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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
kingabzpro/whisper-small-hi-cv	kingabzpro	2025-04-28T12:22:04Z	1	1	transformers	[ "transformers", "pytorch", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_15_0", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-10-13T07:41:36Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_15_0 - mozilla-foundation/common_voice_13_0 language: - hi metrics: - cer - wer library_name: transformers pipeline_tag: automatic-speech-recognition model-index: - name: whisper-small-hi-cv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 15 type: mozilla-foundation/common_voice_15_0 args: hi metrics: - name: Test WER type: wer value: 13.9913 - name: Test CER type: cer value: 5.8844 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 args: hi metrics: - name: Test WER type: wer value: 23.3824 - name: Test CER type: cer value: 10.5288 --- # whisper-small-hi-cv This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 15 dataset. It achieves the following results on the evaluation set: - Wer: 13.9913 - Cer: 5.8844 View the results on Kaggle Notebook: https://www.kaggle.com/code/kingabzpro/whisper-hindi-eval ## Evaluation ```python from datasets import load_dataset,load_metric,Audio from transformers import WhisperForConditionalGeneration, WhisperProcessor import torch import torchaudio test_dataset = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="test") wer = load_metric("wer") cer = load_metric("cer") processor = WhisperProcessor.from_pretrained("kingabzpro/whisper-small-hi-cv") model = WhisperForConditionalGeneration.from_pretrained("kingabzpro/whisper-small-hi-cv").to("cuda") test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16000)) def map_to_pred(batch): audio = batch["audio"] input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features batch["reference"] = processor.tokenizer._normalize(batch['sentence']) with torch.no_grad(): predicted_ids = model.generate(input_features.to("cuda"))[0] transcription = processor.decode(predicted_ids) batch["prediction"] = processor.tokenizer._normalize(transcription) return batch result = test_dataset.map(map_to_pred) print("WER: {:2f}".format(100 * wer.compute(predictions=result["prediction"], references=result["reference"]))) print("CER: {:2f}".format(100 * cer.compute(predictions=result["prediction"], references=result["reference"]))) ``` ```bash WER: 23.3824 CER: 10.5288 ```
kingabzpro/wav2vec2-large-xls-r-1b-Swedish	kingabzpro	2025-04-28T12:21:59Z	844	1	transformers	[ "transformers", "pytorch", "safetensors", "wav2vec2", "automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard", "sv", "dataset:mozilla-foundation/common_voice_8_0", "base_model:facebook/wav2vec2-xls-r-1b", "base_model:finetune:facebook/wav2vec2-xls-r-1b", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - sv license: apache-2.0 tags: - automatic-speech-recognition - robust-speech-event - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_8_0 metrics: - wer - cer base_model: facebook/wav2vec2-xls-r-1b model-index: - name: wav2vec2-large-xls-r-1b-Swedish results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: name: Common Voice sv-SE type: mozilla-foundation/common_voice_8_0 args: sv-SE metrics: - type: wer value: 14.04 name: Test WER With LM - type: cer value: 4.86 name: Test CER With LM - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: sv metrics: - type: wer value: 29.69 name: Test WER - type: cer value: 12.59 name: Test CER --- <!-- 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. --> # wav2vec2-large-xls-r-1b-Swedish This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. It achieves the following results on the evaluation set: Without LM - Loss: 0.3370 - Wer: 18.44 - Cer: 5.75 With LM - Loss: 0.3370 - Wer: 14.04 - Cer: 4.86 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Swedish --dataset mozilla-foundation/common_voice_8_0 --config sv-SE --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Swedish --dataset speech-recognition-community-v2/dev_data --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ### Inference With LM ```python import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "kingabzpro/wav2vec2-large-xls-r-1b-Swedish" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sv-SE", split="test", streaming=True, use_auth_token=True)) sample = next(sample_iter) resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() model = AutoModelForCTC.from_pretrained(model_id) processor = AutoProcessor.from_pretrained(model_id) input_values = processor(resampled_audio, return_tensors="pt").input_values with torch.no_grad(): logits = model(input_values).logits transcription = processor.batch_decode(logits.numpy()).text ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 3.1562 \| 11.11 \| 500 \| 0.4830 \| 0.3729 \| 0.1169 \| \| 0.5655 \| 22.22 \| 1000 \| 0.3553 \| 0.2381 \| 0.0743 \| \| 0.3376 \| 33.33 \| 1500 \| 0.3359 \| 0.2179 \| 0.0696 \| \| 0.2419 \| 44.44 \| 2000 \| 0.3232 \| 0.1844 \| 0.0575 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
Kenazin/Llama-3.1-8B-peft-p-tuning-v4-9	Kenazin	2025-04-28T12:18:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T12:17:54Z	--- 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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Kenazin/Llama-3.1-8B-peft-p-tuning-v4-8	Kenazin	2025-04-28T12:16:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T12:16:07Z	--- 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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Kenazin/Llama-3.1-8B-peft-p-tuning-v4-5	Kenazin	2025-04-28T12:15:04Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T12:15:00Z	--- 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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Kristinaboo/1497959709	Kristinaboo	2025-04-28T12:13:54Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T11:47:16Z	--- 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: TOK --- # 1497959709 <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/Kristinaboo/1497959709/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('Kristinaboo/1497959709', weight_name='lora.safetensors') image = pipeline('TOK').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: 45 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Kristinaboo/1497959709/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/Stellar-Odyssey-12b-v0.0-GGUF	mradermacher	2025-04-28T12:13:14Z	103	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:LyraNovaHeart/Stellar-Odyssey-12b-v0.0", "base_model:quantized:LyraNovaHeart/Stellar-Odyssey-12b-v0.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-10-03T01:07:52Z	--- base_model: LyraNovaHeart/Stellar-Odyssey-12b-v0.0 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/LyraNovaHeart/Stellar-Odyssey-12b-v0.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-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/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.Q3_K_M.gguf) \| Q3_K_M \| 6.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.Q5_K_S.gguf) \| Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.Q5_K_M.gguf) \| Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.Q6_K.gguf) \| Q6_K \| 10.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Stellar-Odyssey-12b-v0.0-GGUF/resolve/main/Stellar-Odyssey-12b-v0.0.Q8_0.gguf) \| Q8_0 \| 13.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
qwbu/RoboDual-Specialist	qwbu	2025-04-28T12:12:51Z	0	0	null	[ "arxiv:2410.08001", "license:apache-2.0", "region:us" ]	null	2025-04-28T11:52:24Z	--- license: apache-2.0 --- # RoboDual This repo contains checkpoints for the specialist policy of our paper: \ Towards Synergistic, Generalized and Efficient Dual-System for Robotic Manipulation ## Evaluation with our checkpint > First set your ```CALVIN_ROOT``` environment variable wtih: ```bash export CALVIN_ROOT=/path/to/your/calvin_root_path ``` - Start evaluation on CALVIN (multi-GPU is also supported): ```bash torchrun --standalone --nnodes 1 --nproc-per-node 1 vla-scripts/evaluate_calvin.py \ --generalist_path "/path/to/calvin_generalist" \ --specialist_path "/path/to/calvin_specialist" \ --with_depth \ # use depth input --with_gripper \ # use gripper-view inputs (both RGB and depth) --with_cfg \ # enable classifier-free guidance --log_dir calvin ``` > Please refer to ```vla-scripts/evaluate_calvin.py``` in our [github repo](https://github.com/OpenDriveLab/RoboDual) for all evaluation options. ## Citation If you find our code or models useful in your work, please cite [our paper](https://arxiv.org/abs/2410.08001): ```bibtex @article{bu2024robodual, title={Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation}, author={Qingwen Bu and Hongyang Li and Li Chen and Jisong Cai and Jia Zeng and Heming Cui and Maoqing Yao and Yu Qiao}, journal={arXiv preprint arXiv:2410.08001}, year={2024} }
mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF	mradermacher	2025-04-28T12:12:33Z	1,044	30	transformers	[ "transformers", "gguf", "qwen", "uncensored", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:NobodyExistsOnTheInternet/ToxicQAFinal", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:Orion-zhen/dpo-toxic-zh", "dataset:unalignment/toxic-dpo-v0.2", "dataset:Crystalcareai/Intel-DPO-Pairs-Norefusals", "base_model:Orion-zhen/Qwen2.5-7B-Instruct-Uncensored", "base_model:quantized:Orion-zhen/Qwen2.5-7B-Instruct-Uncensored", "license:gpl-3.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-10-11T18:14:11Z	--- base_model: Orion-zhen/Qwen2.5-7B-Instruct-Uncensored datasets: - NobodyExistsOnTheInternet/ToxicQAFinal - anthracite-org/kalo-opus-instruct-22k-no-refusal - Orion-zhen/dpo-toxic-zh - unalignment/toxic-dpo-v0.2 - Crystalcareai/Intel-DPO-Pairs-Norefusals language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: gpl-3.0 quantized_by: mradermacher tags: - qwen - uncensored --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Orion-zhen/Qwen2.5-7B-Instruct-Uncensored <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-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/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Mistermango24/My-lora-model-from-civitai	Mistermango24	2025-04-28T12:12:28Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-04-28T12:09:29Z	--- license: artistic-2.0 ---
kande1/Financial_news_topic	kande1	2025-04-28T12:11:49Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-28T12:03:48Z	--- 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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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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ikkiren/research_TS_corr	ikkiren	2025-04-28T12:11:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T08:42:18Z	--- 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. 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Triangle104/Athena-3.5-3B-Q8_0-GGUF	Triangle104	2025-04-28T12:09:57Z	0	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "sft", "llama-cpp", "gguf-my-repo", "base_model:Spestly/Athena-3.5-3B", "base_model:quantized:Spestly/Athena-3.5-3B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T12:09:34Z	--- base_model: Spestly/Athena-3.5-3B library_name: transformers tags: - unsloth - trl - sft - llama-cpp - gguf-my-repo --- # Triangle104/Athena-3.5-3B-Q8_0-GGUF This model was converted to GGUF format from [`Spestly/Athena-3.5-3B`](https://huggingface.co/Spestly/Athena-3.5-3B) 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/Spestly/Athena-3.5-3B) 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 Triangle104/Athena-3.5-3B-Q8_0-GGUF --hf-file athena-3.5-3b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Athena-3.5-3B-Q8_0-GGUF --hf-file athena-3.5-3b-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 Triangle104/Athena-3.5-3B-Q8_0-GGUF --hf-file athena-3.5-3b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Athena-3.5-3B-Q8_0-GGUF --hf-file athena-3.5-3b-q8_0.gguf -c 2048 ```
colemei/SmolLM2-FT-MyDataset	colemei	2025-04-28T12:06:53Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "smol-course", "module_1", "trl", "sft", "conversational", "base_model:HuggingFaceTB/SmolLM2-135M", "base_model:finetune:HuggingFaceTB/SmolLM2-135M", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T12:06:22Z	--- base_model: HuggingFaceTB/SmolLM2-135M library_name: transformers model_name: SmolLM2-FT-MyDataset tags: - generated_from_trainer - smol-course - module_1 - trl - sft licence: license --- # Model Card for SmolLM2-FT-MyDataset This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M). 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="colemei/SmolLM2-FT-MyDataset", 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/qmmei-the-university-of-melbourne/huggingface/runs/j4sxn34l) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
nyuuzyou/Qwen2.5-0.5B-Bluesky-Instruct	nyuuzyou	2025-04-28T12:05:37Z	22	3	transformers	[ "transformers", "gguf", "bluesky", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:alpindale/two-million-bluesky-posts", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-0.5B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-11-29T10:05:55Z	--- base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - bluesky datasets: - alpindale/two-million-bluesky-posts license: other pipeline_tag: text-generation library_name: transformers language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Qwen2.5-0.5B-Bluesky-Instruct This model is a fine-tuned version of the [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) language model on the [alpindale/two-million-bluesky-posts](https://huggingface.co/datasets/alpindale/two-million-bluesky-posts) dataset. License: Dataset usage is subject to Bluesky's Terms of Service.
