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train · 839k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-24 12:28:46	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 493 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-24 12:27:57	card stringlengths 11 1.01M
noneUsername/QwQ-32B-abliterated-AWQ-INT4	noneUsername	2025-03-08T06:53:11Z	0	0	null	[ "safetensors", "qwen2", "base_model:huihui-ai/QwQ-32B-abliterated", "base_model:quantized:huihui-ai/QwQ-32B-abliterated", "4-bit", "awq", "region:us" ]	null	2025-03-08T06:42:33Z	--- base_model: - huihui-ai/QwQ-32B-abliterated --- vllm (pretrained=/root/autodl-tmp/QwQ-32B,add_bos_token=true,max_model_len=4096,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.432\|± \|0.0314\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.744\|± \|0.0277\| vllm (pretrained=/root/autodl-tmp/QwQ-32B,add_bos_token=true,max_model_len=4096,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.444\|± \|0.0222\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.716\|± \|0.0202\| \| Groups \|Version\|Filter\|n-shot\|Metric\| \|Value \| \|Stderr\| \|------------------\|------:\|------\|------\|------\|---\|-----:\|---\|-----:\| \|mmlu \| 2\|none \| \|acc \|↑ \|0.8140\|± \|0.0125\| \| - humanities \| 2\|none \| \|acc \|↑ \|0.8359\|± \|0.0251\| \| - other \| 2\|none \| \|acc \|↑ \|0.8103\|± \|0.0269\| \| - social sciences\| 2\|none \| \|acc \|↑ \|0.8889\|± \|0.0222\| \| - stem \| 2\|none \| \|acc \|↑ \|0.7544\|± \|0.0238\| vllm (pretrained=/root/autodl-tmp/QwQ-32B-abliterated,add_bos_token=true,max_model_len=4096,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.528\|± \|0.0316\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.740\|± \|0.0278\| vllm (pretrained=/root/autodl-tmp/QwQ-32B-abliterated,add_bos_token=true,max_model_len=4096,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.492\|± \|0.0224\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.742\|± \|0.0196\| vllm (pretrained=/root/autodl-tmp/QwQ-32B-abliterated,add_bos_token=true,max_model_len=4096,dtype=bfloat16,max_num_seqs=3), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: 1 \| Groups \|Version\|Filter\|n-shot\|Metric\| \|Value \| \|Stderr\| \|------------------\|------:\|------\|------\|------\|---\|-----:\|---\|-----:\| \|mmlu \| 2\|none \| \|acc \|↑ \|0.8152\|± \|0.0126\| \| - humanities \| 2\|none \| \|acc \|↑ \|0.8359\|± \|0.0253\| \| - other \| 2\|none \| \|acc \|↑ \|0.8000\|± \|0.0276\| \| - social sciences\| 2\|none \| \|acc \|↑ \|0.8722\|± \|0.0240\| \| - stem \| 2\|none \| \|acc \|↑ \|0.7754\|± \|0.0232\| vllm (pretrained=/root/autodl-tmp/QwQ-32B-abliterated-awq,add_bos_token=true,max_model_len=4096,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.476\|± \|0.0316\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.752\|± \|0.0274\| vllm (pretrained=/root/autodl-tmp/QwQ-32B-abliterated-awq,add_bos_token=true,max_model_len=4096,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto \|Tasks\|Version\| Filter \|n-shot\| Metric \| \|Value\| \|Stderr\| \|-----\|------:\|----------------\|-----:\|-----------\|---\|----:\|---\|-----:\| \|gsm8k\| 3\|flexible-extract\| 5\|exact_match\|↑ \|0.524\|± \|0.0224\| \| \| \|strict-match \| 5\|exact_match\|↑ \|0.716\|± \|0.0202\| \| Groups \|Version\|Filter\|n-shot\|Metric\| \|Value \| \|Stderr\| \|------------------\|------:\|------\|------\|------\|---\|-----:\|---\|-----:\| \|mmlu \| 2\|none \| \|acc \|↑ \|0.8023\|± \|0.0130\| \| - humanities \| 2\|none \| \|acc \|↑ \|0.8000\|± \|0.0266\| \| - other \| 2\|none \| \|acc \|↑ \|0.7949\|± \|0.0284\| \| - social sciences\| 2\|none \| \|acc \|↑ \|0.8500\|± \|0.0258\| \| - stem \| 2\|none \| \|acc \|↑ \|0.7789\|± \|0.0235\|
huangyizhuo/distilbert-base-uncased-finetuned-emotion	huangyizhuo	2025-03-08T06:52:58Z	3	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-02-16T01:46:46Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2146 - Accuracy: 0.9265 - F1: 0.9263 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8268 \| 1.0 \| 250 \| 0.3062 \| 0.9095 \| 0.9086 \| \| 0.2478 \| 2.0 \| 500 \| 0.2146 \| 0.9265 \| 0.9263 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.6.0 - Datasets 3.2.0 - Tokenizers 0.21.0
andaole/ppo-Huggy	andaole	2025-03-08T06:51:49Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-03-08T06:51:36Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: andaole/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF	mradermacher	2025-03-08T06:50:28Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:marcuscedricridia/Hush-Qwen2.5-7B-RP", "base_model:quantized:marcuscedricridia/Hush-Qwen2.5-7B-RP", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-08T02:50:15Z	--- base_model: marcuscedricridia/Hush-Qwen2.5-7B-RP 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/marcuscedricridia/Hush-Qwen2.5-7B-RP <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-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/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hush-Qwen2.5-7B-RP-i1-GGUF/resolve/main/Hush-Qwen2.5-7B-RP.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| 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 -->
Jonjew/TomNulensStyle	Jonjew	2025-03-08T06:48:28Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-03-08T06:48:20Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- By Tom Nulens. A digital illustration shoot from a profile camera angle about a double exposure portrait of a woman with abstract technology elements and text. the image also shows a woman in the middle of the image, who appears to be a young woman with dark hair styled in a bun, and is facing the viewer with her eyes closed. she has a serene expression and is wearing a beige cape that is partially open, revealing her upper body. the background is a gradient of beige and black, with a mix of light and dark tones. on the left side of the woman, there is a text overlay that reads "o.e.t." and on the right side, there are various mathematical equations and symbols that appear to be made up of black and gold elements. the woman's hair is styled in an updo, and her face is adorned with gold and black geometric patterns. the overall effect is a striking digital art piece with a focus on technology and abstract elements. <lora:Tom_Nulens:0.8045440673828125> parameters: negative_prompt: 'Steps: 25 Seed: 36692865603449' output: url: images/_00000_6_.png - text: >- By Tom Nulens. A digitally created artwork in a modern, abstract style. The central focus is a stylized, surreal portrait of a woman's face, divided into two halves. The left half is a realistic depiction of a woman's face with detailed features and a neutral expression, framed by golden, metallic feathers and geometric shapes. The right half is more abstract, featuring a blend of metallic textures, organic forms, and geometric patterns. The woman's right eye is partially obscured by a large, metallic, swirling pattern that resembles a vortex or a galaxy. Her hair is intricately designed, with feathers and metallic elements interwoven, adding to the surreal and futuristic aesthetic. The background is a gradient of deep, dark colors, transitioning from black to a rich, dark red, which enhances the golden and metallic tones of the artwork. The overall composition is symmetrical, with the left and right halves mirroring each other, creating a sense of balance and harmony. The textures and colors are highly detailed and rich, giving the artwork a luxurious and opulent feel. The style is reminiscent of high-end fashion photography or digital art, blending realism with abstract elements. <lora:Tom_Nulens:0.8045440673828125> parameters: negative_prompt: 'Steps: 25 Seed: 680769812145859' output: url: images/_00000_5_.png - text: >- By Tom Nulens. A photo-realistic shoot from a frontal camera angle about a woman with a mysterious expression surrounded by white butterflies. the image also shows a dark, moody atmosphere. on the middle of the image, a woman appears to be in her mid-twenties, with a slim body and dark lipstick. she is facing the viewer, with her eyes looking directly at the viewer. her hair is styled in a way that frames her face, and her hair color is black. her face is covered by multiple white butterflies that are flying around her head, creating an ethereal and surreal atmosphere. the butterflies are of various sizes and colors, adding to the sense of depth and dimensionality in the image. the background is a solid black color, providing a stark contrast to the woman's pale skin. the overall effect is one of beauty and mystery, with the butterflies adding a touch of whimsy and enchantment. <lora:Tom_Nulens:0.8045440673828125> parameters: negative_prompt: 'Steps: 25 Seed: 345956540005846' output: url: images/_00000_10_.png - text: >- By Tom Nulens. A photo-realistic portrait shoot from a frontal camera angle about a stylized portrait of a woman with a unique headpiece adorned with yellow orchids and foliage. the image also shows a striking contrast between the two colors. on the middle of the image, a woman appears to be facing the viewer, with her eyes closed, wearing a sleeveless yellow dress that is cut in half, revealing her bare shoulders and black lips. she has a slim body and a bald head, and her face is painted with a combination of white and orange colors. her hair is adorned with large, yellow flowers and orange flowers, and she is wearing a feathered headpiece. the background is a solid, light green color, and the overall aesthetic is minimalistic with a focus on the woman's face and hair. <lora:Tom_Nulens:0.8045440673828125> parameters: negative_prompt: 'Steps: 25 Seed: 775346785547268' output: url: images/_00000_7_.png - text: >- By Tom Nulens. A digital artwork featuring a stylized portrait of a woman. The background is a muted, gradient gray that gradually fades from light to dark, providing a subtle contrast to the vibrant colors and textures of the subject. The woman's face is partially obscured by a gold, metallic mask that covers her eyes and part of her nose, giving her an enigmatic and mysterious appearance. Her lips are painted a deep red, adding a touch of drama to her expression. Her hair is styled extravagantly, with a large, ornate headdress that combines elements of feathers, sequins, and metallic textures. The feathers are predominantly black, adding a sense of elegance and grandeur. The headdress is intertwined with golden and metallic elements, creating a striking contrast against her pale skin. The woman's shoulders and upper body are adorned with a mix of shimmering gold and black fabrics, with a textured, almost armor-like quality. The gold elements have a reflective surface, adding depth and dimension to the artwork. The overall style of the artwork is contemporary and avant-garde, blending elements of high fashion and digital art to create a visually striking and dynamic composition. <lora:Tom_Nulens:0.8045440673828125> parameters: negative_prompt: 'Steps: 25 Seed: 162846266189991' output: url: images/_00000_12_.png - text: >- By Tom Nulens. A highly stylized CGI digital artwork featuring a woman with a pale, almost translucent complexion. Her hair is platinum blonde, styled in a sleek, straight manner that frames her face. The central focus of the image is her face and upper torso, which are intricately adorned with abstract, metallic gold shapes and fragments that appear to be breaking away from her skin. These gold fragments form letters, numbers, and abstract forms, creating a dynamic and fragmented visual effect. The gold fragments are shiny and reflective, contrasting starkly with her pale skin and the white background. The woman's expression is calm and serene, with her eyes closed and her lips slightly parted, adding to the ethereal and almost otherworldly feel of the image. The background is a smooth, light gray, which helps emphasize the stark contrast between the woman's skin and the gold fragments. The texture of her skin is smooth and delicate, while the gold fragments have a rough, jagged texture, enhancing the sense of fragmentation and disintegration. The overall style of the artwork is highly conceptual and abstract, blending elements of modern art and digital manipulation to create a surreal and visually striking image. <lora:Tom_Nulens:0.8045440673828125> parameters: negative_prompt: 'Steps: 25 Seed: 1124374567933754' output: url: images/_00000_15_.png - text: >- By Tom Nulens. A digitally manipulated photograph of a young woman with a striking, avant-garde style. The woman has platinum blonde hair styled into a voluminous, spiky updo with various splashes of black paint and gold splatters, creating a dramatic, almost abstract effect. Her face is partially obscured by the large, bold number "2" in a metallic gold color, which is overlaid on her forehead and hair, giving a futuristic and modern appearance. She is dressed in a dark, metallic leather jacket with a high collar, adding to the edgy, futuristic aesthetic. The background is a gradient of dark gray tones, which helps to emphasize the gold splatters and the subject's face. The overall style is modern and artistic, blending elements of high fashion and digital manipulation. The texture of the woman's hair is smooth and glossy, contrasting with the rough, splattered paint. The image is highly stylized, with a focus on bold, contrasting colors and textures, creating a visually striking and dynamic composition. The overall mood is modern and edgy, with a strong emphasis on fashion and artistic expression. <lora:Tom_Nulens:0.8045440673828125> parameters: negative_prompt: 'Steps: 25 Seed: 404399648602098' output: url: images/_00000_23_.png - text: >- By Tom Nulens. A digital illustration shoot from a profile camera angle about a striking portrait of a woman with a unique, abstract design. the image features a woman in the middle of the frame, with her face facing the viewer. she appears to be a young adult, with fair skin and long, dark eyelashes. her hair is styled in a mohawk-like fashion, with black feathers framing her face. her eyes are accentuated with dramatic black eyeliner, and her lips are painted with dark lipstick. the background is a beige, textured surface with various pieces of paper and abstract shapes scattered throughout, creating a collage-like effect. the woman's face is the focal point of the image, with the abstract shapes and textures blending together to create a sense of depth and dimension. the overall effect is a striking and captivating piece of art that is both visually striking and eye-catching. <lora:Tom_Nulens_II-000007:0.8045440673828125> parameters: negative_prompt: 'Steps: 25 Seed: 724610367327067' output: url: images/_00000_28_.png - text: >- A digital illustration shoot from a profile camera angle about a striking portrait of a woman with a unique, abstract design. the image features a woman in the middle of the frame, with her face facing the viewer. she appears to be a young adult, with fair skin and long, dark eyelashes. her hair is styled in a mohawk-like fashion, with black feathers framing her face. her eyes are accentuated with dramatic black eyeliner, and her lips are painted with dark lipstick. the background is a beige, textured surface with various pieces of paper and abstract shapes scattered throughout, creating a collage-like effect. the woman's face is the focal point of the image, with the abstract shapes and textures blending together to create a sense of depth and dimension. the overall effect is a striking and captivating piece of art that is both visually striking and eye-catching. <lora:Tom_Nulens:0.8045440673828125> <lora:Steve_McDonald:0.75> <lora:Fluxartis_Photography:0.6> parameters: negative_prompt: 'Steps: 25 Seed: 640717374257156' output: url: images/_00000_36_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: By Tom Nulens license: unknown --- # Tom Nulens <Gallery /> ## Model description FROM https://civitai.com/models/1287601/tom-nulens?modelVersionId=1265556 Trigger By Tom Nulens Strength 0.85 25 - 30 steps cfg 3.5 About this version Inspired by Tom Nulens artwork. Tom Nulens is a Visual Designer and Art Director who leverages generative AI to craft imaginative campaigns and captivating visuals. Combining creativity with cutting-edge technology, Tom’s work blends striking aesthetics, polished details, and engaging storytelling to create impactful designs tailored to modern audiences. Recommended resources : Fluxmania III or Flux1.dev fp8. Settings : dpmpp_2m / sgm_uniform / 25 - 30 steps / cfg 3.5 Weighting : 0.85 ## Trigger words You should use `By Tom Nulens` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/TomNulensStyle/tree/main) them in the Files & versions tab.
