2023. ## Benchmark scores In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong># Shots</strong> </td> <td><strong>Metric</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 3.1 8B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 3.1 70B</strong> </td> <td><strong>Llama 3.1 405B</strong> </td> </tr> <tr> <td rowspan="7" >General </td> <td>MMLU </td> <td>5 </td> <td>macro_avg/acc_char </td> <td>66.7 </td> <td>66.7 </td> <td>79.5 </td> <td>79.3 </td> <td>85.2 </td> </tr> <tr> <td>MMLU-Pro (CoT) </td> <td>5 </td> <td>macro_avg/acc_char </td> <td>36.2 </td> <td>37.1 </td> <td>55.0 </td> <td>53.8 </td> <td>61.6 </td> </tr> <tr> <td>AGIEval English </td> <td>3-5 </td> <td>average/acc_char </td> <td>47.1 </td> <td>47.8 </td> <td>63.0 </td> <td>64.6 </td> <td>71.6 </td> </tr> <tr> <td>CommonSenseQA </td> <td>7 </td> <td>acc_char </td> <td>72.6 </td> <td>75.0 </td> <td>83.8 </td> <td>84.1 </td> <td>85.8 </td> </tr> <tr> <td>Winogrande </td> <td>5 </td> <td>acc_char </td> <td>- </td> <td>60.5 </td> <td>- </td> <td>83.3 </td> <td>86.7 </td> </tr> <tr> <td>BIG-Bench Hard (CoT) </td> <td>3 </td> <td>average/em </td> <td>61.1 </td> <td>64.2 </td> <td>81.3 </td> <td>81.6 </td> <td>85.9 </td> </tr> <tr> <td>ARC-Challenge </td> <td>25 </td> <td>acc_char </td> <td>79.4 </td> <td>79.7 </td> <td>93.1 </td> <td>92.9 </td> <td>96.1 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki </td> <td>5 </td> <td>em </td> <td>78.5 </td> <td>77.6 </td> <td>89.7 </td> <td>89.8 </td> <td>91.8 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD </td> <td>1 </td> <td>em </td> <td>76.4 </td> <td>77.0 </td> <td>85.6 </td> <td>81.8 </td> <td>89.3 </td> </tr> <tr> <td>QuAC (F1) </td> <td>1 </td> <td>f1 </td> <td>44.4 </td> <td>44.9 </td> <td>51.1 </td> <td>51.1 </td> <td>53.6 </td> </tr> <tr> <td>BoolQ </td> <td>0 </td> <td>acc_char </td> <td>75.7 </td> <td>75.0 </td> <td>79.0 </td> <td>79.4 </td> <td>80.0 </td> </tr> <tr> <td>DROP (F1) </td> <td>3 </td> <td>f1 </td> <td>58.4 </td> <td>59.5 </td> <td>79.7 </td> <td>79.6 </td> <td>84.8 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong># Shots</strong> </td> <td><strong>Metric</strong> </td> <td><strong>Llama 3 8B Instruct</strong> </td> <td><strong>Llama 3.1 8B Instruct</strong> </td> <td><strong>Llama 3 70B Instruct</strong> </td> <td><strong>Llama 3.1 70B Instruct</strong> </td> <td><strong>Llama 3.1 405B Instruct</strong> </td> </tr> <tr> <td rowspan="4" >General </td> <td>MMLU </td> <td>5 </td> <td>macro_avg/acc </td> <td>68.5 </td> <td>69.4 </td> <td>82.0 </td> <td>83.6 </td> <td>87.3 </td> </tr> <tr> <td>MMLU (CoT) </td> <td>0 </td> <td>macro_avg/acc </td> <td>65.3 </td> <td>73.0 </td> <td>80.9 </td> <td>86.0 </td> <td>88.6 </td> </tr> <tr> <td>MMLU-Pro (CoT) </td> <td>5 </td> <td>micro_avg/acc_char </td> <td>45.5 </td> <td>48.3 </td> <td>63.4 </td> <td>66.4 </td> <td>73.3 </td> </tr> <tr> <td>IFEval </td> <td> </td> <td> </td> <td>76.8 </td> <td>80.4 </td> <td>82.9 </td> <td>87.5 </td> <td>88.6 </td> </tr> <tr> <td rowspan="2" >Reasoning </td> <td>ARC-C </td> <td>0 </td> <td>acc </td> <td>82.4 </td> <td>83.4 </td> <td>94.4 </td> <td>94.8 </td> <td>96.9 </td> </tr> <tr> <td>GPQA </td> <td>0 </td> <td>em </td> <td>34.6 </td> <td>30.4 </td> <td>39.5 </td> <td>46.7 </td> <td>50.7 </td> </tr> <tr> <td rowspan="4" >Code </td> <td>HumanEval </td> <td>0 </td> <td>pass@1 </td> <td>60.4 </td> <td>72.6 </td> <td>81.7 </td> <td>80.5 </td> <td>89.0 </td> </tr> <tr> <td>MBPP ++ base version </td> <td>0 </td> <td>pass@1 </td> <td>70.6 </td> <td>72.8 </td> <td>82.5 </td> <td>86.0 </td> <td>88.6 </td> </tr> <tr> <td>Multipl-E HumanEval </td> <td>0 </td> <td>pass@1 </td> <td>- </td> <td>50.8 </td> <td>- </td> <td>65.5 </td> <td>75.2 </td> </tr> <tr> <td>Multipl-E MBPP </td> <td>0 </td> <td>pass@1 </td> <td>- </td> <td>52.4 </td> <td>- </td> <td>62.0 </td> <td>65.7 </td> </tr> <tr> <td rowspan="2" >Math </td> <td>GSM-8K (CoT) </td> <td>8 </td> <td>em_maj1@1 </td> <td>80.6 </td> <td>84.5 </td> <td>93.0 </td> <td>95.1 </td> <td>96.8 </td> </tr> <tr> <td>MATH (CoT) </td> <td>0 </td> <td>final_em </td> <td>29.1 </td> <td>51.9 </td> <td>51.0 </td> <td>68.0 </td> <td>73.8 </td> </tr> <tr> <td rowspan="4" >Tool Use </td> <td>API-Bank </td> <td>0 </td> <td>acc </td> <td>48.3 </td> <td>82.6 </td> <td>85.1 </td> <td>90.0 </td> <td>92.0 </td> </tr> <tr> <td>BFCL </td> <td>0 </td> <td>acc </td> <td>60.3 </td> <td>76.1 </td> <td>83.0 </td> <td>84.8 </td> <td>88.5 </td> </tr> <tr> <td>Gorilla Benchmark API Bench </td> <td>0 </td> <td>acc </td> <td>1.7 </td> <td>8.2 </td> <td>14.7 </td> <td>29.7 </td> <td>35.3 </td> </tr> <tr> <td>Nexus (0-shot) </td> <td>0 </td> <td>macro_avg/acc </td> <td>18.1 </td> <td>38.5 </td> <td>47.8 </td> <td>56.7 </td> <td>58.7 </td> </tr> <tr> <td>Multilingual </td> <td>Multilingual MGSM (CoT) </td> <td>0 </td> <td>em </td> <td>- </td> <td>68.9 </td> <td>- </td> <td>86.9 </td> <td>91.6 </td> </tr> </table> #### Multilingual benchmarks <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Language</strong> </td> <td><strong>Llama 3.1 8B</strong> </td> <td><strong>Llama 3.1 70B</strong> </td> <td><strong>Llama 3.1 405B</strong> </td> </tr> <tr> <td rowspan="9" ><strong>General</strong> </td> <td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong> </td> <td>Portuguese </td> <td>62.12 </td> <td>80.13 </td> <td>84.95 </td> </tr> <tr> <td>Spanish </td> <td>62.45 </td> <td>80.05 </td> <td>85.08 </td> </tr> <tr> <td>Italian </td> <td>61.63 </td> <td>80.4 </td> <td>85.04 </td> </tr> <tr> <td>German </td> <td>60.59 </td> <td>79.27 </td> <td>84.36 </td> </tr> <tr> <td>French </td> <td>62.34 </td> <td>79.82 </td> <td>84.66 </td> </tr> <tr> <td>Hindi </td> <td>50.88 </td> <td>74.52 </td> <td>80.31 </td> </tr> <tr> <td>Thai </td> <td>50.32 </td> <td>72.95 </td> <td>78.21 </td> </tr> </table> ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: * Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. * Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm. * Provide protections for the community to help prevent the misuse of our models. ### Responsible deployment Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more. #### Llama 3.1 instruct Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper. Fine-tuning data We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.1 systems Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. #### New capabilities Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases. Tool-use: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards. Multilinguality: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide. ### Evaluations We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization. Red teaming For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical and other risks We specifically focused our efforts on mitigating the following critical risk areas: 1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. 2. Child Safety Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3. Cyber attack enablement Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
giyong/whisper-large-v3_ADReSSo	giyong	2025-05-04T03:57:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "audio-classification", "generated_from_trainer", "endpoints_compatible", "region:us" ]	audio-classification	2025-05-03T12:19:51Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: whisper-large-v3_ADReSSo 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-large-v3_ADReSSo This model was trained from scratch 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: 32 - eval_batch_size: 8 - 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 - num_epochs: 3.0 ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.0+cu118 - Datasets 2.14.6 - Tokenizers 0.21.1

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