LuckyLukke/grpo_onesided_1_starter_change-400	LuckyLukke	2025-04-28T12:03:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T12:00:46Z	--- 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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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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Hyphonical/Pixtral-12B-Captioner-Relaxed-Q4_K_M-GGUF	Hyphonical	2025-04-28T12:03:34Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "image-to-text", "en", "base_model:unalignment/Pixtral-12B-Captioner-Relaxed", "base_model:quantized:unalignment/Pixtral-12B-Captioner-Relaxed", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	image-to-text	2025-04-28T11:55:59Z	--- base_model: unalignment/Pixtral-12B-Captioner-Relaxed language: - en library_name: transformers license: apache-2.0 pipeline_tag: image-to-text tags: - llama-cpp - gguf-my-repo --- # Hyphonical/Pixtral-12B-Captioner-Relaxed-Q4_K_M-GGUF This model was converted to GGUF format from [`unalignment/Pixtral-12B-Captioner-Relaxed`](https://huggingface.co/unalignment/Pixtral-12B-Captioner-Relaxed) 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/unalignment/Pixtral-12B-Captioner-Relaxed) 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 Hyphonical/Pixtral-12B-Captioner-Relaxed-Q4_K_M-GGUF --hf-file pixtral-12b-captioner-relaxed-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Hyphonical/Pixtral-12B-Captioner-Relaxed-Q4_K_M-GGUF --hf-file pixtral-12b-captioner-relaxed-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 Hyphonical/Pixtral-12B-Captioner-Relaxed-Q4_K_M-GGUF --hf-file pixtral-12b-captioner-relaxed-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Hyphonical/Pixtral-12B-Captioner-Relaxed-Q4_K_M-GGUF --hf-file pixtral-12b-captioner-relaxed-q4_k_m.gguf -c 2048 ```
mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF	mradermacher	2025-04-28T12:03:18Z	72	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130", "base_model:quantized:LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-12-01T13:15:29Z	--- base_model: LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-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/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
ASethi04/meta-llama-Llama-3.1-8B-legalbench-second-full-parameter-4-1e-05	ASethi04	2025-04-28T12:02:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T11:07:19Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-legalbench-second-full-parameter-4-1e-05 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-legalbench-second-full-parameter-4-1e-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-legalbench-second-full-parameter-4-1e-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/k0ahpjdn) 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}} } ```
nodoo/213321	nodoo	2025-04-28T12:02:08Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T12:02:08Z	--- license: apache-2.0 ---
Nitish035/qwen_instruct_exp_1800	Nitish035	2025-04-28T12:00:09Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:59:54Z	--- 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. (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]
ReopenAI/Qwen2.5-14B-SeamlessV2	ReopenAI	2025-04-28T11:59:36Z	11	2	null	[ "safetensors", "qwen2_mm", "audio-text-to-text", "custom_code", "zh", "dataset:EastBrook/COIG-Kun-Aug-Audio", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "license:mit", "region:us" ]	audio-text-to-text	2025-04-10T06:51:28Z	--- license: mit language: - zh base_model: - Qwen/Qwen2.5-14B-Instruct pipeline_tag: audio-text-to-text datasets: - EastBrook/COIG-Kun-Aug-Audio --- ## Model Details 本模型是语音-文本->文本的多模态模型。基于seamless-m4t-v2-large的音频编码器和Qwen2.5-14B-Instruct文本模型。训练：一阶段使用清洗后的WeNet中文数据（约6000小时）进行ASR任务训练，此阶段除文本模型外全部可训练。二阶段基于chatgpt-corpus、moss-003-sft-data等数据集的问题，使用Qwen2.5-72B-Instruct-GPTQ-Int4首先继续生成更多轮次的问题，然后使用Qwen2.5-72B-Instruct-GPTQ-Int4生成多轮问题的答案，问题使用cosyvoice生成对应音频。生成约620k的多轮语音输入->文本回答数据集。进行语音输入->文本回答的问答任务训练。此阶段除文本模型外全部可训练。部分数据: https://huggingface.co/datasets/ReopenAI/COIG-Kun-Aug-Audio 优势：训练期间文本模型完全冻结，保留原始能力；seamless-m4t-v2-large编码器平均每秒的音频编码成6-7个token，显著小于whisper的50个token。 ### 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] ## Uses ```python import requests import torch from torchvision import io from typing import Dict from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor, AutoModel, SeamlessM4Tv2Model, AutoModelForCausalLM, AutoConfig model_path = "EastBrook/Qwen2.5-14B-SeamlessV2" #model_path = "./" model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda") processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) print("model_path: ", model_path) messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, { "role": "user", "content": [ {"type": "audio"}, #{"type": "text", "text": "请详细介绍一下强化学习中的GRPO。"}, ], }, ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) import librosa audios = [] audio_paths = [ "/mnt/diskhd/Backup/Dataset/WenetSpeech/audio/train/podcast/B00022/X0000005821_5113963_S01270.mp3", ] for path in audio_paths: audio, sr = librosa.load(path, sr=16000) audios.append(audio) inputs = processor( text=[text], images=None, videos=None, #audios=None, audios=audios, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") generated_ids = model.generate(**inputs, max_new_tokens=512, do_sample=False) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print("output_text: ", output_text) ``` ### Out-of-Scope Use 模型主要使用中文音频训练，英文能力较弱。
dzanbek/ea5a3b64-e495-4fc7-80c6-2b9e9c35310e	dzanbek	2025-04-28T11:59:29Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tiiuae/Falcon3-1B-Base", "base_model:adapter:tiiuae/Falcon3-1B-Base", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T11:55:32Z	--- library_name: peft license: other base_model: tiiuae/Falcon3-1B-Base tags: - axolotl - generated_from_trainer model-index: - name: ea5a3b64-e495-4fc7-80c6-2b9e9c35310e 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: tiiuae/Falcon3-1B-Base bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 6cc0fe21f0332fa7_train_data.json ds_type: json format: custom path: /workspace/input_data/6cc0fe21f0332fa7_train_data.json type: field_instruction: question field_output: answer 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: dzanbek/ea5a3b64-e495-4fc7-80c6-2b9e9c35310e hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/6cc0fe21f0332fa7_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: 72923dc5-5ef8-423a-919a-0a486181f7ff wandb_project: s56-2 wandb_run: your_name wandb_runid: 72923dc5-5ef8-423a-919a-0a486181f7ff warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # ea5a3b64-e495-4fc7-80c6-2b9e9c35310e This model is a fine-tuned version of [tiiuae/Falcon3-1B-Base](https://huggingface.co/tiiuae/Falcon3-1B-Base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6014 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5658 \| 0.2128 \| 200 \| 0.6014 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
vmpsergio/bb05a3bb-7420-445b-936d-86fc321f986b	vmpsergio	2025-04-28T11:59:21Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tiiuae/Falcon3-1B-Base", "base_model:adapter:tiiuae/Falcon3-1B-Base", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T11:55:29Z	--- library_name: peft license: other base_model: tiiuae/Falcon3-1B-Base tags: - axolotl - generated_from_trainer model-index: - name: bb05a3bb-7420-445b-936d-86fc321f986b 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: tiiuae/Falcon3-1B-Base bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 6cc0fe21f0332fa7_train_data.json ds_type: json format: custom path: /workspace/input_data/6cc0fe21f0332fa7_train_data.json type: field_instruction: question field_output: answer 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/bb05a3bb-7420-445b-936d-86fc321f986b hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/6cc0fe21f0332fa7_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: 72923dc5-5ef8-423a-919a-0a486181f7ff wandb_project: s56-2 wandb_run: your_name wandb_runid: 72923dc5-5ef8-423a-919a-0a486181f7ff warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # bb05a3bb-7420-445b-936d-86fc321f986b This model is a fine-tuned version of [tiiuae/Falcon3-1B-Base](https://huggingface.co/tiiuae/Falcon3-1B-Base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5993 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5656 \| 0.2128 \| 200 \| 0.5993 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lakleyrainpop/ccd5fcf2-8de4-4cd0-811c-a241c57c74d8	lakleyrainpop	2025-04-28T11:59:01Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tiiuae/Falcon3-1B-Base", "base_model:adapter:tiiuae/Falcon3-1B-Base", "license:other", "region:us" ]	null	2025-04-28T11:57:59Z	--- library_name: peft license: other base_model: tiiuae/Falcon3-1B-Base tags: - axolotl - generated_from_trainer model-index: - name: ccd5fcf2-8de4-4cd0-811c-a241c57c74d8 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: tiiuae/Falcon3-1B-Base bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6cc0fe21f0332fa7_train_data.json ds_type: json format: custom path: /workspace/input_data/6cc0fe21f0332fa7_train_data.json type: field_instruction: question field_output: answer 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: lakleyrainpop/ccd5fcf2-8de4-4cd0-811c-a241c57c74d8 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/6cc0fe21f0332fa7_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: 4 sequence_len: 512 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: 72923dc5-5ef8-423a-919a-0a486181f7ff wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 72923dc5-5ef8-423a-919a-0a486181f7ff warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # ccd5fcf2-8de4-4cd0-811c-a241c57c74d8 This model is a fine-tuned version of [tiiuae/Falcon3-1B-Base](https://huggingface.co/tiiuae/Falcon3-1B-Base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6939 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - 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: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6919 \| 0.0011 \| 1 \| 0.7299 \| \| 0.7035 \| 0.0032 \| 3 \| 0.7297 \| \| 0.7858 \| 0.0064 \| 6 \| 0.7248 \| \| 0.733 \| 0.0096 \| 9 \| 0.6939 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Qwenvergence-14B-v3-Prose-GGUF	mradermacher	2025-04-28T11:58:08Z	78	3	transformers	[ "transformers", "gguf", "mergekit", "merge", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:sometimesanotion/Qwenvergence-14B-v3-Prose", "base_model:quantized:sometimesanotion/Qwenvergence-14B-v3-Prose", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-21T10:49:51Z	--- base_model: sometimesanotion/Qwenvergence-14B-v3-Prose language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/sometimesanotion/Qwenvergence-14B-v3-Prose <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-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/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
data-skills/lora_model	data-skills	2025-04-28T11:57:32Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:57:21Z	--- base_model: unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: data-skills - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-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)
safouene99999/Mistral_7b_Cyber_Instruct	safouene99999	2025-04-28T11:54:46Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:54:22Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: safouene99999 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral 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)
samoline/8a6b1058-a6d5-48f1-bd83-4e502157624f	samoline	2025-04-28T11:53:50Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:unsloth/Llama-3.2-3B-Instruct", "base_model:finetune:unsloth/Llama-3.2-3B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T11:53:15Z	--- base_model: unsloth/Llama-3.2-3B-Instruct library_name: transformers model_name: 8a6b1058-a6d5-48f1-bd83-4e502157624f tags: - generated_from_trainer - axolotl - trl - grpo licence: license --- # Model Card for 8a6b1058-a6d5-48f1-bd83-4e502157624f This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-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="samoline/8a6b1058-a6d5-48f1-bd83-4e502157624f", 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/samoline-nan/Gradients-On-Demand/runs/fydk7gkq) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.3 - Pytorch: 2.5.1+cu124 - 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}} } ```
dhi-roma/la_g17_all_tags	dhi-roma	2025-04-28T11:51:13Z	10	0	spacy	[ "spacy", "token-classification", "history", "la", "doi:10.57967/hf/5235", "license:mit", "region:us" ]	token-classification	2025-04-24T15:13:56Z	--- tags: - spacy - token-classification - history language: - la license: mit --- ## la_g17_all_tags la_g17_all_tags is a SpaCy NER model trained to recognize entities used in the [Graceful17](http://dhi-roma.it/index.php?id=digital_humanities_singleview0&L=24&tx_ttnews%5Byear%5D=1970&tx_ttnews%5Bmonth%5D=01&tx_ttnews%5Bday%5D=01&tx_ttnews%5Btt_news%5D=4284&cHash=9ff9e82502eb7ba56008822f844e5008) project for modeling papal provisions for benefices. ## Training The model was trained using archival sources from the Roman Dataria Apostolica. The research data on which the model is based can be found [here](https://doi.org/10.5281/zenodo.14945481) (coming soon). The labels represent relevant information, such as references to institutions (e.g., dioceses), specific locations, individual persons in various roles, categorical classifications, dates, and more. A more detailed description including methodological background can be found [here] (coming soon). \| Feature \| Description \| \| --- \| --- \| \| Name \| `la_g17_all_tags` \| \| Version \| `0.0.1` \| \| spaCy \| `>=3.7.2,<3.8.0` \| \| Default Pipeline \| `ner` \| \| Components \| `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [Christoph Sander](dataria.dhi-roma.it/organize?folder=models) \| ## Label Scheme <details> <summary>View label scheme (28 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `APUD_SEDEM_VAC`, `BIO_INST`, `BIO_OFFICE`, `CAT_BEN`, `CAT_C`, `CAT_VAC`, `CA_BEN`, `DATE_TRANSCRIPTION_EVENT`, `DATE_TRANSCRIPTION_VACANCY`, `DIOC_BEN`, `FORM_AP`, `NAME_BEN`, `NAME_C`, `NAME_FORMER_POSSESSOR`, `NAME_PATRON_INFORMAL`, `NAME_PATRON_LEGAL`, `NAME_POSSESSOR`, `NAME_PROVIDEE`, `PATRONAGE_BEN`, `PLACE_C`, `PLACE_EVENT`, `PLACE_NAME_C`, `REGION_BEN`, `REGORD_C`, `SOURCE_SUBREGISTER`, `TAX_BEN`, `TYPE_EVENT`, `VAL_PEN` \| </details> ## License MIT License © 2025 Christoph Sander
FKCesrDCzzlzF/kshadpcnd	FKCesrDCzzlzF	2025-04-28T11:47:49Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T11:47:45Z	--- license: apache-2.0 ---
RFwvhVw6C4/shkekdc	RFwvhVw6C4	2025-04-28T11:47:15Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T11:47:15Z	--- license: apache-2.0 ---
Dazelin/SRAYBAN	Dazelin	2025-04-28T11:46:10Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T11:31:35Z	--- 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: SRAYBAN --- # Srayban <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 `SRAYBAN` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SRAYBAN", "lora_weights": "https://huggingface.co/Dazelin/SRAYBAN/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('Dazelin/SRAYBAN', weight_name='lora.safetensors') image = pipeline('SRAYBAN').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/Dazelin/SRAYBAN/discussions) to add images that show off what you’ve made with this LoRA.