linkyfan/Qwen2.5-3b-GPRO	linkyfan	2025-03-08T06:46:47Z	76	1	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "unsloth", "trl", "grpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-03T00:47:06Z	--- library_name: transformers tags: - unsloth - trl - grpo --- # 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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(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]
cavargas10/TRELLIS	cavargas10	2025-03-08T06:45:03Z	0	0	trellis	[ "trellis", "image-to-3d", "en", "arxiv:2412.01506", "license:mit", "region:us" ]	image-to-3d	2025-03-08T06:03:39Z	--- library_name: trellis pipeline_tag: image-to-3d license: mit language: - en --- # TRELLIS Image Large <!-- Provide a quick summary of what the model is/does. --> The image conditioned version of TRELLIS, a large 3D genetive model. It was introduced in the paper [Structured 3D Latents for Scalable and Versatile 3D Generation](https://huggingface.co/papers/2412.01506). Project page: https://trellis3d.github.io/ Code: https://github.com/Microsoft/TRELLIS
YxBxRyXJx/Unsloth_QADS_ORPO_DeepseekQwen_14B_no1	YxBxRyXJx	2025-03-08T06:44:48Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-03-08T06:44:17Z	--- base_model: unsloth/deepseek-r1-distill-qwen-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: YxBxRyXJx - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-qwen-14b-unsloth-bnb-4bit 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)
NC122/Llama-3.2-1B-finetuned	NC122	2025-03-08T06:38:59Z	0	0	null	[ "safetensors", "llama", "trl", "sft", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-03-08T06:00:23Z	--- license: apache-2.0 tags: - trl - sft ---
Sophie-Rain-Spider-man-Leaks-Videos/Sophie.Rain.Spiderman.Videos.Instagram	Sophie-Rain-Spider-man-Leaks-Videos	2025-03-08T06:37:05Z	0	0	null	[ "region:us" ]	null	2025-03-08T06:36:48Z	<p><a href="https://social.danielwellington.com/srain" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a href="https://social.danielwellington.com/srain" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a href="https://social.danielwellington.com/srain" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a></p>
Sophie-Rain-Spiderman-Leak-Videos-Free/Sophie.Rain.SpiderMan.Video.Tutorial	Sophie-Rain-Spiderman-Leak-Videos-Free	2025-03-08T06:36:32Z	0	0	null	[ "region:us" ]	null	2025-03-08T06:36:08Z	There has been a lot of buzz on the internet recently regarding a alleged video scandal involving Sophie Rain and Spider-Man. <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a></p>
mrpks/bert-finetuned-cptindex	mrpks	2025-03-08T06:35:39Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:dmis-lab/biobert-v1.1", "base_model:finetune:dmis-lab/biobert-v1.1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-03-08T06:24:16Z	--- library_name: transformers base_model: dmis-lab/biobert-v1.1 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-cptindex results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-cptindex This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2067 - Precision: 0.7222 - Recall: 0.7879 - F1: 0.7536 - Accuracy: 0.9291 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 25 \| 0.4153 \| 0.75 \| 0.8182 \| 0.7826 \| 0.8333 \| \| No log \| 2.0 \| 50 \| 0.2187 \| 0.6486 \| 0.7273 \| 0.6857 \| 0.9255 \| \| No log \| 3.0 \| 75 \| 0.2067 \| 0.7222 \| 0.7879 \| 0.7536 \| 0.9291 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0
mradermacher/QWEN-Instruct-32B-Token-GGUF	mradermacher	2025-03-08T06:33:24Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Viol2000/QWEN-Instruct-32B-Token", "base_model:quantized:Viol2000/QWEN-Instruct-32B-Token", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T05:12:06Z	--- base_model: Viol2000/QWEN-Instruct-32B-Token language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Viol2000/QWEN-Instruct-32B-Token <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.Q4_K_M.gguf) \| Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/QWEN-Instruct-32B-Token-GGUF/resolve/main/QWEN-Instruct-32B-Token.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/llama-2-7b-monika-v0.3b-GGUF	mradermacher	2025-03-08T06:33:23Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:922-CA/llama-2-7b-monika-v0.3b", "base_model:quantized:922-CA/llama-2-7b-monika-v0.3b", "license:llama2", "endpoints_compatible", "region:us" ]	null	2025-03-08T05:46:02Z	--- base_model: 922-CA/llama-2-7b-monika-v0.3b language: - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/922-CA/llama-2-7b-monika-v0.3b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-7b-monika-v0.3b-GGUF/resolve/main/llama-2-7b-monika-v0.3b.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
ClarenceDan/007022d0-61ef-4fad-bfe4-07af38a01863	ClarenceDan	2025-03-08T06:33:11Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Capybara-7B-V1", "base_model:adapter:NousResearch/Nous-Capybara-7B-V1", "license:mit", "region:us" ]	null	2025-03-08T05:21:23Z	--- library_name: peft license: mit base_model: NousResearch/Nous-Capybara-7B-V1 tags: - axolotl - generated_from_trainer model-index: - name: 007022d0-61ef-4fad-bfe4-07af38a01863 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Nous-Capybara-7B-V1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c11b9ad10f2938a4_train_data.json ds_type: json format: custom path: /workspace/input_data/c11b9ad10f2938a4_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 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: ClarenceDan/007022d0-61ef-4fad-bfe4-07af38a01863 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/c11b9ad10f2938a4_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: 0d088cfb-e5cd-488c-aa02-38d2d25523be wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 0d088cfb-e5cd-488c-aa02-38d2d25523be warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 007022d0-61ef-4fad-bfe4-07af38a01863 This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0003 \| 3 \| nan \| \| 0.0 \| 0.0005 \| 6 \| nan \| \| 0.0 \| 0.0008 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold2	genki10	2025-03-08T06:31:54Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-03-08T06:10:48Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold2 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. --> # ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9735 - Qwk: 0.3200 - Mse: 0.9733 - Rmse: 0.9866 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 6 \| 5.8878 \| 0.0080 \| 5.8881 \| 2.4265 \| \| No log \| 2.0 \| 12 \| 2.9245 \| 0.0 \| 2.9248 \| 1.7102 \| \| No log \| 3.0 \| 18 \| 1.6194 \| 0.0213 \| 1.6198 \| 1.2727 \| \| No log \| 4.0 \| 24 \| 1.7325 \| 0.0345 \| 1.7328 \| 1.3164 \| \| No log \| 5.0 \| 30 \| 1.4855 \| 0.0823 \| 1.4857 \| 1.2189 \| \| No log \| 6.0 \| 36 \| 1.1889 \| 0.1651 \| 1.1887 \| 1.0903 \| \| No log \| 7.0 \| 42 \| 1.1280 \| 0.1929 \| 1.1275 \| 1.0619 \| \| No log \| 8.0 \| 48 \| 0.9689 \| 0.2134 \| 0.9685 \| 0.9841 \| \| No log \| 9.0 \| 54 \| 1.4272 \| 0.1832 \| 1.4264 \| 1.1943 \| \| No log \| 10.0 \| 60 \| 1.2315 \| 0.2411 \| 1.2308 \| 1.1094 \| \| No log \| 11.0 \| 66 \| 1.6767 \| 0.1943 \| 1.6763 \| 1.2947 \| \| No log \| 12.0 \| 72 \| 1.3642 \| 0.1917 \| 1.3640 \| 1.1679 \| \| No log \| 13.0 \| 78 \| 1.5235 \| 0.1944 \| 1.5231 \| 1.2341 \| \| No log \| 14.0 \| 84 \| 2.0727 \| 0.1451 \| 2.0725 \| 1.4396 \| \| No log \| 15.0 \| 90 \| 1.5991 \| 0.2128 \| 1.5988 \| 1.2644 \| \| No log \| 16.0 \| 96 \| 1.6541 \| 0.1702 \| 1.6538 \| 1.2860 \| \| No log \| 17.0 \| 102 \| 1.4618 \| 0.1787 \| 1.4617 \| 1.2090 \| \| No log \| 18.0 \| 108 \| 0.8675 \| 0.3440 \| 0.8670 \| 0.9311 \| \| No log \| 19.0 \| 114 \| 1.1022 \| 0.2873 \| 1.1020 \| 1.0498 \| \| No log \| 20.0 \| 120 \| 2.1904 \| 0.1442 \| 2.1905 \| 1.4800 \| \| No log \| 21.0 \| 126 \| 1.6390 \| 0.1768 \| 1.6390 \| 1.2802 \| \| No log \| 22.0 \| 132 \| 0.9015 \| 0.3012 \| 0.9012 \| 0.9493 \| \| No log \| 23.0 \| 138 \| 1.1640 \| 0.2156 \| 1.1638 \| 1.0788 \| \| No log \| 24.0 \| 144 \| 1.4515 \| 0.1902 \| 1.4514 \| 1.2047 \| \| No log \| 25.0 \| 150 \| 1.6886 \| 0.1810 \| 1.6884 \| 1.2994 \| \| No log \| 26.0 \| 156 \| 0.9759 \| 0.2685 \| 0.9757 \| 0.9878 \| \| No log \| 27.0 \| 162 \| 0.9699 \| 0.3298 \| 0.9696 \| 0.9847 \| \| No log \| 28.0 \| 168 \| 1.1190 \| 0.2820 \| 1.1188 \| 1.0577 \| \| No log \| 29.0 \| 174 \| 1.3450 \| 0.2003 \| 1.3449 \| 1.1597 \| \| No log \| 30.0 \| 180 \| 1.0749 \| 0.2609 \| 1.0746 \| 1.0366 \| \| No log \| 31.0 \| 186 \| 1.0030 \| 0.2746 \| 1.0027 \| 1.0014 \| \| No log \| 32.0 \| 192 \| 1.0923 \| 0.2350 \| 1.0918 \| 1.0449 \| \| No log \| 33.0 \| 198 \| 1.0537 \| 0.2439 \| 1.0535 \| 1.0264 \| \| No log \| 34.0 \| 204 \| 1.1813 \| 0.2531 \| 1.1811 \| 1.0868 \| \| No log \| 35.0 \| 210 \| 0.9487 \| 0.3130 \| 0.9485 \| 0.9739 \| \| No log \| 36.0 \| 216 \| 1.0430 \| 0.2795 \| 1.0427 \| 1.0211 \| \| No log \| 37.0 \| 222 \| 0.9893 \| 0.2980 \| 0.9891 \| 0.9946 \| \| No log \| 38.0 \| 228 \| 0.9735 \| 0.3200 \| 0.9733 \| 0.9866 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
wATCH-Sophie-Rain-Spider-man-leaks-Video/Sophie.Rain.Videos.Link.Short.Clip.Video.Viral.On.Social.Media.X.Twitter	wATCH-Sophie-Rain-Spider-man-leaks-Video	2025-03-08T06:31:37Z	0	0	null	[ "region:us" ]	null	2025-03-08T06:31:14Z	<p><a href="https://social.danielwellington.com/srain" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a href="https://social.danielwellington.com/srain" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a href="https://social.danielwellington.com/srain" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a></p>
wATCH-Sophie-Rain-Spider-Updates-Video/Sophie.Rain.Spider-Man.Video.Tutorial	wATCH-Sophie-Rain-Spider-Updates-Video	2025-03-08T06:30:36Z	0	0	null	[ "region:us" ]	null	2025-03-08T06:26:55Z	Sophie Rain Spider-Man Video Tutorial In recent weeks, a video of Sophie Rain, a little-known social media personality, has gone viral. The video, which is heavily implied to be of an explicit nature, has sparked a significant amount of controversy and debate online. <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a></p>
FearedSlug7/so-vits-svc-Ahoy-Stuart-Brown	FearedSlug7	2025-03-08T06:29:29Z	0	0	null	[ "region:us" ]	null	2025-03-08T06:26:57Z	Here is Ahoy, or Stuart Brown. All samples were taken from his youtube: https://www.youtube.com/@XboxAhoy
Wan-Sheng/Llama-3.2-1B-finetuned	Wan-Sheng	2025-03-08T06:27:05Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-03-08T06:26:04Z	--- library_name: transformers tags: - trl - sft --- # 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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texanrangee/40ffc4c3-0a47-4aa7-9175-c431f61f9e64	texanrangee	2025-03-08T06:25:18Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-08T02:19:26Z	--- 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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Sophie-Rain-Spiderman-New-Video-Fre/Sophie.Rain.Spiderman.Video.Viral	Sophie-Rain-Spiderman-New-Video-Fre	2025-03-08T06:25:00Z	0	0	null	[ "region:us" ]	null	2025-03-08T06:24:45Z	<p><a href="https://social.danielwellington.com/srain" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a href="https://social.danielwellington.com/srain" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a href="https://social.danielwellington.com/srain" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a></p>
lesso09/433049d9-a061-45fc-83f8-db22e048bd39	lesso09	2025-03-08T06:24:51Z	0	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "license:gemma", "region:us" ]	null	2025-03-08T03:13:36Z	--- library_name: peft license: gemma base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 tags: - axolotl - generated_from_trainer model-index: - name: 433049d9-a061-45fc-83f8-db22e048bd39 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: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d5a0ca1fecef3b00_train_data.json ds_type: json format: custom path: /workspace/input_data/d5a0ca1fecef3b00_train_data.json type: field_input: privacy_mask field_instruction: masked_text field_output: unmasked_text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso09/433049d9-a061-45fc-83f8-db22e048bd39 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000209 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/d5a0ca1fecef3b00_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 90 sequence_len: 1024 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: 426e9c82-b359-492a-b373-0f196a20e3b0 wandb_project: 09a wandb_run: your_name wandb_runid: 426e9c82-b359-492a-b373-0f196a20e3b0 warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 433049d9-a061-45fc-83f8-db22e048bd39 This model is a fine-tuned version of [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0008 ## 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: 0.000209 - train_batch_size: 4 - eval_batch_size: 4 - seed: 90 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 0.5465 \| \| 0.0009 \| 0.0809 \| 500 \| 0.0008 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Columbidae/Qwen-27B-Pruned-Retrained	Columbidae	2025-03-08T06:22:46Z	12	0	null	[ "safetensors", "qwen2", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-32B-Instruct", "region:us" ]	null	2025-02-18T03:16:36Z	--- base_model: - Qwen/Qwen2.5-32B-Instruct --- # Pruned Qwen (Epoch 1) This is [ToastyPigeon/qwen2.5-32b-unnamed-test-model](https://huggingface.co/ToastyPigeon/qwen2.5-32b-unnamed-test-model) pruned down from 32b -> 27b. Using [PruneMe](https://github.com/arcee-ai/PruneMe) to find layers to remove resulted in the removal of layers `[25, 29)` and `[36, 43)` for a reduction from 64 -> 52 layers. Trained on 1 epoch of mixed data from the datasets that went into the pre-pruned model (I'll document that later), totaling about ~10M tokens so far of retraining. Coherent but a little dumb. Likely needs more than 10M tokens of retraining to re-align the layers.