dimpac1/Meta-Llama-3.1-8B	dimpac1	2025-04-28T11:44:03Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:43:41Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: dimpac1 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-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)
RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf	RichardErkhov	2025-04-28T11:42:25Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T10:14:36Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2 - GGUF - Model creator: https://huggingface.co/mlfoundations-dev/ - Original model: https://huggingface.co/mlfoundations-dev/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q2_K.gguf) \| Q2_K \| 2.81GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.IQ3_XS.gguf) \| IQ3_XS \| 3.12GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.IQ3_S.gguf) \| IQ3_S \| 3.26GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q3_K_S.gguf) \| Q3_K_S \| 3.25GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.IQ3_M.gguf) \| IQ3_M \| 3.33GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q3_K.gguf) \| Q3_K \| 3.55GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q3_K_M.gguf) \| Q3_K_M \| 3.55GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q3_K_L.gguf) \| Q3_K_L \| 3.81GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.IQ4_XS.gguf) \| IQ4_XS \| 3.96GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q4_0.gguf) \| Q4_0 \| 4.13GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.IQ4_NL.gguf) \| IQ4_NL \| 4.16GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q4_K_S.gguf) \| Q4_K_S \| 4.15GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q4_K.gguf) \| Q4_K \| 4.36GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q4_K_M.gguf) \| Q4_K_M \| 4.36GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q4_1.gguf) \| Q4_1 \| 4.54GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q5_0.gguf) \| Q5_0 \| 4.95GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q5_K_S.gguf) \| Q5_K_S \| 4.95GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q5_K.gguf) \| Q5_K \| 5.07GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q5_K_M.gguf) \| Q5_K_M \| 5.07GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q5_1.gguf) \| Q5_1 \| 5.36GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q6_K.gguf) \| Q6_K \| 5.82GB \| \| [hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2-gguf/blob/main/hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2.Q8_0.gguf) \| Q8_0 \| 7.54GB \| Original model description: --- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B tags: - llama-factory - full - generated_from_trainer model-index: - name: hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2 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. --> # hp_ablations_qwen_scheduler_linear_warmup0.10_dcftv1.2 This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the mlfoundations-dev/oh-dcft-v1.2_no-curation_gpt-4o-mini dataset. It achieves the following results on the evaluation set: - Loss: 0.6354 ## 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 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - total_eval_batch_size: 64 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6388 \| 0.9978 \| 341 \| 0.6434 \| \| 0.6165 \| 1.9967 \| 682 \| 0.6359 \| \| 0.5937 \| 2.9956 \| 1023 \| 0.6354 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.0.2 - Tokenizers 0.20.3
mbXgU1aIGkYD/mbXgU1aIGkYD	mbXgU1aIGkYD	2025-04-28T11:41:57Z	0	0	null	[ "license:bsd-2-clause", "region:us" ]	null	2025-04-28T11:41:40Z	--- license: bsd-2-clause ---
Ahlam010/tokenizer-MARBERT	Ahlam010	2025-04-28T11:40:09Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-27T11:55:52Z	--- 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]
chungbv321/chungbui	chungbv321	2025-04-28T11:36:28Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T10:40:39Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym widget: - output: url: sample/chungbui_000800_00_20250428113536.png text: chungbui base_model: black-forest-labs/FLUX.1-dev instance_prompt: chungbui 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 --- # chungbui A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `chungbui` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
BroBiao/task-17-Qwen-Qwen2.5-3B-Instruct	BroBiao	2025-04-28T11:36:17Z	0	0	peft	[ "peft", "safetensors", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:adapter:Qwen/Qwen2.5-3B-Instruct", "region:us" ]	null	2024-12-05T16:25:42Z	--- base_model: Qwen/Qwen2.5-3B-Instruct library_name: peft language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # 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 0.13.2
Aluba/Comeback_v1_5	Aluba	2025-04-28T11:35:51Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-28T11:22:02Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
vermoney/8c8bf70b-70f9-40f7-a870-e2547aaa40e8	vermoney	2025-04-28T11:34:57Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:sophosympatheia/Midnight-Rose-70B-v1.0", "base_model:adapter:sophosympatheia/Midnight-Rose-70B-v1.0", "license:llama2", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T10:39:44Z	--- library_name: peft license: llama2 base_model: sophosympatheia/Midnight-Rose-70B-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: 8c8bf70b-70f9-40f7-a870-e2547aaa40e8 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: sophosympatheia/Midnight-Rose-70B-v1.0 bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d0f248cfdbcb3f38_train_data.json ds_type: json format: custom path: /workspace/input_data/d0f248cfdbcb3f38_train_data.json type: field_instruction: question field_output: answer 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: vermoney/8c8bf70b-70f9-40f7-a870-e2547aaa40e8 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/d0f248cfdbcb3f38_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: f427a96c-939e-4d37-adde-1c9c742e47ac wandb_project: s56-9 wandb_run: your_name wandb_runid: f427a96c-939e-4d37-adde-1c9c742e47ac warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 8c8bf70b-70f9-40f7-a870-e2547aaa40e8 This model is a fine-tuned version of [sophosympatheia/Midnight-Rose-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Rose-70B-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7240 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6738 \| 0.0159 \| 200 \| 0.7240 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
fedovtt/15e1057d-0a32-4d30-a821-9270229e1027	fedovtt	2025-04-28T11:31:47Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-0.5B-Chat", "base_model:adapter:Qwen/Qwen1.5-0.5B-Chat", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T11:23:11Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-0.5B-Chat tags: - axolotl - generated_from_trainer model-index: - name: 15e1057d-0a32-4d30-a821-9270229e1027 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/Qwen1.5-0.5B-Chat bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 758c031c5f00183c_train_data.json ds_type: json format: custom path: /workspace/input_data/758c031c5f00183c_train_data.json type: field_instruction: prompt field_output: gold_standard_solution 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: fedovtt/15e1057d-0a32-4d30-a821-9270229e1027 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/758c031c5f00183c_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: a290c1ad-bb08-4ba6-a45b-4d51c5beaab4 wandb_project: s56-1 wandb_run: your_name wandb_runid: a290c1ad-bb08-4ba6-a45b-4d51c5beaab4 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 15e1057d-0a32-4d30-a821-9270229e1027 This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2525 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.997 \| 0.0282 \| 200 \| 3.2525 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
maksf8486/e4af15ca-ab2d-4469-84c4-87c974dc4c39	maksf8486	2025-04-28T11:31:41Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-0.5B-Chat", "base_model:adapter:Qwen/Qwen1.5-0.5B-Chat", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T11:22:54Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-0.5B-Chat tags: - axolotl - generated_from_trainer model-index: - name: e4af15ca-ab2d-4469-84c4-87c974dc4c39 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/Qwen1.5-0.5B-Chat bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 758c031c5f00183c_train_data.json ds_type: json format: custom path: /workspace/input_data/758c031c5f00183c_train_data.json type: field_instruction: prompt field_output: gold_standard_solution format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: false reference_model: 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: maksf8486/e4af15ca-ab2d-4469-84c4-87c974dc4c39 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/758c031c5f00183c_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: a290c1ad-bb08-4ba6-a45b-4d51c5beaab4 wandb_project: s56-2 wandb_run: your_name wandb_runid: a290c1ad-bb08-4ba6-a45b-4d51c5beaab4 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # e4af15ca-ab2d-4469-84c4-87c974dc4c39 This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2519 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.0123 \| 0.0282 \| 200 \| 3.2519 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
marialvsantiago/f1cbb311-f67d-4739-8b14-58611436d303	marialvsantiago	2025-04-28T11:31:09Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:sophosympatheia/Midnight-Rose-70B-v1.0", "base_model:adapter:sophosympatheia/Midnight-Rose-70B-v1.0", "license:llama2", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T10:38:57Z	--- library_name: peft license: llama2 base_model: sophosympatheia/Midnight-Rose-70B-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: f1cbb311-f67d-4739-8b14-58611436d303 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: sophosympatheia/Midnight-Rose-70B-v1.0 bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d0f248cfdbcb3f38_train_data.json ds_type: json format: custom path: /workspace/input_data/d0f248cfdbcb3f38_train_data.json type: field_instruction: question field_output: answer 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: marialvsantiago/f1cbb311-f67d-4739-8b14-58611436d303 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/d0f248cfdbcb3f38_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: f427a96c-939e-4d37-adde-1c9c742e47ac wandb_project: s56-33 wandb_run: your_name wandb_runid: f427a96c-939e-4d37-adde-1c9c742e47ac warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # f1cbb311-f67d-4739-8b14-58611436d303 This model is a fine-tuned version of [sophosympatheia/Midnight-Rose-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Rose-70B-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7245 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6729 \| 0.0159 \| 200 \| 0.7245 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ChandrilBasu/Temp	ChandrilBasu	2025-04-28T11:30:43Z	0	0	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T11:30:36Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: Temp 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 --- # Temp <Gallery /> ## Model description ## Trigger words You should use `Temp` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/ChandrilBasu/Temp/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
kokovova/4ff2ede5-3b5d-4af5-9e3f-522fcb1d753a	kokovova	2025-04-28T11:26:46Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-0.5B-Chat", "base_model:adapter:Qwen/Qwen1.5-0.5B-Chat", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T11:23:33Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-0.5B-Chat tags: - axolotl - generated_from_trainer model-index: - name: 4ff2ede5-3b5d-4af5-9e3f-522fcb1d753a 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/Qwen1.5-0.5B-Chat bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 758c031c5f00183c_train_data.json ds_type: json format: custom path: /workspace/input_data/758c031c5f00183c_train_data.json type: field_instruction: prompt field_output: gold_standard_solution 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: kokovova/4ff2ede5-3b5d-4af5-9e3f-522fcb1d753a 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/758c031c5f00183c_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: a290c1ad-bb08-4ba6-a45b-4d51c5beaab4 wandb_project: s56-4 wandb_run: your_name wandb_runid: a290c1ad-bb08-4ba6-a45b-4d51c5beaab4 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 4ff2ede5-3b5d-4af5-9e3f-522fcb1d753a This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3083 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.032 \| 0.0282 \| 200 \| 3.3083 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Varinder2110/himeshaneesha	Varinder2110	2025-04-28T11:25:50Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-27T09:57:43Z	--- 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: TOK --- # Himeshaneesha <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/Varinder2110/himeshaneesha/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('Varinder2110/himeshaneesha', weight_name='lora.safetensors') image = pipeline('TOK').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: 6000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Varinder2110/himeshaneesha/discussions) to add images that show off what you’ve made with this LoRA.
himanshudhingra/4bit-forced	himanshudhingra	2025-04-28T11:24:21Z	0	0	null	[ "safetensors", "llama", "unsloth", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T11:21:49Z	--- license: apache-2.0 tags: - unsloth ---
Kebaya-Mera-Viral-Video-Minit-Twitter/Full.lyna.team.nasdas.telegram.video.de.lyna.team.nasdas	Kebaya-Mera-Viral-Video-Minit-Twitter	2025-04-28T11:23:53Z	0	0	null	[ "region:us" ]	null	2025-04-28T11:22:11Z	Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/Full-lyna-team-nasdas-telegram-video-de-lyna-team-nasdas"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/Full-lyna-team-nasdas-telegram-video-de-lyna-team-nasdas"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Full.lyna.team.nasdas.telegram.video.de.lyna.team.nasdas Full.lyna.team.nasdas.telegram.video.de.lyna.team.nasdas Full.lyna.team.nasdas.telegram.video.de.lyna.team.nasdas ![68747470733a2f2f692e696d6775722e636f6d2f644a486b345a712e676966.gif](https://cdn-uploads.huggingface.co/production/uploads/680f5da2b2fef41b5479f862/88K65Q-cFO8oMyRaDg3K8.gif)
akseljoonas/Agentic-Qwen-3B-e14-lr3-b8	akseljoonas	2025-04-28T11:23:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:smolagents/training-traces", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T11:11:55Z	--- base_model: Qwen/Qwen2.5-3B-Instruct datasets: smolagents/training-traces library_name: transformers model_name: Agentic-Qwen-3B-e14-lr3-b8 tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Agentic-Qwen-3B-e14-lr3-b8 This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [smolagents/training-traces](https://huggingface.co/datasets/smolagents/training-traces) 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="akseljoonas/Agentic-Qwen-3B-e14-lr3-b8", 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/akseljoonas-university-of-groningen/huggingface/runs/h4c972bk) This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.0 - 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}} } ```
JohnConnor123/Llama-3.2-3B-Instruct-BNB-8bit	JohnConnor123	2025-04-28T11:23:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-3B-Instruct", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-28T11:06:05Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\ \ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\ \ for use, reproduction, distribution and modification of the Llama Materials set\ \ forth herein.\n\n“Documentation” means the specifications, manuals and documentation\ \ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \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\n“Llama 3.2”\ \ 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://www.llama.com/llama-downloads.\n\n“Llama Materials” means,\ \ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\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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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 base_model: meta-llama/Llama-3.2-3B-Instruct --- > ## This quantization was done using the [quantization-benchmark](https://github.com/JohnConnor123/quantization-benchmark) framework ## Bitsandbytes quantization config >{'load_in_8bit': True} ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 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. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. 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 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-3B-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 torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-3B-Instruct" pipe = pipeline( "text-generation", model=model_id, 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 = pipe( 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) ### 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/Llama-3.2-3B-Instruct --include "original/" --local-dir Llama-3.2-3B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k 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 240 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. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. 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 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: 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 generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: 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. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). 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 3.2 Systems Safety as a System: Large language models, including Llama 3.2, 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, 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 and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards 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. Red Teaming: 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 Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, 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 and have determined that such testing also applies to the smaller 1B and 3B models. 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 Attacks: For Llama 3.1 405B, 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. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: 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). Grants: 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). Reporting: 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 Values: The core values of Llama 3.2 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 3.2 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. Testing: Llama 3.2 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 3.2’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 3.2 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.
Timexxx/Timerrr	Timexxx	2025-04-28T11:22:33Z	0	0	allennlp	[ "allennlp", "biology", "token-classification", "ab", "dataset:nvidia/OpenCodeReasoning", "base_model:nari-labs/Dia-1.6B", "base_model:finetune:nari-labs/Dia-1.6B", "license:apache-2.0", "region:us" ]	token-classification	2025-04-28T11:19:38Z	--- license: apache-2.0 datasets: - nvidia/OpenCodeReasoning language: - ab metrics: - bertscore base_model: - nari-labs/Dia-1.6B new_version: deepseek-ai/DeepSeek-V3-0324 pipeline_tag: token-classification library_name: allennlp tags: - biology ---
HALLUCINATIONS-OF-NECROMANCY/PAIMON	HALLUCINATIONS-OF-NECROMANCY	2025-04-28T11:22:32Z	0	0	null	[ "region:us" ]	null	2025-04-26T19:31:42Z	PERSINA: PAYMON EGYPTIAN: PAI-AMUN
Gopal1853/agam_1.0	Gopal1853	2025-04-28T11:21:54Z	0	1	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-28T11:21:11Z	--- 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. 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HALLUCINATIONS-OF-NECROMANCY/VASSAGO	HALLUCINATIONS-OF-NECROMANCY	2025-04-28T11:21:40Z	0	0	null	[ "region:us" ]	null	2025-04-28T10:18:23Z	WA-SU GU-ZU WASU-AKER WEPWAWET YAHOO EGYPTIAN: YAHU
ainse/fluxdevtest	ainse	2025-04-28T11:20:48Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T11:20:45Z	--- 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: gigachad --- # Fluxdevtest <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 `gigachad` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "gigachad", "lora_weights": "https://huggingface.co/ainse/fluxdevtest/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('ainse/fluxdevtest', weight_name='lora.safetensors') image = pipeline('gigachad').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/ainse/fluxdevtest/discussions) to add images that show off what you’ve made with this LoRA.
Kebaya-Mera-Viral-Video-Minit-Twitter/landon.jackson.viral.video.FULL.HD.Original.Trending.VIDEO	Kebaya-Mera-Viral-Video-Minit-Twitter	2025-04-28T11:19:44Z	0	0	null	[ "region:us" ]	null	2025-04-28T11:10:52Z	Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/landon-jackson-viral-video-FULL-HD-Original-Trending-VIDEO"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/landon-jackson-viral-video-FULL-HD-Original-Trending-VIDEO"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ landon.jackson.viral.video.FULL.HD.Original.Trending.VIDEO landon.jackson.viral.video.FULL.HD.Original.Trending.VIDEO landon.jackson.viral.video.FULL.HD.Original.Trending.VIDEO ![68747470733a2f2f692e696d6775722e636f6d2f644a486b345a712e676966.gif](https://cdn-uploads.huggingface.co/production/uploads/680f5da2b2fef41b5479f862/BkaSmQSDmDNZvEJ9OKJGG.gif)
mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF	mradermacher	2025-04-28T11:19:06Z	636	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:nbeerbower/EVA-abliterated-TIES-Qwen2.5-14B", "base_model:quantized:nbeerbower/EVA-abliterated-TIES-Qwen2.5-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-02-10T12:13:13Z	--- base_model: nbeerbower/EVA-abliterated-TIES-Qwen2.5-14B language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/nbeerbower/EVA-abliterated-TIES-Qwen2.5-14B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-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/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-i1-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mlgDiYTBOTm0/oosjvkx	mlgDiYTBOTm0	2025-04-28T11:17:15Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T11:17:15Z	--- license: apache-2.0 ---
TheStageAI/Elastic-DeepSeek-R1-Distill-Llama-8B	TheStageAI	2025-04-28T11:17:11Z	60	1	null	[ "text2text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "license:apache-2.0", "region:us" ]	text2text-generation	2025-04-24T14:17:26Z	--- license: apache-2.0 base_model: - deepseek-ai/DeepSeek-R1-Distill-Llama-8B base_model_relation: quantized pipeline_tag: text2text-generation language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Elastic model: DeepSeek-R1-Distill-Llama-8B. Fastest and most flexible models for self-serving. Elastic models are the models produced by TheStage AI ANNA: Automated Neural Networks Accelerator. ANNA allows you to control model size, latency and quality with a simple slider movement. For each model, ANNA produces a series of optimized models: * __XL__: Mathematically equivalent neural network, optimized with our DNN compiler. * __L__: Near lossless model, with less than 1% degradation obtained on corresponding benchmarks. * __M__: Faster model, with accuracy degradation less than 1.5%. * __S__: The fastest model, with accuracy degradation less than 2%. __Goals of elastic models:__ * Provide flexibility in cost vs quality selection for inference * Provide clear quality and latency benchmarks * Provide interface of HF libraries: transformers and diffusers with a single line of code * Provide models supported on a wide range of hardware, which are pre-compiled and require no JIT. * Provide the best models and service for self-hosting. > It's important to note that specific quality degradation can vary from model to model. For instance, with an S model, you can have 0.5% degradation as well. ![Performance Graph](images/performance_graph.png) ----- ## Inference To infer our models, you just need to replace `transformers` import with `elastic_models.transformers`: ```python import torch from transformers import AutoTokenizer from elastic_models.transformers import AutoModelForCausalLM # Currently we require to have your HF token # as we use original weights for part of layers and # model confugaration as well model_name = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" hf_token = '' device = torch.device("cuda") # Create mode tokenizer = AutoTokenizer.from_pretrained( model_name, token=hf_token ) model = AutoModelForCausalLM.from_pretrained( model_name, token=hf_token, torch_dtype=torch.bfloat16, attn_implementation="sdpa", mode='S' ).to(device) model.generation_config.pad_token_id = tokenizer.eos_token_id # Inference simple as transformers library prompt = "Describe basics of DNNs quantization." messages = [ { "role": "system", "content": "You are a search bot, answer on user text queries." }, { "role": "user", "content": prompt } ] chat_prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False ) inputs = tokenizer(chat_prompt, return_tensors="pt") inputs.to(device) with torch.inference_mode(): generate_ids = model.generate(*inputs, max_length=500) input_len = inputs['input_ids'].shape[1] generate_ids = generate_ids[:, input_len:] output = tokenizer.batch_decode( generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False )[0] # Validate answer print(f"# Q:\n{prompt}\n") print(f"# A:\n{output}\n") ``` __System requirements:__ GPUs: H100, L40s * CPU: AMD, Intel * Python: 3.10-3.12 To work with our models just run these lines in your terminal: ```shell pip install thestage pip install elastic_models[nvidia]\ --index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\ --extra-index-url https://pypi.nvidia.com\ --extra-index-url https://pypi.org/simple pip install flash_attn==2.7.3 --no-build-isolation pip uninstall apex ``` Then go to [app.thestage.ai](https://app.thestage.ai), login and generate API token from your profile page. Set up API token as follows: ```shell thestage config set --api-token <YOUR_API_TOKEN> ``` Congrats, now you can use accelerated models! ---- ## Benchmarks Benchmarking is one of the most important procedures during model acceleration. We aim to provide clear performance metrics for models using our algorithms. The `W8A8, int8 column` indicates that we applied W8A8 quantization with int8 data type to all linear layers and used the same calibration data as for ANNA. The S model achieves practically identical speed but much higher quality, as ANNA knows how to improve quantization quality on sensitive layers! ### Quality benchmarks \| Metric/Model \| S \| M \| L \| XL \| Original \| W8A8, int8 \| \|---------------\|---\|---\|---\|----\|----------\|------------\| \| arc_challenge \| 38.70 \| 40.40 \| 40.40 \| 40.50 \| 40.50 \| 19.30 \| - \| \| mmlu \| 52.70 \| 54.70 \| 55.50 \| 54.80 \| 54.80 \| 47.70 \| - \| \| piqa \| 76.30 \| 75.90 \| 75.70 \| 76.10 \| 76.10 \| 55.00 \| - \| \| winogrande \| 66.60 \| 66.20 \| 67.80 \| 68.00 \| 68.00 \| 56.10 \| - \| * MMLU: Evaluates general knowledge across 57 subjects including science, humanities, engineering, and more. Shows model's ability to handle diverse academic topics. * PIQA: Evaluates physical commonsense reasoning through questions about everyday physical interactions. Shows model's understanding of real-world physics concepts. * Arc Challenge: Evaluates grade-school level multiple-choice questions requiring reasoning. Shows model's ability to solve complex reasoning tasks. * Winogrande: Evaluates commonsense reasoning through sentence completion tasks. Shows model's capability to understand context and resolve ambiguity. ### Latency benchmarks __100 input/300 output; tok/s:__ \| GPU/Model \| S \| M \| L \| XL \| Original \| W8A8, int8 \| \|-----------\|-----\|---\|---\|----\|----------\|------------\| \| H100 \| 194 \| 191 \| 161 \| 131 \| 58 \| 198 \| - \| \| L40S \| 72 \| 70 \| 56 \| 44 \| 40 \| 74 \| - \| ## Links * __Platform__: [app.thestage.ai](app.thestage.ai) * __Subscribe for updates__: [TheStageAI X](https://x.com/TheStageAI) <!-- * __Elastic models Github__: [app.thestage.ai](app.thestage.ai) --> * __Contact email__: [email protected]