NC122/couplet-json	NC122	2025-03-08T06:22:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-03-08T06:22:28Z	--- license: apache-2.0 ---
lesso07/f7c2c1a2-4569-4418-8a9c-89a07eba80ab	lesso07	2025-03-08T06:21:33Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:deepseek-ai/deepseek-coder-6.7b-instruct", "base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct", "license:other", "region:us" ]	null	2025-03-08T03:56:44Z	--- library_name: peft license: other base_model: deepseek-ai/deepseek-coder-6.7b-instruct tags: - axolotl - generated_from_trainer model-index: - name: f7c2c1a2-4569-4418-8a9c-89a07eba80ab 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: deepseek-ai/deepseek-coder-6.7b-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - a66c6e85b5025d0e_train_data.json ds_type: json format: custom path: /workspace/input_data/a66c6e85b5025d0e_train_data.json type: field_input: text field_instruction: title_main field_output: html format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso07/f7c2c1a2-4569-4418-8a9c-89a07eba80ab hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000207 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/a66c6e85b5025d0e_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 70 sequence_len: 1024 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: 712a841b-9a3a-4dfc-8c94-c6ef8d4f9f1e wandb_project: 07a wandb_run: your_name wandb_runid: 712a841b-9a3a-4dfc-8c94-c6ef8d4f9f1e warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f7c2c1a2-4569-4418-8a9c-89a07eba80ab This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0509 ## 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: 0.000207 - train_batch_size: 4 - eval_batch_size: 4 - seed: 70 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 0.4302 \| \| 0.0524 \| 0.1042 \| 500 \| 0.0509 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
wATCH-Sophie-Rain-Spiderman-Free-New-Video/Sophie.Rain.Spider-Man.Video.Tutorial	wATCH-Sophie-Rain-Spiderman-Free-New-Video	2025-03-08T06:21:18Z	0	0	null	[ "region:us" ]	null	2025-03-08T06:20:41Z	Sophie Rain SpiderMan Video Tutorial <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a></p>
lesso15/1c7a6067-30ea-4d89-8411-981ee8038bfb	lesso15	2025-03-08T06:21:01Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:deepseek-ai/deepseek-coder-6.7b-instruct", "base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct", "license:other", "region:us" ]	null	2025-03-08T03:56:59Z	--- library_name: peft license: other base_model: deepseek-ai/deepseek-coder-6.7b-instruct tags: - axolotl - generated_from_trainer model-index: - name: 1c7a6067-30ea-4d89-8411-981ee8038bfb 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: deepseek-ai/deepseek-coder-6.7b-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - a66c6e85b5025d0e_train_data.json ds_type: json format: custom path: /workspace/input_data/a66c6e85b5025d0e_train_data.json type: field_input: text field_instruction: title_main field_output: html format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso15/1c7a6067-30ea-4d89-8411-981ee8038bfb hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000215 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/a66c6e85b5025d0e_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 150 sequence_len: 1024 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: 712a841b-9a3a-4dfc-8c94-c6ef8d4f9f1e wandb_project: 15a wandb_run: your_name wandb_runid: 712a841b-9a3a-4dfc-8c94-c6ef8d4f9f1e warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 1c7a6067-30ea-4d89-8411-981ee8038bfb This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0512 ## 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: 0.000215 - train_batch_size: 4 - eval_batch_size: 4 - seed: 150 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 0.4299 \| \| 0.0527 \| 0.1042 \| 500 \| 0.0512 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
davidwu1991/gemma-2-2B-it-thinking-function_calling-V0	davidwu1991	2025-03-08T06:19:57Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "endpoints_compatible", "region:us" ]	null	2025-03-08T06:17:03Z	--- base_model: google/gemma-2-2b-it library_name: transformers model_name: gemma-2-2B-it-thinking-function_calling-V0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-2-2B-it-thinking-function_calling-V0 This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it). 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="davidwu1991/gemma-2-2B-it-thinking-function_calling-V0", 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 SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.47.1 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ProperKetoCapsules779/ProperKetoCapsules	ProperKetoCapsules779	2025-03-08T06:18:14Z	0	0	null	[ "region:us" ]	null	2025-03-08T06:15:55Z	Proper Keto Capsules France : Les capsules Proper Keto sont un complément alimentaire de pointe conçu pour soutenir l'état naturel de cétose de votre corps. Fabriquées à partir d’un mélange d’ingrédients de haute qualité et scientifiquement prouvés, ces capsules sont conçues pour vous aider à atteindre et à maintenir les nombreux avantages d’un mode de vie cétogène. ## [Cliquez ici pour commander sur le site officiel de Proper](https://properketocapsules.fr) Vous souhaitez essayer les capsules Proper Keto mais vous ne savez pas si elles sont la meilleure option pour vous ? Pour bien comprendre ce que ce supplément offre, il est important de l’examiner attentivement avant de prendre une décision. 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jkaunert/smolvlm-instruct-spider-classifier	jkaunert	2025-03-08T06:15:05Z	0	0	transformers	[ "transformers", "safetensors", "idefics3", "image-text-to-text", "conversational", "en", "dataset:jkaunert/spider_dataset_dorsal_view", "dataset:jkaunert/spider_dataset_egg_sacs", "dataset:jkaunert/spider_dataset_eyes_visible", "dataset:jkaunert/spider_dataset_female", "dataset:jkaunert/spider_dataset_gravid", "dataset:jkaunert/spider_dataset_in_retreat", "dataset:jkaunert/spider_dataset_lateral_view", "dataset:jkaunert/spider_dataset_male", "dataset:jkaunert/spider_dataset_penultimate", "dataset:jkaunert/spider_dataset_spiderlings", "dataset:jkaunert/spider_dataset_web_present", "dataset:jkaunert/spider_dataset_with_prey", "base_model:HuggingFaceTB/SmolLM2-1.7B-Instruct", "base_model:finetune:HuggingFaceTB/SmolLM2-1.7B-Instruct", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-03-08T05:29:26Z	--- library_name: transformers datasets: - jkaunert/spider_dataset_dorsal_view - jkaunert/spider_dataset_egg_sacs - jkaunert/spider_dataset_eyes_visible - jkaunert/spider_dataset_female - jkaunert/spider_dataset_gravid - jkaunert/spider_dataset_in_retreat - jkaunert/spider_dataset_lateral_view - jkaunert/spider_dataset_male - jkaunert/spider_dataset_penultimate - jkaunert/spider_dataset_spiderlings - jkaunert/spider_dataset_web_present - jkaunert/spider_dataset_with_prey language: - en base_model: - HuggingFaceTB/SmolLM2-1.7B-Instruct --- # SmolVLM-Instruct-Spider-Classifier SmolVLM-Instruct-Spider-Classifier is a fine-tuned version of SmolVLM that accepts arbitrary sequences of image and text inputs to produce text outputs. SmolVLM-Instruct-Spider-Classifier is designed to analyze spiders in images, and attempt to determine their family, genus and/or species. Its lightweight architecture makes it suitable for on-device applications while maintaining strong performance. ## Model Details ## Model Summary - Developed by: Joshua Kaunert - Model type: Multi-modal model (image+text) - Language(s) (NLP): English - License: Apache 2.0 - Architecture: Based on [Idefics3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (see technical summary) ## Resources - Demo: [SmolVLM-Instruct-Spider-Classifier Demo]() - Blog: [Blog post]() ## Uses SmolVLM-Instruct-Spider-Classifier is fine-tuned to be used for inference of spider taxonomy on multimodal (image + text) tasks where the input comprises text queries along with one or more images of spiders. The model does not support image generation. ### Technical Summary SmolVLM-Instruct-Spider-Classifier is a fine-tuned version of SmolVLM, which leverages the lightweight SmolLM2 language model to provide a compact yet powerful multimodal experience. It introduces several changes compared to previous Idefics models: - Image compression: Introduces a more radical image compression compared to Idefics3 to enable the model to infer faster and use less RAM. - Visual Token Encoding: SmolVLM uses 81 visual tokens to encode image patches of size 384×384. Larger images are divided into patches, each encoded separately, enhancing efficiency without compromising performance. ### How to get started You can use transformers to load and infer SmolVLM-Instruct-Spider-Classifier. ```python import torch from PIL import Image from transformers import AutoProcessor, AutoModelForVision2Seq from transformers.image_utils import load_image DEVICE = "cuda" if torch.cuda.is_available() else "cpu" # Load images image1 = load_image("https://citybugs.tamu.edu/wp-content/uploads/sites/3/2014/07/IMG_7929_sm.jpg") image2 = load_image("https://southcoastbotanicgarden.org/wp-content/uploads/2022/11/Spiders-insects.jpg") # Initialize processor and model processor = AutoProcessor.from_pretrained("jkaunert/SmolVLM-Instruct-Spider-Classifier") model = AutoModelForVision2Seq.from_pretrained( "jkaunert/SmolVLM-Instruct-Spider-Classifier", torch_dtype=torch.bfloat16, _attn_implementation="flash_attention_2" if DEVICE == "cuda" else "eager", ).to(DEVICE) # Create input messages messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "image"}, {"type": "text", "text": "What type of spiders are in the two images?"} ] }, ] # Prepare inputs prompt = processor.apply_chat_template(messages, add_generation_prompt=True) inputs = processor(text=prompt, images=[image1, image2], return_tensors="pt") inputs = inputs.to(DEVICE) # Generate outputs generated_ids = model.generate(inputs, max_new_tokens=500) generated_texts = processor.batch_decode( generated_ids, skip_special_tokens=True, ) print(generated_texts[0]) """ Wolf Spider Black and Yellow Garden Spider """ ``` ### Model optimizations Precision: For better performance, load and run the model in half-precision (`torch.float16` or `torch.bfloat16`) if your hardware supports it. ```python from transformers import AutoModelForVision2Seq import torch model = AutoModelForVision2Seq.from_pretrained( "jkaunert/SmolVLM-Instruct-Spider-Classifier", torch_dtype=torch.bfloat16 ).to("cuda") ``` You can also load SmolVLM-Instruct-Spider-Classifier with 4/8-bit quantization using bitsandbytes, torchao or Quanto. Refer to [this page](https://huggingface.co/docs/transformers/en/main_classes/quantization) for other options. ```python from transformers import AutoModelForVision2Seq, BitsAndBytesConfig import torch quantization_config = BitsAndBytesConfig(load_in_8bit=True) model = AutoModelForVision2Seq.from_pretrained( "jkaunert/SmolVLM-Instruct-Spider-Classifier", quantization_config=quantization_config, ) ``` Vision Encoder Efficiency*: Adjust the image resolution by setting `size={"longest_edge": N384}` when initializing the processor, where N is your desired value. The default `N=4` works well, which results in input images of size 1536×1536. For documents, `N=5` might be beneficial. Decreasing N can save GPU memory and is appropriate for lower-resolution images. This is also useful if you want to fine-tune on videos.