EAvUxwipnqj/dekfs	EAvUxwipnqj	2025-04-28T11:16:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T11:16:27Z	--- license: apache-2.0 ---
DanielNRU/pollen-ner-cycle-1000	DanielNRU	2025-04-28T11:15:24Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/rubert-base-cased", "base_model:adapter:DeepPavlov/rubert-base-cased", "region:us" ]	null	2025-04-28T10:31:15Z	--- library_name: peft base_model: DeepPavlov/rubert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner-cycle-1000 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. --> # pollen-ner-cycle-1000 This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2608 - Precision: 0.7063 - Recall: 0.8046 - F1: 0.7523 ## 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: 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 125 \| 0.8324 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 2.0 \| 250 \| 0.5457 \| 0.5 \| 0.4197 \| 0.4564 \| \| No log \| 3.0 \| 375 \| 0.4199 \| 0.5905 \| 0.6944 \| 0.6382 \| \| 0.7256 \| 4.0 \| 500 \| 0.3539 \| 0.6190 \| 0.7447 \| 0.6760 \| \| 0.7256 \| 5.0 \| 625 \| 0.3166 \| 0.6618 \| 0.7872 \| 0.7191 \| \| 0.7256 \| 6.0 \| 750 \| 0.2972 \| 0.6650 \| 0.7950 \| 0.7242 \| \| 0.7256 \| 7.0 \| 875 \| 0.2787 \| 0.6965 \| 0.7988 \| 0.7441 \| \| 0.3014 \| 8.0 \| 1000 \| 0.2653 \| 0.7096 \| 0.7988 \| 0.7516 \| \| 0.3014 \| 9.0 \| 1125 \| 0.2601 \| 0.7133 \| 0.7988 \| 0.7536 \| \| 0.3014 \| 10.0 \| 1250 \| 0.2608 \| 0.7063 \| 0.8046 \| 0.7523 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
dgambettaphd/M_llm2_gen1_run0_S_doc1000_synt64_tot128_SYNLAST	dgambettaphd	2025-04-28T11:14:38Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:14:25Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
technocrat3128/ISLexitas_NAWAH_28-04-25	technocrat3128	2025-04-28T11:13:37Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:12:29Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: technocrat3128 - 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)
conheocon/unin	conheocon	2025-04-28T11:13:31Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2025-04-28T11:13:27Z	--- license: bsd-3-clause-clear ---
Triangle104/Athena-3.5-3B-Q5_K_M-GGUF	Triangle104	2025-04-28T11:13:19Z	0	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "sft", "llama-cpp", "gguf-my-repo", "base_model:Spestly/Athena-3.5-3B", "base_model:quantized:Spestly/Athena-3.5-3B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T11:12:27Z	--- base_model: Spestly/Athena-3.5-3B library_name: transformers tags: - unsloth - trl - sft - llama-cpp - gguf-my-repo --- # Triangle104/Athena-3.5-3B-Q5_K_M-GGUF This model was converted to GGUF format from [`Spestly/Athena-3.5-3B`](https://huggingface.co/Spestly/Athena-3.5-3B) 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/Spestly/Athena-3.5-3B) 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 Triangle104/Athena-3.5-3B-Q5_K_M-GGUF --hf-file athena-3.5-3b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Athena-3.5-3B-Q5_K_M-GGUF --hf-file athena-3.5-3b-q5_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 Triangle104/Athena-3.5-3B-Q5_K_M-GGUF --hf-file athena-3.5-3b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Athena-3.5-3B-Q5_K_M-GGUF --hf-file athena-3.5-3b-q5_k_m.gguf -c 2048 ```
MayBashendy/ellipse_SDP_all_binary_multilingual_e5_small_lr3e-05_targ2	MayBashendy	2025-04-28T11:10:46Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-04-28T09:23:51Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
phospho-app/GetTheRubberNextG2-123	phospho-app	2025-04-28T11:10:11Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-04-28T10:45:19Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [nebo1337/GetTheRubberNextG2](https://huggingface.co/datasets/nebo1337/GetTheRubberNextG2) - Wandb run URL: None - Epochs: 1 - Batch size: 64 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
ThomasBaruzier/QwQ-32B-Preview-EXL2	ThomasBaruzier	2025-04-28T11:09:38Z	36	0	transformers	[ "transformers", "chat", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-11-29T09:29:45Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: Qwen/Qwen2.5-32B-Instruct tags: - chat library_name: transformers --- <hr> # ExllamaV2 quantizations of Qwen/QwQ-32B-Preview <img src="https://cdn-uploads.huggingface.co/production/uploads/646410e04bf9122922289dc7/EuI1mJxPIfBW2Dl3GzW6w.jpeg" alt="qwen" width="60%"/> Using ExllamaV2 commit [735fa7b](https://github.com/turboderp/exllamav2/commit/735fa7b) for quantization. Original model: [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) All quants were made using the default [calibration files](https://github.com/turboderp/exllamav2/tree/master/exllamav2/conversion/standard_cal_data). <hr> # Branches \| Precision \| Size \| \| -------------------------------------------------------------------------------- \| ------- \| \| [2.5bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/2.5bpw) \| 11374MB \| \| [2.6bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/2.6bpw) \| 11743MB \| \| [2.7bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/2.7bpw) \| 12118MB \| \| [2.8bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/2.8bpw) \| 12490MB \| \| [2.9bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/2.9bpw) \| 12863MB \| \| [3.0bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/3.0bpw) \| 13234MB \| \| [3.1bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/3.1bpw) \| 13601MB \| \| [3.2bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/3.2bpw) \| 13978MB \| \| [3.3bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/3.3bpw) \| 14348MB \| \| [3.4bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/3.4bpw) \| 14722MB \| \| [3.5bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/3.5bpw) \| 15088MB \| \| [3.6bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/3.6bpw) \| 15466MB \| \| [3.7bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/3.7bpw) \| 15836MB \| \| [3.8bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/3.8bpw) \| 16195MB \| \| [3.9bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/3.9bpw) \| 16570MB \| \| [4.0bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/4.0bpw) \| 16954MB \| \| [4.1bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/4.1bpw) \| 17323MB \| \| [4.2bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/4.2bpw) \| 17694MB \| \| [4.3bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/4.3bpw) \| 18068MB \| \| [4.4bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/4.4bpw) \| 18440MB \| \| [4.5bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/4.5bpw) \| 18811MB \| \| [4.7bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/4.7bpw) \| 19572MB \| \| [5.0bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/5.0bpw) \| 20657MB \| \| [5.5bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/5.5bpw) \| 22527MB \| \| [6.0bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/6.0bpw) \| 24389MB \| \| [8.0bpw](https://huggingface.co/ThomasBaruzier/QwQ-32B-Preview-EXL2/tree/8.0bpw) \| 30158MB \| <hr> # Perplexity table (the lower the better) <hr> # QwQ-32B-Preview ## Introduction QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities. As a preview release, it demonstrates promising analytical abilities while having several important limitations: 1. Language Mixing and Code-Switching: The model may mix languages or switch between them unexpectedly, affecting response clarity. 2. Recursive Reasoning Loops: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer. 3. Safety and Ethical Considerations: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it. 4. Performance and Benchmark Limitations: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding. Specification: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 32,768 tokens For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwq-32b-preview/). You can also check Qwen2.5 [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/QwQ-32B-Preview" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "How many r in strawberry." messages = [ {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwq-32b-preview, title = {QwQ: Reflect Deeply on the Boundaries of the Unknown}, url = {https://qwenlm.github.io/blog/qwq-32b-preview/}, author = {Qwen Team}, month = {November}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
TheStageAI/Elastic-Qwen2.5-7B-Instruct	TheStageAI	2025-04-28T11:09:20Z	30	2	null	[ "text2text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:quantized:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "region:us" ]	text2text-generation	2025-04-22T16:29:01Z	--- license: apache-2.0 base_model: - Qwen/Qwen2.5-7B-Instruct base_model_relation: quantized pipeline_tag: text2text-generation language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Elastic model: Qwen2.5-7B-Instruct. Fastest and most flexible models for self-serving. Elastic models are the models produced by TheStage AI ANNA: Automated Neural Networks Accelerator. ANNA allows you to control model size, latency and quality with a simple slider movement. For each model, ANNA produces a series of optimized models: * __XL__: Mathematically equivalent neural network, optimized with our DNN compiler. * __L__: Near lossless model, with less than 1% degradation obtained on corresponding benchmarks. * __M__: Faster model, with accuracy degradation less than 1.5%. * __S__: The fastest model, with accuracy degradation less than 2%. __Goals of elastic models:__ * Provide flexibility in cost vs quality selection for inference * Provide clear quality and latency benchmarks * Provide interface of HF libraries: transformers and diffusers with a single line of code * Provide models supported on a wide range of hardware, which are pre-compiled and require no JIT. * Provide the best models and service for self-hosting. > It's important to note that specific quality degradation can vary from model to model. For instance, with an S model, you can have 0.5% degradation as well. ![Performance Graph](images/performance_graph.png) ----- ## Inference To infer our models, you just need to replace `transformers` import with `elastic_models.transformers`: ```python import torch from transformers import AutoTokenizer from elastic_models.transformers import AutoModelForCausalLM # Currently we require to have your HF token # as we use original weights for part of layers and # model confugaration as well model_name = "Qwen/Qwen2.5-7B-Instruct" hf_token = '' device = torch.device("cuda") # Create mode tokenizer = AutoTokenizer.from_pretrained( model_name, token=hf_token ) model = AutoModelForCausalLM.from_pretrained( model_name, token=hf_token, torch_dtype=torch.bfloat16, attn_implementation="sdpa", mode='S' ).to(device) model.generation_config.pad_token_id = tokenizer.eos_token_id # Inference simple as transformers library prompt = "Describe basics of DNNs quantization." messages = [ { "role": "system", "content": "You are a search bot, answer on user text queries." }, { "role": "user", "content": prompt } ] chat_prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False ) inputs = tokenizer(chat_prompt, return_tensors="pt") inputs.to(device) with torch.inference_mode(): generate_ids = model.generate(*inputs, max_length=500) input_len = inputs['input_ids'].shape[1] generate_ids = generate_ids[:, input_len:] output = tokenizer.batch_decode( generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False )[0] # Validate answer print(f"# Q:\n{prompt}\n") print(f"# A:\n{output}\n") ``` __System requirements:__ GPUs: H100, L40s * CPU: AMD, Intel * Python: 3.10-3.12 To work with our models just run these lines in your terminal: ```shell pip install thestage pip install elastic_models[nvidia]\ --index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\ --extra-index-url https://pypi.nvidia.com\ --extra-index-url https://pypi.org/simple pip install flash_attn==2.7.3 --no-build-isolation pip uninstall apex ``` Then go to [app.thestage.ai](https://app.thestage.ai), login and generate API token from your profile page. Set up API token as follows: ```shell thestage config set --api-token <YOUR_API_TOKEN> ``` Congrats, now you can use accelerated models! ---- ## Benchmarks Benchmarking is one of the most important procedures during model acceleration. We aim to provide clear performance metrics for models using our algorithms. The `W8A8, int8 column` indicates that we applied W8A8 quantization with int8 data type to all linear layers and used the same calibration data as for ANNA. The S model achieves practically identical speed but much higher quality, as ANNA knows how to improve quantization quality on sensitive layers! ### Quality benchmarks \| Metric/Model \| S \| M \| L \| XL \| Original \| W8A8, int8 \| \|---------------\|---\|---\|---\|----\|----------\|------------\| \| arc_challenge \| 49.10 \| 50.10 \| 53.20 \| 52.60 \| 52.60 \| 41.70 \| - \| \| mmlu \| 71.70 \| 73.00 \| 74.10 \| 73.50 \| 73.50 \| 64.60 \| - \| \| piqa \| 77.00 \| 78.20 \| 78.80 \| 79.50 \| 79.50 \| 67.10 \| - \| \| winogrande \| 66.20 \| 69.10 \| 71.50 \| 70.60 \| 70.60 \| 53.10 \| - \| * MMLU: Evaluates general knowledge across 57 subjects including science, humanities, engineering, and more. Shows model's ability to handle diverse academic topics. * PIQA: Evaluates physical commonsense reasoning through questions about everyday physical interactions. Shows model's understanding of real-world physics concepts. * Arc Challenge: Evaluates grade-school level multiple-choice questions requiring reasoning. Shows model's ability to solve complex reasoning tasks. * Winogrande: Evaluates commonsense reasoning through sentence completion tasks. Shows model's capability to understand context and resolve ambiguity. ### Latency benchmarks __100 input/300 output; tok/s:__ \| GPU/Model \| S \| M \| L \| XL \| Original \| W8A8, int8 \| \|-----------\|-----\|---\|---\|----\|----------\|------------\| \| H100 \| 201 \| 173 \| 162 \| 135 \| 62 \| 201 \| - \| \| L40S \| 76 \| 67 \| 61 \| 47 \| 43 \| 78 \| - \| ## Links * __Platform__: [app.thestage.ai](app.thestage.ai) * __Subscribe for updates__: [TheStageAI X](https://x.com/TheStageAI) <!-- * __Elastic models Github__: [app.thestage.ai](app.thestage.ai) --> * __Contact email__: [email protected]