wATCH-Sophie-Rain-SpiderMan-New-VideoFree/Sophie.Rain.Spider-Man.Video.Tutorial	wATCH-Sophie-Rain-SpiderMan-New-VideoFree	2025-03-08T06:14:52Z	0	0	null	[ "region:us" ]	null	2025-03-08T06:13:38Z	Sophie Rain Spiderman Video Tutorial <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a href="https://link.rmg.co.uk/nude?updates" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a></p>
jiatsol/First_llm	jiatsol	2025-03-08T06:14:23Z	0	0	null	[ "safetensors", "llama", "trl", "sft", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-03-08T01:23:03Z	--- license: apache-2.0 tags: - trl - sft ---
YxBxRyXJx/Unsloth_QADS_ORPO_Qwen_14B_no1	YxBxRyXJx	2025-03-08T06:14:21Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-03-08T06:14:10Z	--- base_model: unsloth/qwen2.5-14b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: YxBxRyXJx - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-14b-instruct-bnb-4bit 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)
ChrisWeiWei/FirstModel	ChrisWeiWei	2025-03-08T06:13:53Z	0	0	null	[ "safetensors", "llama", "trl", "sft", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-03-08T01:23:05Z	--- license: apache-2.0 tags: - trl - sft ---
kmugglet/landerv2	kmugglet	2025-03-08T06:13:30Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-03-08T06:13:09Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 234.46 +/- 21.12 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
kaizen9/falcon3-1B-fp	kaizen9	2025-03-08T06:13:14Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-08T05:28:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Alphatao/83085f87-a61b-4e55-9e3a-5c94617602a4	Alphatao	2025-03-08T06:12:38Z	0	0	peft	[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-3-mini-128k-instruct", "base_model:adapter:microsoft/Phi-3-mini-128k-instruct", "license:mit", "region:us" ]	null	2025-03-08T00:47:29Z	--- library_name: peft license: mit base_model: microsoft/Phi-3-mini-128k-instruct tags: - axolotl - generated_from_trainer model-index: - name: 83085f87-a61b-4e55-9e3a-5c94617602a4 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: microsoft/Phi-3-mini-128k-instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1fed254576df142d_train_data.json ds_type: json format: custom path: /workspace/input_data/1fed254576df142d_train_data.json type: field_instruction: question field_output: answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null device_map: ? '' : 0,1,2,3,4,5,6,7 early_stopping_patience: 2 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null flash_attention: false gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: false hub_model_id: Alphatao/83085f87-a61b-4e55-9e3a-5c94617602a4 hub_repo: null hub_strategy: null hub_token: null learning_rate: 0.0002 load_best_model_at_end: true load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.3 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 1734 micro_batch_size: 4 mlflow_experiment_name: /tmp/1fed254576df142d_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 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 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.04 wandb_entity: null wandb_mode: online wandb_name: 9090c43e-ec93-46ec-8b87-d11083a1aa8d wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 9090c43e-ec93-46ec-8b87-d11083a1aa8d warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 83085f87-a61b-4e55-9e3a-5c94617602a4 This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1721 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - 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: 1734 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.0314 \| 0.0007 \| 1 \| 0.2233 \| \| 1.4041 \| 0.0682 \| 100 \| 0.1848 \| \| 1.4015 \| 0.1363 \| 200 \| 0.1847 \| \| 1.3008 \| 0.2045 \| 300 \| 0.1823 \| \| 1.4179 \| 0.2727 \| 400 \| 0.1815 \| \| 1.2804 \| 0.3408 \| 500 \| 0.1807 \| \| 1.3661 \| 0.4090 \| 600 \| 0.1787 \| \| 1.7189 \| 0.4772 \| 700 \| 0.1777 \| \| 1.2251 \| 0.5453 \| 800 \| 0.1764 \| \| 1.5796 \| 0.6135 \| 900 \| 0.1748 \| \| 1.5114 \| 0.6817 \| 1000 \| 0.1735 \| \| 1.4334 \| 0.7498 \| 1100 \| 0.1725 \| \| 1.2593 \| 0.8180 \| 1200 \| 0.1713 \| \| 1.14 \| 0.8862 \| 1300 \| 0.1704 \| \| 1.2217 \| 0.9543 \| 1400 \| 0.1698 \| \| 0.9364 \| 1.0225 \| 1500 \| 0.1717 \| \| 1.0701 \| 1.0907 \| 1600 \| 0.1721 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
xuxinyao123/r1-Distill-Qwen32B-JobDescription-LoRA	xuxinyao123	2025-03-08T06:12:20Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-03-08T06:12:01Z	--- base_model: unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: xuxinyao123 - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit 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)
xuxinyao123/r1-Distill-Qwen32B-JobDescription	xuxinyao123	2025-03-08T06:11:01Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-03-08T05:47:25Z	--- base_model: unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: xuxinyao123 - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit 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)
azureWoo/HW1	azureWoo	2025-03-08T06:10:55Z	0	0	null	[ "safetensors", "llama", "trl", "sft", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-03-08T01:23:03Z	--- license: apache-2.0 tags: - trl - sft ---
genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold1	genki10	2025-03-08T06:10:40Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-03-08T05:28:37Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold1 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. --> # ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold1 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8327 - Qwk: 0.3976 - Mse: 0.8323 - Rmse: 0.9123 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 1.0 \| 6 \| 8.5660 \| 0.0 \| 8.5634 \| 2.9263 \| \| No log \| 2.0 \| 12 \| 4.3556 \| 0.0 \| 4.3536 \| 2.0865 \| \| No log \| 3.0 \| 18 \| 2.3360 \| -0.0257 \| 2.3343 \| 1.5278 \| \| No log \| 4.0 \| 24 \| 1.3149 \| 0.0 \| 1.3134 \| 1.1461 \| \| No log \| 5.0 \| 30 \| 1.5626 \| 0.1351 \| 1.5613 \| 1.2495 \| \| No log \| 6.0 \| 36 \| 1.2271 \| 0.1048 \| 1.2258 \| 1.1072 \| \| No log \| 7.0 \| 42 \| 0.9125 \| 0.2193 \| 0.9114 \| 0.9547 \| \| No log \| 8.0 \| 48 \| 0.9870 \| 0.2990 \| 0.9861 \| 0.9930 \| \| No log \| 9.0 \| 54 \| 1.2005 \| 0.2640 \| 1.1997 \| 1.0953 \| \| No log \| 10.0 \| 60 \| 1.8297 \| 0.1706 \| 1.8292 \| 1.3525 \| \| No log \| 11.0 \| 66 \| 1.4883 \| 0.2663 \| 1.4881 \| 1.2199 \| \| No log \| 12.0 \| 72 \| 1.3868 \| 0.2591 \| 1.3865 \| 1.1775 \| \| No log \| 13.0 \| 78 \| 1.3561 \| 0.2743 \| 1.3559 \| 1.1644 \| \| No log \| 14.0 \| 84 \| 0.9493 \| 0.3647 \| 0.9491 \| 0.9742 \| \| No log \| 15.0 \| 90 \| 0.9508 \| 0.4053 \| 0.9507 \| 0.9750 \| \| No log \| 16.0 \| 96 \| 1.0064 \| 0.3290 \| 1.0062 \| 1.0031 \| \| No log \| 17.0 \| 102 \| 1.3650 \| 0.2374 \| 1.3646 \| 1.1682 \| \| No log \| 18.0 \| 108 \| 1.2152 \| 0.2955 \| 1.2148 \| 1.1022 \| \| No log \| 19.0 \| 114 \| 0.9137 \| 0.4110 \| 0.9134 \| 0.9557 \| \| No log \| 20.0 \| 120 \| 0.7971 \| 0.4204 \| 0.7968 \| 0.8926 \| \| No log \| 21.0 \| 126 \| 0.8015 \| 0.4281 \| 0.8013 \| 0.8952 \| \| No log \| 22.0 \| 132 \| 1.1552 \| 0.2907 \| 1.1547 \| 1.0746 \| \| No log \| 23.0 \| 138 \| 1.3357 \| 0.2812 \| 1.3351 \| 1.1554 \| \| No log \| 24.0 \| 144 \| 0.8835 \| 0.3462 \| 0.8830 \| 0.9397 \| \| No log \| 25.0 \| 150 \| 0.8720 \| 0.3616 \| 0.8716 \| 0.9336 \| \| No log \| 26.0 \| 156 \| 1.0973 \| 0.3180 \| 1.0969 \| 1.0473 \| \| No log \| 27.0 \| 162 \| 0.8798 \| 0.3740 \| 0.8796 \| 0.9378 \| \| No log \| 28.0 \| 168 \| 0.9333 \| 0.2919 \| 0.9330 \| 0.9659 \| \| No log \| 29.0 \| 174 \| 0.9176 \| 0.3745 \| 0.9173 \| 0.9578 \| \| No log \| 30.0 \| 180 \| 0.7844 \| 0.3962 \| 0.7840 \| 0.8855 \| \| No log \| 31.0 \| 186 \| 0.8459 \| 0.3755 \| 0.8455 \| 0.9195 \| \| No log \| 32.0 \| 192 \| 0.7618 \| 0.4214 \| 0.7614 \| 0.8726 \| \| No log \| 33.0 \| 198 \| 0.8272 \| 0.4329 \| 0.8269 \| 0.9093 \| \| No log \| 34.0 \| 204 \| 0.7425 \| 0.4277 \| 0.7421 \| 0.8615 \| \| No log \| 35.0 \| 210 \| 0.7926 \| 0.4160 \| 0.7923 \| 0.8901 \| \| No log \| 36.0 \| 216 \| 0.7006 \| 0.4421 \| 0.7003 \| 0.8368 \| \| No log \| 37.0 \| 222 \| 0.9709 \| 0.3262 \| 0.9703 \| 0.9851 \| \| No log \| 38.0 \| 228 \| 0.7722 \| 0.4426 \| 0.7718 \| 0.8785 \| \| No log \| 39.0 \| 234 \| 0.8496 \| 0.3538 \| 0.8493 \| 0.9215 \| \| No log \| 40.0 \| 240 \| 0.8142 \| 0.3669 \| 0.8139 \| 0.9021 \| \| No log \| 41.0 \| 246 \| 0.7794 \| 0.4173 \| 0.7791 \| 0.8826 \| \| No log \| 42.0 \| 252 \| 0.7848 \| 0.4010 \| 0.7844 \| 0.8856 \| \| No log \| 43.0 \| 258 \| 0.8955 \| 0.3481 \| 0.8951 \| 0.9461 \| \| No log \| 44.0 \| 264 \| 0.8009 \| 0.3883 \| 0.8005 \| 0.8947 \| \| No log \| 45.0 \| 270 \| 0.8590 \| 0.4061 \| 0.8586 \| 0.9266 \| \| No log \| 46.0 \| 276 \| 0.8759 \| 0.3669 \| 0.8754 \| 0.9356 \| \| No log \| 47.0 \| 282 \| 0.8940 \| 0.3971 \| 0.8936 \| 0.9453 \| \| No log \| 48.0 \| 288 \| 0.7105 \| 0.4454 \| 0.7101 \| 0.8427 \| \| No log \| 49.0 \| 294 \| 0.7844 \| 0.4154 \| 0.7840 \| 0.8855 \| \| No log \| 50.0 \| 300 \| 0.7501 \| 0.4294 \| 0.7497 \| 0.8658 \| \| No log \| 51.0 \| 306 \| 0.9443 \| 0.3583 \| 0.9437 \| 0.9714 \| \| No log \| 52.0 \| 312 \| 0.8329 \| 0.3818 \| 0.8325 \| 0.9124 \| \| No log \| 53.0 \| 318 \| 0.7643 \| 0.4224 \| 0.7639 \| 0.8740 \| \| No log \| 54.0 \| 324 \| 0.8095 \| 0.3896 \| 0.8092 \| 0.8995 \| \| No log \| 55.0 \| 330 \| 0.7666 \| 0.4208 \| 0.7662 \| 0.8753 \| \| No log \| 56.0 \| 336 \| 0.7739 \| 0.4078 \| 0.7735 \| 0.8795 \| \| No log \| 57.0 \| 342 \| 0.7472 \| 0.4494 \| 0.7468 \| 0.8642 \| \| No log \| 58.0 \| 348 \| 0.7146 \| 0.4491 \| 0.7143 \| 0.8452 \| \| No log \| 59.0 \| 354 \| 0.8430 \| 0.3948 \| 0.8426 \| 0.9179 \| \| No log \| 60.0 \| 360 \| 0.7941 \| 0.3836 \| 0.7937 \| 0.8909 \| \| No log \| 61.0 \| 366 \| 0.7308 \| 0.4265 \| 0.7305 \| 0.8547 \| \| No log \| 62.0 \| 372 \| 0.7799 \| 0.4304 \| 0.7795 \| 0.8829 \| \| No log \| 63.0 \| 378 \| 0.8374 \| 0.3505 \| 0.8370 \| 0.9149 \| \| No log \| 64.0 \| 384 \| 0.7378 \| 0.4154 \| 0.7375 \| 0.8588 \| \| No log \| 65.0 \| 390 \| 0.7359 \| 0.4359 \| 0.7356 \| 0.8577 \| \| No log \| 66.0 \| 396 \| 0.8004 \| 0.4094 \| 0.8000 \| 0.8944 \| \| No log \| 67.0 \| 402 \| 0.7774 \| 0.4164 \| 0.7770 \| 0.8815 \| \| No log \| 68.0 \| 408 \| 0.8016 \| 0.4308 \| 0.8012 \| 0.8951 \| \| No log \| 69.0 \| 414 \| 0.7948 \| 0.4181 \| 0.7943 \| 0.8913 \| \| No log \| 70.0 \| 420 \| 0.7841 \| 0.4272 \| 0.7838 \| 0.8853 \| \| No log \| 71.0 \| 426 \| 0.7494 \| 0.4312 \| 0.7490 \| 0.8655 \| \| No log \| 72.0 \| 432 \| 0.7778 \| 0.4054 \| 0.7774 \| 0.8817 \| \| No log \| 73.0 \| 438 \| 0.8187 \| 0.3944 \| 0.8182 \| 0.9046 \| \| No log \| 74.0 \| 444 \| 0.7657 \| 0.4435 \| 0.7654 \| 0.8748 \| \| No log \| 75.0 \| 450 \| 0.8143 \| 0.4063 \| 0.8139 \| 0.9022 \| \| No log \| 76.0 \| 456 \| 0.7605 \| 0.4289 \| 0.7602 \| 0.8719 \| \| No log \| 77.0 \| 462 \| 0.8327 \| 0.3976 \| 0.8323 \| 0.9123 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
frank1401/homework1140308	frank1401	2025-03-08T06:09:44Z	0	0	null	[ "safetensors", "llama", "trl", "sft", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-03-08T01:23:03Z	--- license: apache-2.0 tags: - trl - sft ---
wenh2004/chatglm3-lora-legal	wenh2004	2025-03-08T06:09:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-08T06:09:41Z	--- 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]
mradermacher/Cydonia-24B-v2.1-GGUF	mradermacher	2025-03-08T06:08:02Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:TheDrummer/Cydonia-24B-v2.1", "base_model:quantized:TheDrummer/Cydonia-24B-v2.1", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T04:01:03Z	--- base_model: TheDrummer/Cydonia-24B-v2.1 language: - en library_name: transformers license: other quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/TheDrummer/Cydonia-24B-v2.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Cydonia-24B-v2.1-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/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.Q2_K.gguf) \| Q2_K \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.Q3_K_M.gguf) \| Q3_K_M \| 11.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.Q3_K_L.gguf) \| Q3_K_L \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.IQ4_XS.gguf) \| IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.Q4_K_S.gguf) \| Q4_K_S \| 13.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.Q4_K_M.gguf) \| Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.Q5_K_S.gguf) \| Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.Q5_K_M.gguf) \| Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.Q6_K.gguf) \| Q6_K \| 19.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v2.1-GGUF/resolve/main/Cydonia-24B-v2.1.Q8_0.gguf) \| Q8_0 \| 25.2 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
texanrangee/cb5cc7da-6009-4e41-8afa-4d09161ecade	texanrangee	2025-03-08T06:06:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-08T00:50:10Z	--- 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]
Peaceuai/model_3	Peaceuai	2025-03-08T06:01:22Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T06:00:24Z	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: Peaceuai - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
sophiayk20/m2m100_418M_pt_formal	sophiayk20	2025-03-08T06:00:32Z	0	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "generated_from_trainer", "base_model:facebook/m2m100_418M", "base_model:finetune:facebook/m2m100_418M", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-03-08T01:43:11Z	--- library_name: transformers license: mit base_model: facebook/m2m100_418M tags: - generated_from_trainer metrics: - bleu model-index: - name: m2m100_418M_pt_formal 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. --> # m2m100_418M_pt_formal This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3145 - Bleu: 40.5054 ## 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: 32 - 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 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 4.0262 \| 0.3956 \| 500 \| 0.3792 \| 36.1929 \| \| 0.3736 \| 0.7911 \| 1000 \| 0.3307 \| 38.8103 \| \| 0.3346 \| 1.1867 \| 1500 \| 0.3236 \| 39.2764 \| \| 0.3118 \| 1.5823 \| 2000 \| 0.3194 \| 39.7404 \| \| 0.3084 \| 1.9778 \| 2500 \| 0.3160 \| 40.0558 \| \| 0.2856 \| 2.3734 \| 3000 \| 0.3154 \| 40.2045 \| \| 0.277 \| 2.7690 \| 3500 \| 0.3145 \| 40.5054 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0
Nayana-cognitivelab/Nayana-IR-colsmol_v0_1-hi-12k-4bit	Nayana-cognitivelab	2025-03-08T06:00:17Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-08T06:00:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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TArtx/parler-tts-mini-narrated-30	TArtx	2025-03-08T05:59:02Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-03-08T05:42:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/DSR1-Qwen-32B-still-GGUF	mradermacher	2025-03-08T05:58:08Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:moogician/DSR1-Qwen-32B-still", "base_model:quantized:moogician/DSR1-Qwen-32B-still", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T05:11:01Z	--- base_model: moogician/DSR1-Qwen-32B-still language: - en library_name: transformers license: other quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/moogician/DSR1-Qwen-32B-still <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DSR1-Qwen-32B-still-GGUF/resolve/main/DSR1-Qwen-32B-still.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Sunnyboon007/Lola	Sunnyboon007	2025-03-08T05:57:29Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-03-08T05:57:29Z	--- license: apache-2.0 ---
mlx-community/Preferred-MedLLM-Qwen-72B-8bit	mlx-community	2025-03-08T05:57:12Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mlx", "en", "ja", "base_model:pfnet/Preferred-MedLLM-Qwen-72B", "base_model:quantized:pfnet/Preferred-MedLLM-Qwen-72B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	2025-03-08T04:17:29Z	--- base_model: pfnet/Preferred-MedLLM-Qwen-72B language: - en - ja library_name: transformers pipeline_tag: text-generation license: other license_name: qwen tags: - mlx --- # mlx-community/Preferred-MedLLM-Qwen-72B-8bit The Model [mlx-community/Preferred-MedLLM-Qwen-72B-8bit](https://huggingface.co/mlx-community/Preferred-MedLLM-Qwen-72B-8bit) was converted to MLX format from [pfnet/Preferred-MedLLM-Qwen-72B](https://huggingface.co/pfnet/Preferred-MedLLM-Qwen-72B) using mlx-lm version 0.21.5. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Preferred-MedLLM-Qwen-72B-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
BICORP/Test-16	BICORP	2025-03-08T05:56:32Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-03-07T18:40:47Z	--- license: apache-2.0 ---
MOtifssss/Qwen2.5-1.5B-Open-R1-Distill	MOtifssss	2025-03-08T05:53:36Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-14T03:56:25Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Open-R1-Distill tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2.5-1.5B-Open-R1-Distill This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). 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="MOtifssss/Qwen2.5-1.5B-Open-R1-Distill", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gaotang/Self-Reflection%20Fine-tuning/runs/gvl1pohh) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Nayana-cognitivelab/Nayana-IR-colpali_v1_3-combined-15k-4bit	Nayana-cognitivelab	2025-03-08T05:50:48Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-08T05:50:35Z	--- 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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enuma-elis/Llama-3.3-70B-Instruct-bnb-4bit	enuma-elis	2025-03-08T05:50:23Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.3-70B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Llama-3.3-70B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-03-08T05:04:32Z	--- base_model: unsloth/Llama-3.3-70B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: enuma-elis - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.3-70B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Nayana-cognitivelab/Nayana-IR-colpali_v1_3-kn-12k-4bit	Nayana-cognitivelab	2025-03-08T05:48:23Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-08T05:48:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
XingYu520/svc	XingYu520	2025-03-08T05:46:40Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-03-08T05:46:40Z	--- license: apache-2.0 ---
Nayana-cognitivelab/Nayana-IR-colpali_v1_3-hi-47k-4bit	Nayana-cognitivelab	2025-03-08T05:45:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-08T05:45:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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mradermacher/DeepThinkers-Phi4-GGUF	mradermacher	2025-03-08T05:41:41Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:EpistemeAI/DeepThinkers-Phi4", "base_model:quantized:EpistemeAI/DeepThinkers-Phi4", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T04:06:29Z	--- base_model: EpistemeAI/DeepThinkers-Phi4 language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/EpistemeAI/DeepThinkers-Phi4 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/DeepThinkers-Phi4-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/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.Q2_K.gguf) \| Q2_K \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.Q3_K_S.gguf) \| Q3_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.Q3_K_M.gguf) \| Q3_K_M \| 7.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.Q3_K_L.gguf) \| Q3_K_L \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.IQ4_XS.gguf) \| IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.Q4_K_S.gguf) \| Q4_K_S \| 8.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.Q4_K_M.gguf) \| Q4_K_M \| 9.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.Q5_K_S.gguf) \| Q5_K_S \| 10.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.Q5_K_M.gguf) \| Q5_K_M \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.Q6_K.gguf) \| Q6_K \| 12.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepThinkers-Phi4-GGUF/resolve/main/DeepThinkers-Phi4.Q8_0.gguf) \| Q8_0 \| 15.7 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/leads-mistral-7b-v1-GGUF	mradermacher	2025-03-08T05:41:41Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:zifeng-ai/leads-mistral-7b-v1", "base_model:quantized:zifeng-ai/leads-mistral-7b-v1", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T04:20:28Z	--- base_model: zifeng-ai/leads-mistral-7b-v1 language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/zifeng-ai/leads-mistral-7b-v1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/leads-mistral-7b-v1-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/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/leads-mistral-7b-v1-GGUF/resolve/main/leads-mistral-7b-v1.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
lesso13/3d3dbb29-e18e-4edb-b7d8-6f569dfffd83	lesso13	2025-03-08T05:39:23Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4", "base_model:adapter:MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4", "region:us" ]	null	2025-03-08T03:29:04Z	--- library_name: peft base_model: MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4 tags: - axolotl - generated_from_trainer model-index: - name: 3d3dbb29-e18e-4edb-b7d8-6f569dfffd83 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: MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1a2cc6d384a11c08_train_data.json ds_type: json format: custom path: /workspace/input_data/1a2cc6d384a11c08_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso13/3d3dbb29-e18e-4edb-b7d8-6f569dfffd83 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000213 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/1a2cc6d384a11c08_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 130 sequence_len: 1024 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: 2d6e8b65-2a1a-4258-ac7a-632a18b74ff6 wandb_project: 13a wandb_run: your_name wandb_runid: 2d6e8b65-2a1a-4258-ac7a-632a18b74ff6 warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 3d3dbb29-e18e-4edb-b7d8-6f569dfffd83 This model is a fine-tuned version of [MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4](https://huggingface.co/MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5126 ## 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: 0.000213 - train_batch_size: 4 - eval_batch_size: 4 - seed: 130 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 2.2911 \| \| 1.5286 \| 0.1132 \| 500 \| 1.5126 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso16/5852b5e9-8156-4032-b100-3170940cd041	lesso16	2025-03-08T05:39:00Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4", "base_model:adapter:MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4", "region:us" ]	null	2025-03-08T03:29:08Z	--- library_name: peft base_model: MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4 tags: - axolotl - generated_from_trainer model-index: - name: 5852b5e9-8156-4032-b100-3170940cd041 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: MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1a2cc6d384a11c08_train_data.json ds_type: json format: custom path: /workspace/input_data/1a2cc6d384a11c08_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso16/5852b5e9-8156-4032-b100-3170940cd041 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000216 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/1a2cc6d384a11c08_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 160 sequence_len: 1024 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: 2d6e8b65-2a1a-4258-ac7a-632a18b74ff6 wandb_project: 16a wandb_run: your_name wandb_runid: 2d6e8b65-2a1a-4258-ac7a-632a18b74ff6 warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 5852b5e9-8156-4032-b100-3170940cd041 This model is a fine-tuned version of [MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4](https://huggingface.co/MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5113 ## 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: 0.000216 - train_batch_size: 4 - eval_batch_size: 4 - seed: 160 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 2.2907 \| \| 1.5151 \| 0.1132 \| 500 \| 1.5113 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
John6666/sdxl-noobsim-v46vpred-ultrares-v46noobsimvpred15-sdxl	John6666	2025-03-08T05:35:35Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "realistic", "photorealistic", "CLIP_L_OMEGAβ", "CLIP_G_OMEGAβ", "finetune", "experiment", "v-pred", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-03-08T05:30:28Z	--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - realistic - photorealistic - CLIP_L_OMEGAβ - CLIP_G_OMEGAβ - finetune - experiment - v-pred --- Original model is [here](https://civitai.com/models/1177470?modelVersionId=1504726). This model created by [AbstractPhila](https://civitai.com/user/AbstractPhila).