abdullahkhudhair/LTR-categories-spanish_v1	abdullahkhudhair	2025-04-28T11:08:35Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-28T11:07:29Z	--- 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. 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danyush/qwen2.5_vl-3b_virat_lr_shuffled	danyush	2025-04-28T11:07:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "llama-factory", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-28T10:58:03Z	--- library_name: transformers tags: - llama-factory --- # 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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thexdk/llm-course-hw2-ppo	thexdk	2025-04-28T11:06:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T11:05:33Z	--- 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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deeponh/hindi_8b_3b_D2	deeponh	2025-04-28T11:06:34Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T09:45:35Z	--- 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. 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ThomasBaruzier/Qwen2.5-7B-Instruct-GGUF	ThomasBaruzier	2025-04-28T11:05:50Z	85	0	null	[ "gguf", "chat", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2309.00071", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-7B", "base_model:quantized:Qwen/Qwen2.5-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-19T17:05:22Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-7B tags: - chat --- <hr> # Llama.cpp imatrix quantizations of Qwen/Qwen2.5-7B-Instruct <img src="https://cdn-uploads.huggingface.co/production/uploads/646410e04bf9122922289dc7/gDUbZOu1ND0j-th4Q6tep.jpeg" alt="qwen" width="60%"/> Using llama.cpp commit [eca0fab](https://github.com/ggerganov/llama.cpp/commit/eca0fab) for quantization. Original model: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) All quants were made using the imatrix option and Bartowski's [calibration file](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8). <hr> # Perplexity table (the lower the better) \| Quant \| Size (MB) \| PPL \| Size (%) \| Accuracy (%) \| PPL error rate \| \| ------- \| --------- \| ------- \| -------- \| ------------ \| -------------- \| \| IQ1_S \| 1816 \| 29.2065 \| 12.5 \| 27.04 \| 0.23022 \| \| IQ1_M \| 1948 \| 17.5876 \| 13.4 \| 44.9 \| 0.13256 \| \| IQ2_XXS \| 2168 \| 13.0997 \| 14.92 \| 60.28 \| 0.10138 \| \| IQ2_XS \| 2355 \| 10.6611 \| 16.21 \| 74.06 \| 0.07823 \| \| IQ2_S \| 2476 \| 10.236 \| 17.04 \| 77.14 \| 0.07461 \| \| IQ2_M \| 2652 \| 9.5241 \| 18.25 \| 82.91 \| 0.06876 \| \| Q2_K_S \| 2703 \| 9.7181 \| 18.6 \| 81.25 \| 0.07113 \| \| Q2_K \| 2877 \| 9.271 \| 19.8 \| 85.17 \| 0.06672 \| \| IQ3_XXS \| 2971 \| 8.677 \| 20.44 \| 91 \| 0.06191 \| \| IQ3_XS \| 3192 \| 8.468 \| 21.97 \| 93.25 \| 0.05972 \| \| Q3_K_S \| 3331 \| 8.6524 \| 22.92 \| 91.26 \| 0.06144 \| \| IQ3_S \| 3338 \| 8.4723 \| 22.97 \| 93.2 \| 0.06045 \| \| IQ3_M \| 3409 \| 8.4488 \| 23.46 \| 93.46 \| 0.05973 \| \| Q3_K_M \| 3632 \| 8.2232 \| 24.99 \| 96.02 \| 0.05882 \| \| Q3_K_L \| 3900 \| 8.1675 \| 26.84 \| 96.68 \| 0.05826 \| \| IQ4_XS \| 4024 \| 8.0803 \| 27.69 \| 97.72 \| 0.0579 \| \| IQ4_NL \| 4233 \| 8.0301 \| 29.13 \| 98.33 \| 0.05724 \| \| Q4_0 \| 4239 \| 7.9852 \| 29.17 \| 98.88 \| 0.05586 \| \| Q4_K_S \| 4252 \| 8.0483 \| 29.26 \| 98.11 \| 0.05752 \| \| Q4_K_M \| 4467 \| 7.997 \| 30.74 \| 98.74 \| 0.05697 \| \| Q4_1 \| 4648 \| 8.1016 \| 31.98 \| 97.46 \| 0.05824 \| \| Q5_K_S \| 5069 \| 7.9466 \| 34.88 \| 99.36 \| 0.05652 \| \| Q5_0 \| 5082 \| 7.9939 \| 34.97 \| 98.78 \| 0.05726 \| \| Q5_K_M \| 5193 \| 7.9279 \| 35.73 \| 99.6 \| 0.05633 \| \| Q5_1 \| 5491 \| 7.9334 \| 37.79 \| 99.53 \| 0.05634 \| \| Q6_K \| 5965 \| 7.9066 \| 41.05 \| 99.87 \| 0.05617 \| \| Q8_0 \| 7724 \| 7.8996 \| 53.15 \| 99.96 \| 0.05614 \| \| F16 \| 14532 \| 7.8961 \| 100 \| 100 \| 0.05608 \| <hr> # Qwen2.5-7B-Instruct ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 7B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 for KV - Context Length: Full 131,072 tokens and generation 8192 tokens - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-7B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` For deployment, we recommend using vLLM. Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
ASethi04/meta-llama-Llama-3.1-8B-pubmedqa-third-full-parameter-4-1e-05	ASethi04	2025-04-28T11:05:46Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T09:35:25Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-pubmedqa-third-full-parameter-4-1e-05 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-pubmedqa-third-full-parameter-4-1e-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-pubmedqa-third-full-parameter-4-1e-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/c99mmu16) 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}} } ```
arcee-samsung/Sky-T1-32B-Flash-Spectrum	arcee-samsung	2025-04-28T11:02:59Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "arxiv:2401.08417", "base_model:NovaSky-AI/Sky-T1-32B-Preview", "base_model:finetune:NovaSky-AI/Sky-T1-32B-Preview", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T10:52:32Z	--- base_model: NovaSky-AI/Sky-T1-32B-Preview library_name: transformers model_name: outputs/simpo-skyT1-out tags: - generated_from_trainer licence: license --- # Model Card for outputs/simpo-skyT1-out This model is a fine-tuned version of [NovaSky-AI/Sky-T1-32B-Preview](https://huggingface.co/NovaSky-AI/Sky-T1-32B-Preview). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with CPO, a method introduced in [Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation](https://huggingface.co/papers/2401.08417). ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite CPO as: ```bibtex @inproceedings{xu2024contrastive, title = {{Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}}, author = {Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim}, year = 2024, booktitle = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024}, publisher = {OpenReview.net}, url = {https://openreview.net/forum?id=51iwkioZpn} } ``` 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}} } ```
maksf8486/ad02eaea-0a89-4822-9825-d628c096f60b	maksf8486	2025-04-28T10:59:21Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/codegemma-2b", "base_model:adapter:unsloth/codegemma-2b", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T10:32:46Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codegemma-2b tags: - axolotl - generated_from_trainer model-index: - name: ad02eaea-0a89-4822-9825-d628c096f60b 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/codegemma-2b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ba0621f537bc8cd4_train_data.json ds_type: json format: custom path: /workspace/input_data/ba0621f537bc8cd4_train_data.json type: field_input: system field_instruction: question field_output: chosen format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: false reference_model: 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: maksf8486/ad02eaea-0a89-4822-9825-d628c096f60b hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/ba0621f537bc8cd4_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: 37395544-fb64-4439-9c6c-16a1de7f207f wandb_project: s56-2 wandb_run: your_name wandb_runid: 37395544-fb64-4439-9c6c-16a1de7f207f warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # ad02eaea-0a89-4822-9825-d628c096f60b This model is a fine-tuned version of [unsloth/codegemma-2b](https://huggingface.co/unsloth/codegemma-2b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9049 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8693 \| 0.0088 \| 200 \| 0.9049 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
fats-fme/fccb9ecb-f0ef-4add-bb56-ffd4561dcd23	fats-fme	2025-04-28T10:58:49Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/codegemma-2b", "base_model:adapter:unsloth/codegemma-2b", "license:apache-2.0", "region:us" ]	null	2025-04-28T10:32:09Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codegemma-2b tags: - axolotl - generated_from_trainer model-index: - name: fccb9ecb-f0ef-4add-bb56-ffd4561dcd23 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/codegemma-2b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ba0621f537bc8cd4_train_data.json ds_type: json format: custom path: /workspace/input_data/ba0621f537bc8cd4_train_data.json type: field_input: system field_instruction: question field_output: chosen format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: fats-fme/fccb9ecb-f0ef-4add-bb56-ffd4561dcd23 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 130GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/ba0621f537bc8cd4_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 2048 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 37395544-fb64-4439-9c6c-16a1de7f207f wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 37395544-fb64-4439-9c6c-16a1de7f207f warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # fccb9ecb-f0ef-4add-bb56-ffd4561dcd23 This model is a fine-tuned version of [unsloth/codegemma-2b](https://huggingface.co/unsloth/codegemma-2b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7279 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - 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: 200 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0000 \| 1 \| 1.1109 \| \| 0.7957 \| 0.0044 \| 100 \| 0.8069 \| \| 0.7234 \| 0.0088 \| 200 \| 0.7279 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
deeponh/bengali_8b_3b_D2	deeponh	2025-04-28T10:57:56Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T07:56:41Z	--- 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. 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OpenVINO/Qwen2.5-1.5B-Instruct-fp16-ov	OpenVINO	2025-04-28T10:57:08Z	230	0	null	[ "openvino", "qwen2", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-04-04T11:23:53Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE base_model: - Qwen/Qwen2.5-1.5B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Qwen2.5-1.5B-Instruct-fp16-ov * Model creator: [Qwen](https://huggingface.co/Qwen) * Original model: [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) ## Description This is [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to FP16. ## Compatibility The provided OpenVINO™ IR model is compatible with: * OpenVINO version 2025.1.0 and higher * Optimum Intel 1.24.0 and higher ## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) 1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend: ``` pip install optimum[openvino] ``` 2. Run model inference: ``` from transformers import AutoTokenizer from optimum.intel.openvino import OVModelForCausalLM model_id = "OpenVINO/qwen2.5-1.5b-instruct-fp16-ov" tokenizer = AutoTokenizer.from_pretrained(model_id) model = OVModelForCausalLM.from_pretrained(model_id) inputs = tokenizer("What is OpenVINO?", return_tensors="pt") outputs = model.generate(**inputs, max_length=200) text = tokenizer.batch_decode(outputs)[0] print(text) ``` For more examples and possible optimizations, refer to the [Inference with Optimum Intel](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-optimum-intel.html). ## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai) 1. Install packages required for using OpenVINO GenAI. ``` pip install openvino-genai huggingface_hub ``` 2. Download model from HuggingFace Hub ``` import huggingface_hub as hf_hub model_id = "OpenVINO/qwen2.5-1.5b-instruct-fp16-ov" model_path = "qwen2.5-1.5b-instruct-fp16-ov" hf_hub.snapshot_download(model_id, local_dir=model_path) ``` 3. Run model inference: ``` import openvino_genai as ov_genai device = "CPU" pipe = ov_genai.LLMPipeline(model_path, device) print(pipe.generate("What is OpenVINO?", max_length=200)) ``` More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-genai.html) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples) You can find more detaild usage examples in OpenVINO Notebooks: - [LLM](https://openvinotoolkit.github.io/openvino_notebooks/?search=LLM) - [RAG text generation](https://openvinotoolkit.github.io/openvino_notebooks/?search=RAG+system&tasks=Text+Generation) ## Limitations Check the original [model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for limitations. ## Legal information The original model is distributed under [Apache License Version 2.0](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE) license. More details can be found in [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). ## Disclaimer Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