lesso11/549b03ab-54a6-48ce-9b14-3f40ae246b2c	lesso11	2025-03-08T05:34:09Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-135M", "base_model:adapter:unsloth/SmolLM-135M", "license:apache-2.0", "region:us" ]	null	2025-03-08T00:13:56Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-135M tags: - axolotl - generated_from_trainer model-index: - name: 549b03ab-54a6-48ce-9b14-3f40ae246b2c 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/SmolLM-135M bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d148afb262ef385c_train_data.json ds_type: json format: custom path: /workspace/input_data/d148afb262ef385c_train_data.json type: field_input: alpaca_prompt field_instruction: instruction field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso11/549b03ab-54a6-48ce-9b14-3f40ae246b2c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000211 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 7000 micro_batch_size: 4 mlflow_experiment_name: /tmp/d148afb262ef385c_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 110 sequence_len: 1024 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: 104e868d-5498-4c04-8a6e-af6f3717fe60 wandb_project: 11a wandb_run: your_name wandb_runid: 104e868d-5498-4c04-8a6e-af6f3717fe60 warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 549b03ab-54a6-48ce-9b14-3f40ae246b2c This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8269 ## 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: 0.000211 - train_batch_size: 4 - eval_batch_size: 4 - seed: 110 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 7000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0005 \| 1 \| 2.3279 \| \| 1.9823 \| 0.2722 \| 500 \| 1.9859 \| \| 1.995 \| 0.5444 \| 1000 \| 1.9599 \| \| 1.9828 \| 0.8166 \| 1500 \| 1.9339 \| \| 1.8594 \| 1.0892 \| 2000 \| 1.9220 \| \| 1.842 \| 1.3614 \| 2500 \| 1.9010 \| \| 1.7988 \| 1.6336 \| 3000 \| 1.8846 \| \| 1.7691 \| 1.9058 \| 3500 \| 1.8621 \| \| 1.6727 \| 2.1784 \| 4000 \| 1.8448 \| \| 1.7137 \| 2.4506 \| 4500 \| 1.8377 \| \| 1.7098 \| 2.7228 \| 5000 \| 1.8284 \| \| 1.6372 \| 2.9950 \| 5500 \| 1.8186 \| \| 1.6218 \| 3.2676 \| 6000 \| 1.8223 \| \| 1.6231 \| 3.5398 \| 6500 \| 1.8248 \| \| 1.6592 \| 3.8120 \| 7000 \| 1.8269 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/DPSK-Distill-32B-Token-GGUF	mradermacher	2025-03-08T05:32:38Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Viol2000/DPSK-Distill-32B-Token", "base_model:quantized:Viol2000/DPSK-Distill-32B-Token", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T04:06:52Z	--- base_model: Viol2000/DPSK-Distill-32B-Token language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Viol2000/DPSK-Distill-32B-Token <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.Q4_K_M.gguf) \| Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DPSK-Distill-32B-Token-GGUF/resolve/main/DPSK-Distill-32B-Token.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold0	genki10	2025-03-08T05:28:29Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-03-08T04:43:29Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold0 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. --> # ASAP_nosemanticV2_FineTuningBERT_AugV12_k7_task1_organization_k7_k7_fold0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7375 - Qwk: 0.5133 - Mse: 0.7375 - Rmse: 0.8588 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 6 \| 6.7808 \| 0.0 \| 6.7808 \| 2.6040 \| \| No log \| 2.0 \| 12 \| 4.2243 \| 0.0039 \| 4.2243 \| 2.0553 \| \| No log \| 3.0 \| 18 \| 2.0865 \| 0.0944 \| 2.0865 \| 1.4445 \| \| No log \| 4.0 \| 24 \| 1.2136 \| 0.0316 \| 1.2136 \| 1.1016 \| \| No log \| 5.0 \| 30 \| 1.2239 \| 0.0548 \| 1.2239 \| 1.1063 \| \| No log \| 6.0 \| 36 \| 1.0788 \| 0.1002 \| 1.0788 \| 1.0386 \| \| No log \| 7.0 \| 42 \| 1.0530 \| 0.2204 \| 1.0530 \| 1.0262 \| \| No log \| 8.0 \| 48 \| 0.7780 \| 0.3963 \| 0.7780 \| 0.8821 \| \| No log \| 9.0 \| 54 \| 0.7862 \| 0.4230 \| 0.7862 \| 0.8867 \| \| No log \| 10.0 \| 60 \| 0.8138 \| 0.4185 \| 0.8138 \| 0.9021 \| \| No log \| 11.0 \| 66 \| 0.6654 \| 0.4144 \| 0.6654 \| 0.8157 \| \| No log \| 12.0 \| 72 \| 0.7386 \| 0.5279 \| 0.7386 \| 0.8594 \| \| No log \| 13.0 \| 78 \| 0.8035 \| 0.5002 \| 0.8035 \| 0.8964 \| \| No log \| 14.0 \| 84 \| 0.7581 \| 0.5051 \| 0.7581 \| 0.8707 \| \| No log \| 15.0 \| 90 \| 0.7233 \| 0.5089 \| 0.7233 \| 0.8505 \| \| No log \| 16.0 \| 96 \| 0.8051 \| 0.4401 \| 0.8051 \| 0.8972 \| \| No log \| 17.0 \| 102 \| 0.7567 \| 0.4941 \| 0.7567 \| 0.8699 \| \| No log \| 18.0 \| 108 \| 0.8707 \| 0.4863 \| 0.8707 \| 0.9331 \| \| No log \| 19.0 \| 114 \| 1.1974 \| 0.3453 \| 1.1974 \| 1.0943 \| \| No log \| 20.0 \| 120 \| 0.7859 \| 0.4799 \| 0.7859 \| 0.8865 \| \| No log \| 21.0 \| 126 \| 1.0138 \| 0.3648 \| 1.0138 \| 1.0069 \| \| No log \| 22.0 \| 132 \| 0.7047 \| 0.5068 \| 0.7047 \| 0.8395 \| \| No log \| 23.0 \| 138 \| 0.8307 \| 0.4661 \| 0.8307 \| 0.9114 \| \| No log \| 24.0 \| 144 \| 0.7614 \| 0.5073 \| 0.7614 \| 0.8726 \| \| No log \| 25.0 \| 150 \| 0.8226 \| 0.4655 \| 0.8226 \| 0.9070 \| \| No log \| 26.0 \| 156 \| 0.8165 \| 0.4613 \| 0.8165 \| 0.9036 \| \| No log \| 27.0 \| 162 \| 0.9831 \| 0.4721 \| 0.9831 \| 0.9915 \| \| No log \| 28.0 \| 168 \| 1.0394 \| 0.4185 \| 1.0394 \| 1.0195 \| \| No log \| 29.0 \| 174 \| 0.7373 \| 0.5216 \| 0.7373 \| 0.8587 \| \| No log \| 30.0 \| 180 \| 0.9787 \| 0.4235 \| 0.9787 \| 0.9893 \| \| No log \| 31.0 \| 186 \| 0.8639 \| 0.4846 \| 0.8639 \| 0.9295 \| \| No log \| 32.0 \| 192 \| 0.7515 \| 0.5432 \| 0.7515 \| 0.8669 \| \| No log \| 33.0 \| 198 \| 0.9501 \| 0.4566 \| 0.9501 \| 0.9747 \| \| No log \| 34.0 \| 204 \| 0.9949 \| 0.4107 \| 0.9949 \| 0.9974 \| \| No log \| 35.0 \| 210 \| 1.0571 \| 0.4636 \| 1.0571 \| 1.0281 \| \| No log \| 36.0 \| 216 \| 0.9711 \| 0.4803 \| 0.9711 \| 0.9854 \| \| No log \| 37.0 \| 222 \| 0.9057 \| 0.4351 \| 0.9057 \| 0.9517 \| \| No log \| 38.0 \| 228 \| 1.0173 \| 0.4779 \| 1.0173 \| 1.0086 \| \| No log \| 39.0 \| 234 \| 0.8642 \| 0.5086 \| 0.8642 \| 0.9296 \| \| No log \| 40.0 \| 240 \| 0.8859 \| 0.4795 \| 0.8859 \| 0.9412 \| \| No log \| 41.0 \| 246 \| 0.8663 \| 0.4843 \| 0.8663 \| 0.9307 \| \| No log \| 42.0 \| 252 \| 0.8377 \| 0.5049 \| 0.8377 \| 0.9152 \| \| No log \| 43.0 \| 258 \| 0.8322 \| 0.5052 \| 0.8322 \| 0.9122 \| \| No log \| 44.0 \| 264 \| 0.6407 \| 0.5504 \| 0.6407 \| 0.8004 \| \| No log \| 45.0 \| 270 \| 0.9204 \| 0.4813 \| 0.9204 \| 0.9594 \| \| No log \| 46.0 \| 276 \| 0.9213 \| 0.4563 \| 0.9213 \| 0.9598 \| \| No log \| 47.0 \| 282 \| 1.0325 \| 0.4318 \| 1.0325 \| 1.0161 \| \| No log \| 48.0 \| 288 \| 0.6882 \| 0.5536 \| 0.6882 \| 0.8296 \| \| No log \| 49.0 \| 294 \| 0.6289 \| 0.5331 \| 0.6289 \| 0.7931 \| \| No log \| 50.0 \| 300 \| 0.8150 \| 0.5143 \| 0.8150 \| 0.9028 \| \| No log \| 51.0 \| 306 \| 0.8232 \| 0.5139 \| 0.8232 \| 0.9073 \| \| No log \| 52.0 \| 312 \| 0.8732 \| 0.5006 \| 0.8732 \| 0.9344 \| \| No log \| 53.0 \| 318 \| 0.7829 \| 0.4945 \| 0.7829 \| 0.8848 \| \| No log \| 54.0 \| 324 \| 0.7116 \| 0.4936 \| 0.7116 \| 0.8436 \| \| No log \| 55.0 \| 330 \| 0.7365 \| 0.4995 \| 0.7365 \| 0.8582 \| \| No log \| 56.0 \| 336 \| 0.8263 \| 0.4929 \| 0.8263 \| 0.9090 \| \| No log \| 57.0 \| 342 \| 0.7782 \| 0.5277 \| 0.7782 \| 0.8822 \| \| No log \| 58.0 \| 348 \| 0.8694 \| 0.5090 \| 0.8694 \| 0.9324 \| \| No log \| 59.0 \| 354 \| 0.8633 \| 0.5002 \| 0.8633 \| 0.9292 \| \| No log \| 60.0 \| 360 \| 0.8805 \| 0.4966 \| 0.8805 \| 0.9383 \| \| No log \| 61.0 \| 366 \| 0.7954 \| 0.5146 \| 0.7954 \| 0.8919 \| \| No log \| 62.0 \| 372 \| 0.6620 \| 0.5549 \| 0.6620 \| 0.8137 \| \| No log \| 63.0 \| 378 \| 0.9616 \| 0.4712 \| 0.9616 \| 0.9806 \| \| No log \| 64.0 \| 384 \| 0.8070 \| 0.5131 \| 0.8070 \| 0.8983 \| \| No log \| 65.0 \| 390 \| 0.7670 \| 0.5010 \| 0.7670 \| 0.8758 \| \| No log \| 66.0 \| 396 \| 0.7910 \| 0.5145 \| 0.7910 \| 0.8894 \| \| No log \| 67.0 \| 402 \| 0.7502 \| 0.5119 \| 0.7502 \| 0.8662 \| \| No log \| 68.0 \| 408 \| 0.9059 \| 0.4825 \| 0.9059 \| 0.9518 \| \| No log \| 69.0 \| 414 \| 0.7574 \| 0.4987 \| 0.7574 \| 0.8703 \| \| No log \| 70.0 \| 420 \| 0.6676 \| 0.5253 \| 0.6676 \| 0.8171 \| \| No log \| 71.0 \| 426 \| 0.9502 \| 0.4772 \| 0.9502 \| 0.9748 \| \| No log \| 72.0 \| 432 \| 0.6744 \| 0.5363 \| 0.6744 \| 0.8212 \| \| No log \| 73.0 \| 438 \| 0.7808 \| 0.5107 \| 0.7808 \| 0.8836 \| \| No log \| 74.0 \| 444 \| 0.7827 \| 0.5203 \| 0.7827 \| 0.8847 \| \| No log \| 75.0 \| 450 \| 0.7132 \| 0.5079 \| 0.7132 \| 0.8445 \| \| No log \| 76.0 \| 456 \| 0.7905 \| 0.5028 \| 0.7905 \| 0.8891 \| \| No log \| 77.0 \| 462 \| 0.8389 \| 0.5044 \| 0.8389 \| 0.9159 \| \| No log \| 78.0 \| 468 \| 0.6327 \| 0.5523 \| 0.6327 \| 0.7954 \| \| No log \| 79.0 \| 474 \| 0.7644 \| 0.5113 \| 0.7644 \| 0.8743 \| \| No log \| 80.0 \| 480 \| 0.7303 \| 0.5021 \| 0.7303 \| 0.8546 \| \| No log \| 81.0 \| 486 \| 0.7201 \| 0.5023 \| 0.7201 \| 0.8486 \| \| No log \| 82.0 \| 492 \| 0.7375 \| 0.5133 \| 0.7375 \| 0.8588 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
OgiServiceDesigner/rlhf-Llama-3.2-1B	OgiServiceDesigner	2025-03-08T05:28:00Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-03-08T05:26:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
davidmaestrecic/agency_cic_model-david-1	davidmaestrecic	2025-03-08T05:26:40Z	3	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "region:us" ]	null	2025-03-05T03:30:00Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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] ### Framework versions - PEFT 0.14.0
LeroyDyer/_Spydaz_Web_AI_AGI_R1_OmG_MathMaster	LeroyDyer	2025-03-08T05:24:59Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:LeroyDyer/_Spydaz_Web_AI_AGI_R1_Math_Master", "base_model:finetune:LeroyDyer/_Spydaz_Web_AI_AGI_R1_Math_Master", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-03-08T05:17:43Z	--- base_model: LeroyDyer/_Spydaz_Web_AI_AGI_R1_Math_Master tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: LeroyDyer - License: apache-2.0 - Finetuned from model : LeroyDyer/_Spydaz_Web_AI_AGI_R1_Math_Master 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)
nossie0360/q-FrozenLake-v1-4x4-noSlippery	nossie0360	2025-03-08T05:24:16Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-03-08T05:24:13Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="nossie0360/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
John6666/cinero-illustrious-v4fp8-sdxl	John6666	2025-03-08T05:22:33Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "realism", "portrait", "photography", "creative", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-03-08T05:17:28Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - realism - portrait - photography - creative - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1332040/cinero-illustrious-v4?modelVersionId=1503990). This model created by [homoludens](https://civitai.com/user/homoludens).
jonathansculley/Reinforce-Pixelcopter-PLE-v0	jonathansculley	2025-03-08T05:21:11Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-03-08T04:43:27Z	--- 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: 81.70 +/- 46.68 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
fats-fme/969a6c78-290a-48f6-a49e-95855e666555	fats-fme	2025-03-08T05:21:10Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:NousResearch/Genstruct-7B", "base_model:adapter:NousResearch/Genstruct-7B", "license:apache-2.0", "region:us" ]	null	2025-03-08T04:08:07Z	--- library_name: peft license: apache-2.0 base_model: NousResearch/Genstruct-7B tags: - axolotl - generated_from_trainer model-index: - name: 969a6c78-290a-48f6-a49e-95855e666555 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Genstruct-7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 75b2f36e9fccddee_train_data.json ds_type: json format: custom path: /workspace/input_data/75b2f36e9fccddee_train_data.json type: field_instruction: tools field_output: func_desc format: '{instruction}' 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: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: fats-fme/969a6c78-290a-48f6-a49e-95855e666555 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 256 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 128 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 70GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/75b2f36e9fccddee_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: 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: ce748d17-c160-411d-84ac-e3efbcca61b8 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: ce748d17-c160-411d-84ac-e3efbcca61b8 warmup_steps: 100 weight_decay: 0.05 xformers_attention: null ``` </details><br> # 969a6c78-290a-48f6-a49e-95855e666555 This model is a fine-tuned version of [NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/Genstruct-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0056 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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: 100 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 0.9089 \| \| 0.1634 \| 0.0211 \| 100 \| 0.0352 \| \| 0.063 \| 0.0422 \| 200 \| 0.0056 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Jonjew/PascalBlancheStyle	Jonjew	2025-03-08T05:19:21Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-03-08T05:19:14Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- By Passcal Blanché. A highly stylized CGI illustration in a futuristic sci-fi aesthetic. The central figure is a muscular and athletic female warrior, wearing a red jumpsuit that accentuates her curves. Her skin is a shiny metallic blue and her long, flowing hair is also blue, matching the metallic hue. She wears a skull-shaped helmet that covers much of her face. The skull-like appearance that caps her head with a prominent upper jaw, gives her a menacing appearance. output: url: images/_00000_7_.png - text: >- By Passcal Blanché. A digital artwork depicting a fantastical, ethereal scene. At the center is a nude female figure with a pale, almost translucent skin tone. She has long, flowing black hair that obscures her face, giving her a mysterious and otherworldly appearance. Her body is slender yet muscular, with pronounced abs and defined limbs. She is seated on a large, ornate, red stone pedestal that features intricate, abstract carvings of mythical beasts. output: url: images/_00000_27_.png - text: >- By Passcal Blanché. A digitally created artwork in a surreal and cyberpunk style. The central figure is a woman with a pale, smooth complexion and long, flowing hair, wearing a futuristic, form-fitting outfit that accentuates her curvy figure. She sits cross-legged against a plain beige background and her Japanese-inspired outfit includes a high-necked metallic bodice that reveals her ample bosom and tight, high-waisted pants. Her face is made up in a geisha style with red stripes over her eyes and mouth, adding a striking contrast to her otherwise neutral makeup. output: url: images/_00000_21_.png - text: >- By Passcal Blanché. A digitally created artwork in a surrealist style. It depicts an androgynous figure with blue skin, long flowing black hair, and a muscular physique. The figure is crouching and wearing a large chain on one of its arms. The figure wears wings resembling those of a dragonfly or butterfly, which are transparent with a teal tint. It also wears accessories that resemble bones as well as a small skull at the waist giving a tribal appeal. The figure's face is serene, its head is tilted slightly to the side and its eyes are focused on something in the distance. output: url: images/_00000_32_.png - text: >- By Passcal Blanché. A digital illustration in a fantasy art style. The central figure is a fearsome, mythical creature, resembling a hybrid of a snake and a human, known as Medusa. Medusa has the head of a woman with flowing dark green hair and a fierce expression. Her body is serpentine, with scales that shimmer in shades of green and brown. She holds a trident in her right hand, and a severed head in her left hand, which she holds aloft. output: url: images/_00000_46_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: By Passcal Blanché license: unknown --- # Pascal Blanché <Gallery /> ## Model description FROM https://civitai.com/models/1285926/pascal-blanche?modelVersionId=1274884 Trigger By Passcal Blanché Strength 0.8 - 1.2 About this version Inspired by Pascal Blanché artwork (one of my favorites). Recommended resources : Fluxmania III or Flux1.dev fp8. Settings : dpmpp_2m / sgm_uniform / 25 - 30 steps / cfg 3.5 Weighting : 0.8 - 1.2 ## Trigger words You should use `By Passcal Blanché` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/PascalBlancheStyle/tree/main) them in the Files & versions tab.
nt-ai/whisper-small-bn	nt-ai	2025-03-08T05:13:35Z	45	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "bn", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-02-17T07:55:49Z	--- library_name: transformers language: - bn license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Bengali - Nripen Tudu 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 Small Bengali - Nripen Tudu This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1583 - eval_wer: 47.3416 - eval_runtime: 7484.6397 - eval_samples_per_second: 1.116 - eval_steps_per_second: 0.14 - epoch: 0.7626 - step: 800 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.49.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.2 - Tokenizers 0.21.0
eastcourt/distilbert-base-uncased-finetuned-cola	eastcourt	2025-03-08T05:13:02Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-03-08T05:12:49Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola 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. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8251 - Matthews Correlation: 0.5570 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5209 \| 1.0 \| 535 \| 0.4635 \| 0.4830 \| \| 0.3506 \| 2.0 \| 1070 \| 0.4708 \| 0.5339 \| \| 0.2351 \| 3.0 \| 1605 \| 0.6342 \| 0.5331 \| \| 0.1735 \| 4.0 \| 2140 \| 0.7744 \| 0.5456 \| \| 0.126 \| 5.0 \| 2675 \| 0.8251 \| 0.5570 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.5.1+cu124 - Datasets 2.21.0 - Tokenizers 0.21.0
prithivMLmods/Sombrero-Opus-14B-Sm3	prithivMLmods	2025-03-08T05:12:34Z	0	3	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "StreamlinedMemory", "Math", "conversational", "en", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T03:20:29Z	--- license: apache-2.0 language: - en base_model: - Qwen/Qwen2.5-14B-Instruct pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - StreamlinedMemory - Math --- ![sdfsdsd.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/H1xUnIpR9hskr21e56gJB.png) # Sombrero-Opus-14B-Sm3 Sombrero-Opus-14B-Sm3 is based on the Qwen 2.5 14B modality architecture, designed to enhance coding efficiency and computational reasoning. This model is optimized for streamlined memory usage, avoiding unwanted textual token generation, and excelling in coding, explanatory reasoning, mathematical problem-solving, and technical tasks. It has been fine-tuned using specialized datasets to improve code generation, structured programming logic, and problem-solving capabilities. ## Key Improvements 1. Optimized for Coding: The model specializes in generating high-quality, structured code with minimal redundant tokens, ensuring efficient execution. 2. Enhanced Memory Utilization: Implements streamlined memory optimization to reduce computational overhead and improve performance. 3. Superior Reasoning Capabilities: Excels in solving complex mathematical and algorithmic problems with logical and structured explanations. 4. Long-Context Support: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed coding responses. 5. Reduced Unwanted Textual Tokens: Ensures a more focused output for coding tasks by minimizing excessive textual responses. ## Quickstart with transformers Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Sombrero-Opus-14B-Sm3" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Write a Python function to find the Fibonacci sequence." messages = [ {"role": "system", "content": "You are an advanced coding 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) ``` ## Intended Use 1. Code Generation & Optimization: Designed for developers, assisting in writing, refactoring, and optimizing code across multiple programming languages. 2. Algorithm & Mathematical Problem Solving: Provides precise explanations and solutions for computational and mathematical problems. 3. Technical Explanations & Documentation: Generates clear and structured explanations for coding concepts, libraries, and APIs. 4. Debugging Assistance: Helps analyze code snippets, detect errors, and suggest corrections. 5. Educational Use: Assists students and learners by breaking down complex programming topics into easily understandable sections. 6. Structured Data Processing: Capable of analyzing and generating structured outputs, such as JSON, XML, and tables, making it ideal for data science applications. ## Limitations 1. Hardware Requirements: Requires high-memory GPUs or TPUs due to its large parameter size and long-context support. 2. Potential Bias in Responses: While designed to be neutral, outputs may still reflect biases present in training data. 3. Inconsistent Outputs in Creative Tasks: May produce variable results in storytelling and non-technical topics. 4. Limited Real-World Awareness: Does not have access to real-time events beyond its training cutoff. 5. Error Propagation in Extended Outputs: Minor errors in early responses may affect overall coherence in long-form code outputs. 6. Prompt Sensitivity**: The effectiveness of responses may depend on how well the input prompt is structured.