OpenVINO/Qwen2.5-1.5B-Instruct-int4-ov	OpenVINO	2025-04-28T10:56:53Z	15	0	null	[ "openvino", "qwen2", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-04-10T08:54:51Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE base_model: - Qwen/Qwen2.5-1.5B-Instruct base_model_relation: quantized language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Qwen2.5-1.5B-Instruct-int4-ov * Model creator: [Qwen](https://huggingface.co/Qwen) * Original model: [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) ## Description This is [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT4 by [NNCF](https://github.com/openvinotoolkit/nncf). ## Quantization Parameters Weight compression was performed using `nncf.compress_weights` with the following parameters: * mode: INT4_SYM * ratio: 1 * group_size: 128 For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2025/openvino-workflow/model-optimization-guide/weight-compression.html). ## Compatibility The provided OpenVINO™ IR model is compatible with: * OpenVINO version 2025.1.0 and higher * Optimum Intel 1.24.0 and higher ## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) 1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend: ``` pip install optimum[openvino] ``` 2. Run model inference: ``` from transformers import AutoTokenizer from optimum.intel.openvino import OVModelForCausalLM model_id = "OpenVINO/qwen2.5-1.5b-instruct-int4-ov" tokenizer = AutoTokenizer.from_pretrained(model_id) model = OVModelForCausalLM.from_pretrained(model_id) inputs = tokenizer("What is OpenVINO?", return_tensors="pt") outputs = model.generate(**inputs, max_length=200) text = tokenizer.batch_decode(outputs)[0] print(text) ``` For more examples and possible optimizations, refer to the [Inference with Optimum Intel](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-optimum-intel.html). ## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai) 1. Install packages required for using OpenVINO GenAI. ``` pip install openvino-genai huggingface_hub ``` 2. Download model from HuggingFace Hub ``` import huggingface_hub as hf_hub model_id = "OpenVINO/qwen2.5-1.5b-instruct-int4-ov" model_path = "qwen2.5-1.5b-instruct-int4-ov" hf_hub.snapshot_download(model_id, local_dir=model_path) ``` 3. Run model inference: ``` import openvino_genai as ov_genai device = "CPU" pipe = ov_genai.LLMPipeline(model_path, device) print(pipe.generate("What is OpenVINO?", max_length=200)) ``` More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-genai.html) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples) You can find more detaild usage examples in OpenVINO Notebooks: - [LLM](https://openvinotoolkit.github.io/openvino_notebooks/?search=LLM) - [RAG text generation](https://openvinotoolkit.github.io/openvino_notebooks/?search=RAG+system&tasks=Text+Generation) ## Limitations Check the original [model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for limitations. ## Legal information The original model is distributed under [Apache License Version 2.0](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE) license. More details can be found in [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). ## Disclaimer Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
deeponh/bengali_8b_8b_D2	deeponh	2025-04-28T10:53:46Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T10:48:25Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
TETzT2WpEOQb/kaldoa	TETzT2WpEOQb	2025-04-28T10:52:56Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T10:52:56Z	--- license: apache-2.0 ---
u-lee/mobile_healthcare_exercise_model	u-lee	2025-04-28T10:52:35Z	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-04-28T10:38:50Z	--- license: apache-2.0 ---
HZiAldcqCNuXl/ohudwa	HZiAldcqCNuXl	2025-04-28T10:52:23Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T10:52:13Z	--- license: apache-2.0 ---
NexesMess/Llama_3.x_70b_TDRussel-Storywriter_128K_Dop_v1.02	NexesMess	2025-04-28T10:51:51Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2406.11617", "base_model:nbeerbower/Llama3.1-Gutenberg-Doppel-70B", "base_model:merge:nbeerbower/Llama3.1-Gutenberg-Doppel-70B", "base_model:tdrussell/Llama-3-70B-Instruct-Storywriter", "base_model:merge:tdrussell/Llama-3-70B-Instruct-Storywriter", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-27T14:50:03Z	--- base_model: - nbeerbower/Llama3.1-Gutenberg-Doppel-70B - tdrussell/Llama-3-70B-Instruct-Storywriter 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 [Linear DELLA](https://arxiv.org/abs/2406.11617) merge method using [nbeerbower/Llama3.1-Gutenberg-Doppel-70B](https://huggingface.co/nbeerbower/Llama3.1-Gutenberg-Doppel-70B) as a base. ### Models Merged The following models were included in the merge: * [tdrussell/Llama-3-70B-Instruct-Storywriter](https://huggingface.co/tdrussell/Llama-3-70B-Instruct-Storywriter) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: della_linear base_model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B models: - model: tdrussell/Llama-3-70B-Instruct-Storywriter parameters: weight: # layer per layer - filter: q_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: k_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: v_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: o_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: input_layernorm value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: up_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: gate_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: down_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: post_attention_layernorm value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] density: 0.5 epsilon: 0.1 lambda: 1.0 - model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B parameters: weight: 1.0 density: # layer per layer - filter: q_proj value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - filter: k_proj value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - filter: v_proj value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - filter: o_proj value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - filter: input_layernorm value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - filter: up_proj value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - filter: gate_proj value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - filter: down_proj value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - filter: post_attention_layernorm value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] epsilon: # layer per layer - filter: q_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: k_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: v_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: o_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: input_layernorm value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: up_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: gate_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: down_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: post_attention_layernorm value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] lambda: 1.0 dtype: float32 out_dtype: bfloat16 parameters: int8_mask: true normalize: true rescale: true chat_template: auto tokenizer: source: union ```
secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation_qwen_code_14b_5k_first_2000_generation	secmlr	2025-04-28T10:51:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-Coder-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-14B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T06:54:59Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-14B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: SWE-BENCH-5k-first-2000-claude-search-replace-generation_qwen_code_14b_5k_first_2000_generation 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. --> # SWE-BENCH-5k-first-2000-claude-search-replace-generation_qwen_code_14b_5k_first_2000_generation This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) on the SWE-BENCH-5k-first-2000-claude-search-replace-generation 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 12 - total_train_batch_size: 48 - total_eval_batch_size: 32 - 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: 3.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 2.20.0 - Tokenizers 0.20.3
TZxrmWMk9lhRiu/akdohw	TZxrmWMk9lhRiu	2025-04-28T10:51:29Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T10:51:29Z	--- license: apache-2.0 ---
Zig4UZdPpLtJ59/jakkd	Zig4UZdPpLtJ59	2025-04-28T10:50:56Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T10:50:56Z	--- license: apache-2.0 ---
SmailOmar05/Smailomar	SmailOmar05	2025-04-28T10:49:04Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T10:49:04Z	--- license: apache-2.0 ---
secmlr/DS-Noisy-N_DS-Clean-N_DS-OSS-N_QWQ-OSS-N_QWQ-Clean-N_QWQ-Noisy-N_Qwen2.5-7B-Instruct_sft	secmlr	2025-04-28T10:48:07Z	3	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T03:17:53Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: DS-Noisy-N_DS-Clean-N_DS-OSS-N_QWQ-OSS-N_QWQ-Clean-N_QWQ-Noisy-N_Qwen2.5-7B-Instruct_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. --> # DS-Noisy-N_DS-Clean-N_DS-OSS-N_QWQ-OSS-N_QWQ-Clean-N_QWQ-Noisy-N_Qwen2.5-7B-Instruct_sft This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the DS-Noisy-N, the DS-Clean-N, the DS-OSS-N, the QWQ-OSS-N, the QWQ-Clean-N and the QWQ-Noisy-N datasets. ## 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: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 12 - total_train_batch_size: 24 - total_eval_batch_size: 16 - 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: 3.0 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
Rinnnt/Reinforce-Pixelcopter-PLE-v0	Rinnnt	2025-04-28T10:47:35Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-04-28T10:47:27Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 19.80 +/- 14.69 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
mlfoundations-dev/nemo_nano_3k	mlfoundations-dev	2025-04-28T10:47:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T06:29:47Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: nemo_nano_3k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nemo_nano_3k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/nemo_nano_3k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
KingNish/Qwen2.5-0.5b-RBase	KingNish	2025-04-28T10:45:09Z	7	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-10-02T16:25:21Z	--- base_model: - Qwen/Qwen2.5-0.5B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft --- # Qwen 2.5 0.5B Model ## Model Description This model is a compact yet powerful language model trained to answer a variety of questions with impressive quality. Despite its smaller size, it has demonstrated performance comparable to Llama 3.2 1B, and in some cases, it even outperforms it. This model was specifically trained on a 12,800 rows of the Magpie 300k Dataset. ## Performance The Qwen 2.5 model has shown promising results in various tests, including the "strawberry test, Decimal Comparison test" where it successfully provided accurate answers. However, it is important to note that, like many models of its size, it may occasionally produce incorrect answers or flawed reasoning. Continuous improvements and full training are planned to enhance its performance further. ## How to Use To use the Qwen 2.5 model, you can load it using the Hugging Face Transformers library. Here’s a simple example: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "KingNish/Qwen2.5-0.5b-Test-ft" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Which is greater 9.9 or 9.11 ??" messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ## Future Work I am actively working on improving the Qwen 2.5 model by training it on a larger dataset. # Uploaded model - Developed by: KingNish - License: apache-2.0 - Finetuned from model :** Qwen/Qwen2.5-0.5B-Instruct This qwen2 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)
masoudkaviani/whisper-medium-fa	masoudkaviani	2025-04-28T10:44:37Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "fa", "dataset:mozilla-foundation/common_voice_17_0", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-28T10:35:28Z	--- library_name: transformers language: - fa license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 model-index: - name: Whisper medium Fa - Common Voice 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 medium Fa - Common Voice This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 17.0 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: 1e-05 - train_batch_size: 16 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF	mradermacher	2025-04-28T10:44:30Z	287	1	transformers	[ "transformers", "gguf", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:Orion-zhen/meissa-unalignments", "base_model:Orion-zhen/Qwen2.5-14B-Instruct-Uncensored", "base_model:quantized:Orion-zhen/Qwen2.5-14B-Instruct-Uncensored", "license:gpl-3.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-07T10:18:31Z	--- base_model: Orion-zhen/Qwen2.5-14B-Instruct-Uncensored datasets: - Orion-zhen/meissa-unalignments language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: gpl-3.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Orion-zhen/Qwen2.5-14B-Instruct-Uncensored <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-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/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-14B-Instruct-Uncensored.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
ArliAI/QwQ-32B-ArliAI-RpR-v3-GGUF	ArliAI	2025-04-28T10:43:10Z	240	1	null	[ "gguf", "en", "base_model:ArliAI/QwQ-32B-ArliAI-RpR-v3", "base_model:quantized:ArliAI/QwQ-32B-ArliAI-RpR-v3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-27T13:15:21Z	--- license: apache-2.0 thumbnail: "https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/coilCTGeL0OUYr9PA9zna.jpeg" language: - en base_model: - ArliAI/QwQ-32B-ArliAI-RpR-v3 --- # QwQ-32B-ArliAI-RpR-v3 <img src="https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/coilCTGeL0OUYr9PA9zna.jpeg" alt="clickbait" width="500"> <small>Image generated using Arli AI Image Generation https://www.arliai.com/image-generation</small> ## RpR v3 Changes: - The best model from ArliAI yet: Extreme creativity and out of the box thinking. - No longer use QwQ-abliterated as base: v3 is a re-do of v2 but without the problems stemming from starting out with a QwQ-lorablated base. This turned out to not be a good move as it clearly lobotomizes the model more and was even visible from higher training and eval loss values. - Fixed dissasociated thoughts: A lot of effort have been made to completely re-run the RpR dataset generation in order to make sure the generated thinking tokens now always match what the model responses are. - Fixed random refusals: The previous RpR v1 dataset was generated with vanilla QwQ which caused some refusals in both the thinking and response examples, with RpR v3 the dataset generation is now done using QwQ-abliterated which prevents any refusals from coming through. - Fixed nonsense words found in dataset: There were a bunch of presumably censoring attempts found on the open datasets used for the RPMax/RpR datasets and these misplaced words/phrases has now been fixed to prevent the model from copying this behavior. - Rex scheduler: v3 is trained using the newer and better Rex scheduler instead of the regular cosine scheduler in order to improve the model learning nuances from more of the dataset as this scheduler keeps the learning rate higher for longer. ## RpR Series Overview: Building on RPMax with Reasoning RpR (RolePlay with Reasoning) is a new series of models from ArliAI. This series builds directly upon the successful dataset curation methodology and training methods developed for the RPMax series. RpR models use the same curated, deduplicated RP and creative writing dataset used for RPMax, with a focus on variety to ensure high creativity and minimize cross-context repetition. Users familiar with RPMax will recognize the unique, non-repetitive writing style unlike other finetuned-for-RP models. With the release of QwQ as the first high performing open-source reasoning model that can be easily trained, it was clear that the available instruct and creative writing reasoning datasets contains only one response per example. This is type of single response dataset used for training reasoning models causes degraded output quality in long multi-turn chats. Which is why Arli AI decided to create a real RP model capable of long multi-turn chat with reasoning. In order to create RpR, we first had to actually create the reasoning RP dataset by re-processing our existing known-good RPMax dataset into a reasoning dataset. This was possible by using the base QwQ Instruct model itself to create the reasoning process for every turn in the RPMax dataset conversation examples, which is then further refined in order to make sure the reasoning is in-line with the actual response examples from the dataset. Another important thing to get right is to make sure the model is trained on examples that present reasoning blocks in the same way as it encounters it during inference. Which is, never seeing the reasoning blocks in it's context. In order to do this, the training run was completed using axolotl with manual template-free segments dataset in order to make sure that the model is never trained to see the reasoning block in the context. Just like how the model will be used during inference time. The result of training QwQ on this dataset with this method are consistently coherent and interesting outputs even in long multi-turn RP chats. This is as far as we know the first true correctly-trained reasoning model trained for RP and creative writing. You can access the model at https://arliai.com and we also have a models ranking page at https://www.arliai.com/models-ranking Ask questions in our new Discord Server https://discord.com/invite/t75KbPgwhk or on our subreddit https://www.reddit.com/r/ArliAI/ ## Model Description QwQ-32B-ArliAI-RpR-v3 is the third release in the RpR series. It is a 32-billion parameter model fine-tuned using the RpR dataset based on the curated RPMax dataset combined with techniques to maintain reasoning abilities in long multi-turn chats. ### Specs * Base Model: QwQ-32B * Max Context Length: Max 128K (Realistically 32K) * Parameters: 32B * Reasoning Model: Yes ### Training Details * Sequence Length: 8192 * Epochs: 1 epoch training (Inherited from RPMax methods) * Fine-tuning Method: RS-QLORA+ (Rank-Stabilized LoRA + LoRA Plus 8x) * Rank/Alpha: 128-rank 128-alpha * Learning Rate: 0.00001 * Scheduler: Rex * Gradient accumulation: 32 ### Very Nice Training graphs :) <img src="https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/gBGmhMB0kgoJTmxs-fvtk.png" alt="Train Loss" width="600"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/DtdMtuoA4bX8mKmxOSY10.png" alt="Eval Loss" width="600"> ### Quantization * BF16: https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v3 * GGUF: https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v3-GGUF ### How to use reasoning models correctly in ST <img src="https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/njVt2Vir8Isd3ApjTBmoI.png" alt="RpR ST Settings" width="600"> For any reasoning models in general, you need to make sure to set: * Prefix is set to ONLY \<think> and the suffix is set to ONLY \</think> without any spaces or newlines (enter) * Reply starts with \<think> * Always add character names is unchecked * Include names is set to never * As always the chat template should also conform to the model being used Note: Reasoning models work properly only if include names is set to never, since they always expect the eos token of the user turn followed by the \<think> token in order to start reasoning before outputting their response. If you set include names to enabled, then it will always append the character name at the end like "Seraphina:\<eos_token>" which confuses the model on whether it should respond or reason first. The rest of your sampler parameters can be set as you wish as usual. If you don't see the reasoning wrapped inside the thinking block, then either your settings is still wrong and doesn't follow my example or that your ST version is too old without reasoning block auto parsing. If you see the whole response is in the reasoning block, then your \<think> and \</think> reasoning token suffix and prefix might have an extra space or newline. Or the model just isn't a reasoning model that is smart enough to always put reasoning in between those tokens. ### If you set everything up correctly, it should look like this: <img src="https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/IDs6FooZgVTIBNHFHZUZB.png" alt="RpR example response" width="600"> --- <details> <summary>Details: The RPMax Foundation (Dataset & Training Philosophy)</summary> The following sections detail the core philosophy behind the dataset and training methodology originally developed for RPMax, which serves as the foundation for the RpR series. ### The Goal: Reduced Repetition and Higher Creativity The goal of the dataset curation used for both RPMax and RpR is to reduce repetitions and increase the models ability to creatively write in different situations presented to it. What this means is it is a model that will output responses very differently without falling into predictable tropes across different situations. ### What is repetition and creativity? First of all, creativity should mean the variety in output that the model is capable of creating. You should not confuse creativity with writing prose. When a model writes in a way that can be said to be pleasant like writers would write in a novel, this is not creative writing. This is just a model having a certain pleasant type of writing prose. So a model that writes nicely is not necessarily a creative model. Repetition and creativity are essentially intertwined with each other, so if a model is repetitive then a model can also be said to be un-creative as it cannot write new things and can only repeat similar responses that it has created before. For repetition there are actually two very different forms of repetition. In-context repetition: When people mention a model is repetitive, this usually mean a model that likes to repeat the same phrases in a single conversation. An example of this is when a model says that a character "flicks her hair and...." and then starts to prepend that "flicks her hair and..." into every other action that character does. It can be said that the model is boring, but even in real people's writing it is possible that this kind of repetition could be intentional to subtly prove a point or showcase a character's traits in some scenarios. So this type of repetition is not always bad and completely discouraging a model from doing this does not always lead to improve a model's writing ability. In this regard, RPMax and RpR is not yet focused on eliminating this type of repetition so there might be some in-context repetition that can be seen in the outputs. Eliminating this will be the next big step of the RPMax and RpR series of models. Cross-context repetition: A second worse type of repetition is a model's tendency to repeat the same phrases or tropes in very different situations. An example is a model that likes to repeat the infamous "shivers down my spine" phrase in wildly different conversations that don't necessarily fit with that phrase. This type of repetition is ALWAYS bad as it is a sign that the model has over-fitted into that style of "creative writing" that it has often seen in the training dataset. A model's tendency to have cross-context repetition is also usually visible in how a model likes to choose similar repetitive names when writing stories. Such as the infamous "elara" and "whispering woods" names. The primary goal of the dataset curation for RPMax and RpR is to create a highly creative model by reducing cross-context repetition, as that is the type of repetition that follows you through different conversations. This is combated by making sure the dataset does not have repetitions of the same situations or characters in different example entries. ### Dataset Curation The success of models trained on this dataset (including RPMax and now RpR) is thanks to the training method and the unique dataset created for fine-tuning. It contains as many open source creative writing and RP datasets that can be found (all from Hugging Face), from which have been curated to weed out datasets that are purely synthetic generations as they often only serve to dumb down the model and make the model learn GPT-isms (slop) rather than help. Then Llama 3.1 8B (or a similarly capable model) is used to create a database of the characters and situations that are portrayed in these datasets, which is then used to de-dupe these datasets to make sure that there is only a single entry of any character or situation. ### The Golden Rule of Fine-Tuning Unlike the initial pre-training stage where the more data you throw at it the better it becomes for the most part, the golden rule for fine-tuning models isn't quantity, but instead quality over quantity. So the dataset used here is actually orders of magnitude smaller than it would be if it included repeated characters and situations in the dataset, but the end result is a model that does not feel like just another "in-breed" of another creative writing/RP model. ### Training Parameters and Unconventional Approach The usual way is to have a low learning rate and high gradient accumulation for better loss stability, and then run multiple epochs of the training run until the loss is acceptable. The RPMax and RpR methodology, however, uses only one single epoch, a low gradient accumulation, and a higher than normal learning rate. The loss curve during training is actually unstable and jumps up and down a lot, but if it is smoothed out, it is steadily decreasing over time. The theory is that this allows the models to learn from each individual example in the dataset much more, and by not showing the model the same example twice using multiple epochs, it stops the model from latching on and reinforcing a single character or story trope. The jumping up and down of loss during training is because as the model gets trained on a new entry from the dataset, the model will have never seen a similar example before and therefore can't really predict an answer similar to the example entry. While the relatively high end loss of 1.0 or slightly above is actually acceptable because the goal was never to create a model that can output exactly like the dataset that is being used to train it. Rather to create a model that is creative enough to make up it's own style of responses. This is different from training a model in a particular domain and needing the model to reliably be able to output like the example dataset, such as when training a model on a company's internal knowledge base. </details> --- ## Try It Out! Model preference is subjective, so please do try QwQ-32B-ArliAI-RpR-v3 for yourself. Your feedback both good and bad is always valueable and will help us improve the future RPMax and RpR models.
mradermacher/lwd-Mirau-RP-14B-i1-GGUF	mradermacher	2025-04-28T10:40:30Z	921	2	transformers	[ "transformers", "gguf", "qwen", "qwen2", "rp", "roleplay", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:AetherArchitectural/lwd-Mirau-RP-14B", "base_model:quantized:AetherArchitectural/lwd-Mirau-RP-14B", "license:unknown", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-10T10:49:17Z	--- base_model: AetherArchitectural/lwd-Mirau-RP-14B language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: unknown quantized_by: mradermacher tags: - qwen - qwen2 - rp - roleplay --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/AetherArchitectural/lwd-Mirau-RP-14B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-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/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/lwd-Mirau-RP-14B-i1-GGUF/resolve/main/lwd-Mirau-RP-14B.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
scott12355/Qwen-SophiaAI-Finetune-7B	scott12355	2025-04-28T10:40:14Z	62	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "dataset:Amod/mental_health_counseling_conversations", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:quantized:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2025-03-28T10:33:02Z	--- library_name: transformers datasets: - Amod/mental_health_counseling_conversations base_model: - Qwen/Qwen2.5-7B-Instruct --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details Qwen 2.5-7B-Instruct, fine-tuned on an extensive mental health counselling dataset, part of my final year university project. This advanced model is designed to provide nuanced and empathetic responses, reflecting a deep understanding of mental health issues. ### 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]

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