mradermacher/OpenR1-Qwen-7B-SFT2-GGUF	mradermacher	2025-03-08T05:12:15Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ZMC2019/OpenR1-Qwen-7B-SFT2", "base_model:quantized:ZMC2019/OpenR1-Qwen-7B-SFT2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T04:39:12Z	--- base_model: ZMC2019/OpenR1-Qwen-7B-SFT2 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ZMC2019/OpenR1-Qwen-7B-SFT2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-Qwen-7B-SFT2-GGUF/resolve/main/OpenR1-Qwen-7B-SFT2.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 -->
texanrangee/7c211d71-28c5-45b5-94b2-5d1adcebf91e	texanrangee	2025-03-08T05:10:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-08T04:37:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
omarkhx/omar-first	omarkhx	2025-03-08T05:09:14Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-03-08T05:09:14Z	--- license: apache-2.0 ---
mradermacher/Llama3.2_3B_Reasoning_V2-GGUF	mradermacher	2025-03-08T05:07:25Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "sft", "en", "base_model:Aditya0619/Llama3.2_3B_Reasoning_V2", "base_model:quantized:Aditya0619/Llama3.2_3B_Reasoning_V2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T04:31:12Z	--- base_model: Aditya0619/Llama3.2_3B_Reasoning_V2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Aditya0619/Llama3.2_3B_Reasoning_V2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.Q3_K_M.gguf) \| Q3_K_M \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.Q3_K_L.gguf) \| Q3_K_L \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.Q4_K_S.gguf) \| Q4_K_S \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.Q4_K_M.gguf) \| Q4_K_M \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.Q5_K_S.gguf) \| Q5_K_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.Q5_K_M.gguf) \| Q5_K_M \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.Q6_K.gguf) \| Q6_K \| 2.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.Q8_0.gguf) \| Q8_0 \| 3.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama3.2_3B_Reasoning_V2-GGUF/resolve/main/Llama3.2_3B_Reasoning_V2.f16.gguf) \| f16 \| 6.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF	mradermacher	2025-03-08T05:03:17Z	0	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "grpo", "en", "base_model:m1n9x/Qwen2.5_3B-GRPO-medical-reasoning", "base_model:quantized:m1n9x/Qwen2.5_3B-GRPO-medical-reasoning", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T04:21:31Z	--- base_model: m1n9x/Qwen2.5_3B-GRPO-medical-reasoning language: - en library_name: transformers quantized_by: mradermacher tags: - unsloth - trl - grpo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/m1n9x/Qwen2.5_3B-GRPO-medical-reasoning <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.Q2_K.gguf) \| Q2_K \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.Q3_K_M.gguf) \| Q3_K_M \| 1.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.Q3_K_L.gguf) \| Q3_K_L \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.Q4_K_M.gguf) \| Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.Q5_K_S.gguf) \| Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.Q8_0.gguf) \| Q8_0 \| 3.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5_3B-GRPO-medical-reasoning-GGUF/resolve/main/Qwen2.5_3B-GRPO-medical-reasoning.f16.gguf) \| f16 \| 6.3 \| 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 -->
rmikeyjohnson314/CohortAI	rmikeyjohnson314	2025-03-08T04:56:03Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-03-08T04:56:03Z	--- license: apache-2.0 ---
CompassioninMachineLearning/20K_mixed_15k_animals_march7_strict_llama_chat_prompts	CompassioninMachineLearning	2025-03-08T04:53:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-03-08T04:49:24Z	--- base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: JoyeeChen - License: apache-2.0 - Finetuned from model : unsloth/llama-3.1-8b-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)
WPRM/policy-bid-text-epoch5-1e-5	WPRM	2025-03-08T04:51:42Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-3B-Instruct-AWQ", "base_model:adapter:Qwen/Qwen2.5-3B-Instruct-AWQ", "region:us" ]	null	2025-03-08T04:51:31Z	--- base_model: Qwen/Qwen2.5-3B-Instruct-AWQ library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0
anwitac795/LunarLander-v2	anwitac795	2025-03-08T04:43:42Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-03-08T04:43:30Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 251.27 +/- 25.01 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
zhangthree/fortunetelling	zhangthree	2025-03-08T04:43:32Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "zh", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-08T04:13:35Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en - zh --- # Uploaded model - Developed by: zhangthree - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-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)
Jonjew/CraigHannaStyle	Jonjew	2025-03-08T04:42:27Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-03-08T04:42:19Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- By Craig Hanna. A portrait of a woman with an expressive expression, painted in a realistic style with a focus on texture and color. the woman is positioned in the middle of the image, with her upper body facing the viewer. she appears to be in her mid-twenties, with dark brown hair styled in a messy bun on the left side of her face. her eyes are a bright orange, and she has a serene and contemplative expression. her lips are slightly parted, as if she is about to say something. she is wearing a white shirt, and her hands are clasped together in front of her neck. the background is a soft gradient of light purple and white, with splashes of green and brown, creating a sense of movement and energy. the style is reminiscent of contemporary art, with bold lines and vibrant colors that bring the subject to life. parameters: negative_prompt: 'Steps: 25 Seed: 798538151615720' output: url: images/SC_00063_.png - text: >- By Craig Hanna. A realistic digital painting of a woman standing in a contemplative pose, wearing a long, white dress with intricate patterns and a matching shawl draped over her left shoulder. the woman, who appears to be in her late 20s or early 30s, has dark skin, dark hair tied in a bun, and a serious expression. she is standing in the middle of the image, facing away from the viewer, with her full body visible. the background is a dark, textured wall, and the floor is made of light-colored wood. the lighting is soft and diffused, casting gentle shadows on the woman's face and body. the style is realistic with a touch of realism, featuring detailed textures and a muted color palette. parameters: negative_prompt: 'Steps: 25 Seed: 12833010338283' output: url: images/SC_00021_.png - text: >- By Craig Hanna. A painting of a nude woman with a muscular physique, sitting at a table with his back turned towards the viewer. the woman has a black hair tied in a ponytail, and is wearing a green and red checkered cloth draped over his lap. his body is slim and muscular, with a focus on his back and shoulders. he is facing away from the viewer, with his hands resting on the table. the background is a simple, neutral-colored wall with a yellow pillow on the right side. the painting is done in a realistic style with a mix of warm tones and textures, creating a sense of intimacy and closeness. parameters: negative_prompt: 'Steps: 25 Seed: 416661955050907' output: url: images/SC_00061_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: By Craig Hanna license: unknown --- # Craig Hanna <Gallery /> ## Model description FROM https://civitai.com/models/1287715/craig-hanna?modelVersionId=1452941 Trigger By Craig Hanna Strength 1 About this version Model inspired by Craig Hanna artwork. Trained on Civitai with a dataset of 46 images Recommended resources : Fluxmania III Recommended settings : dpmpp_2m / sgm_uniform / 25 steps / flux guidance : 3.5 Weighting : 1.0 ## Trigger words You should use `By Craig Hanna` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/CraigHannaStyle/tree/main) them in the Files & versions tab.
genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold4	genki10	2025-03-08T04:41:26Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-03-08T04:00:14Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold4 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. --> # ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold4 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7084 - Qwk: 0.4859 - Mse: 0.7084 - Rmse: 0.8417 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 5 \| 6.4415 \| 0.0001 \| 6.4415 \| 2.5380 \| \| No log \| 2.0 \| 10 \| 3.5960 \| 0.0079 \| 3.5960 \| 1.8963 \| \| No log \| 3.0 \| 15 \| 2.0749 \| 0.1307 \| 2.0749 \| 1.4404 \| \| No log \| 4.0 \| 20 \| 1.2818 \| 0.0509 \| 1.2818 \| 1.1322 \| \| No log \| 5.0 \| 25 \| 1.5192 \| 0.1236 \| 1.5192 \| 1.2326 \| \| No log \| 6.0 \| 30 \| 1.1616 \| 0.1964 \| 1.1616 \| 1.0778 \| \| No log \| 7.0 \| 35 \| 1.1014 \| 0.2685 \| 1.1014 \| 1.0495 \| \| No log \| 8.0 \| 40 \| 0.7682 \| 0.4501 \| 0.7682 \| 0.8765 \| \| No log \| 9.0 \| 45 \| 1.5972 \| 0.2671 \| 1.5972 \| 1.2638 \| \| No log \| 10.0 \| 50 \| 0.8350 \| 0.4151 \| 0.8350 \| 0.9138 \| \| No log \| 11.0 \| 55 \| 0.6592 \| 0.4850 \| 0.6592 \| 0.8119 \| \| No log \| 12.0 \| 60 \| 1.2997 \| 0.3583 \| 1.2997 \| 1.1400 \| \| No log \| 13.0 \| 65 \| 0.8738 \| 0.4252 \| 0.8738 \| 0.9348 \| \| No log \| 14.0 \| 70 \| 0.9234 \| 0.4575 \| 0.9234 \| 0.9609 \| \| No log \| 15.0 \| 75 \| 0.8433 \| 0.4523 \| 0.8433 \| 0.9183 \| \| No log \| 16.0 \| 80 \| 1.0331 \| 0.4008 \| 1.0331 \| 1.0164 \| \| No log \| 17.0 \| 85 \| 0.8675 \| 0.4715 \| 0.8675 \| 0.9314 \| \| No log \| 18.0 \| 90 \| 1.3914 \| 0.3609 \| 1.3914 \| 1.1796 \| \| No log \| 19.0 \| 95 \| 1.1679 \| 0.3914 \| 1.1679 \| 1.0807 \| \| No log \| 20.0 \| 100 \| 0.9148 \| 0.4106 \| 0.9148 \| 0.9564 \| \| No log \| 21.0 \| 105 \| 0.7493 \| 0.4598 \| 0.7493 \| 0.8656 \| \| No log \| 22.0 \| 110 \| 0.8396 \| 0.4606 \| 0.8396 \| 0.9163 \| \| No log \| 23.0 \| 115 \| 0.9157 \| 0.4431 \| 0.9157 \| 0.9569 \| \| No log \| 24.0 \| 120 \| 0.8621 \| 0.4384 \| 0.8621 \| 0.9285 \| \| No log \| 25.0 \| 125 \| 0.9933 \| 0.4326 \| 0.9933 \| 0.9967 \| \| No log \| 26.0 \| 130 \| 0.7497 \| 0.5092 \| 0.7497 \| 0.8658 \| \| No log \| 27.0 \| 135 \| 1.0817 \| 0.4061 \| 1.0817 \| 1.0401 \| \| No log \| 28.0 \| 140 \| 0.7755 \| 0.4526 \| 0.7755 \| 0.8806 \| \| No log \| 29.0 \| 145 \| 0.8250 \| 0.4694 \| 0.8250 \| 0.9083 \| \| No log \| 30.0 \| 150 \| 0.9010 \| 0.4517 \| 0.9010 \| 0.9492 \| \| No log \| 31.0 \| 155 \| 0.9045 \| 0.4566 \| 0.9045 \| 0.9511 \| \| No log \| 32.0 \| 160 \| 0.8956 \| 0.4382 \| 0.8956 \| 0.9463 \| \| No log \| 33.0 \| 165 \| 0.7899 \| 0.4306 \| 0.7899 \| 0.8888 \| \| No log \| 34.0 \| 170 \| 0.7645 \| 0.4310 \| 0.7645 \| 0.8743 \| \| No log \| 35.0 \| 175 \| 1.0332 \| 0.4153 \| 1.0332 \| 1.0165 \| \| No log \| 36.0 \| 180 \| 0.7561 \| 0.4338 \| 0.7561 \| 0.8696 \| \| No log \| 37.0 \| 185 \| 0.7050 \| 0.4536 \| 0.7050 \| 0.8397 \| \| No log \| 38.0 \| 190 \| 1.1110 \| 0.4055 \| 1.1110 \| 1.0540 \| \| No log \| 39.0 \| 195 \| 0.7175 \| 0.4637 \| 0.7175 \| 0.8471 \| \| No log \| 40.0 \| 200 \| 0.8152 \| 0.4596 \| 0.8152 \| 0.9029 \| \| No log \| 41.0 \| 205 \| 0.7787 \| 0.4781 \| 0.7787 \| 0.8824 \| \| No log \| 42.0 \| 210 \| 0.6487 \| 0.5117 \| 0.6487 \| 0.8054 \| \| No log \| 43.0 \| 215 \| 0.8734 \| 0.4426 \| 0.8734 \| 0.9346 \| \| No log \| 44.0 \| 220 \| 0.6645 \| 0.5053 \| 0.6645 \| 0.8152 \| \| No log \| 45.0 \| 225 \| 0.7868 \| 0.4952 \| 0.7868 \| 0.8870 \| \| No log \| 46.0 \| 230 \| 0.8741 \| 0.4593 \| 0.8741 \| 0.9349 \| \| No log \| 47.0 \| 235 \| 0.6701 \| 0.5088 \| 0.6701 \| 0.8186 \| \| No log \| 48.0 \| 240 \| 1.0190 \| 0.4121 \| 1.0190 \| 1.0095 \| \| No log \| 49.0 \| 245 \| 0.6543 \| 0.5173 \| 0.6543 \| 0.8089 \| \| No log \| 50.0 \| 250 \| 0.7958 \| 0.4730 \| 0.7958 \| 0.8921 \| \| No log \| 51.0 \| 255 \| 0.7281 \| 0.4919 \| 0.7281 \| 0.8533 \| \| No log \| 52.0 \| 260 \| 0.6535 \| 0.5265 \| 0.6535 \| 0.8084 \| \| No log \| 53.0 \| 265 \| 0.7914 \| 0.4533 \| 0.7914 \| 0.8896 \| \| No log \| 54.0 \| 270 \| 0.6916 \| 0.4868 \| 0.6916 \| 0.8316 \| \| No log \| 55.0 \| 275 \| 0.6915 \| 0.5032 \| 0.6915 \| 0.8315 \| \| No log \| 56.0 \| 280 \| 0.8551 \| 0.4545 \| 0.8551 \| 0.9247 \| \| No log \| 57.0 \| 285 \| 0.7546 \| 0.4753 \| 0.7546 \| 0.8687 \| \| No log \| 58.0 \| 290 \| 0.6653 \| 0.5059 \| 0.6653 \| 0.8157 \| \| No log \| 59.0 \| 295 \| 0.8104 \| 0.4539 \| 0.8104 \| 0.9002 \| \| No log \| 60.0 \| 300 \| 0.7595 \| 0.4712 \| 0.7595 \| 0.8715 \| \| No log \| 61.0 \| 305 \| 0.6900 \| 0.4928 \| 0.6900 \| 0.8307 \| \| No log \| 62.0 \| 310 \| 0.7538 \| 0.4829 \| 0.7538 \| 0.8682 \| \| No log \| 63.0 \| 315 \| 0.6874 \| 0.4860 \| 0.6874 \| 0.8291 \| \| No log \| 64.0 \| 320 \| 0.6741 \| 0.5139 \| 0.6741 \| 0.8210 \| \| No log \| 65.0 \| 325 \| 0.6863 \| 0.5143 \| 0.6863 \| 0.8284 \| \| No log \| 66.0 \| 330 \| 0.6944 \| 0.5087 \| 0.6944 \| 0.8333 \| \| No log \| 67.0 \| 335 \| 0.7359 \| 0.4666 \| 0.7359 \| 0.8579 \| \| No log \| 68.0 \| 340 \| 0.6938 \| 0.5014 \| 0.6938 \| 0.8330 \| \| No log \| 69.0 \| 345 \| 0.6738 \| 0.5180 \| 0.6738 \| 0.8209 \| \| No log \| 70.0 \| 350 \| 0.6574 \| 0.5327 \| 0.6574 \| 0.8108 \| \| No log \| 71.0 \| 355 \| 0.6721 \| 0.5191 \| 0.6721 \| 0.8198 \| \| No log \| 72.0 \| 360 \| 0.6284 \| 0.5288 \| 0.6284 \| 0.7927 \| \| No log \| 73.0 \| 365 \| 0.7548 \| 0.4998 \| 0.7548 \| 0.8688 \| \| No log \| 74.0 \| 370 \| 0.6402 \| 0.5253 \| 0.6402 \| 0.8001 \| \| No log \| 75.0 \| 375 \| 0.7444 \| 0.4907 \| 0.7444 \| 0.8628 \| \| No log \| 76.0 \| 380 \| 0.6742 \| 0.5121 \| 0.6742 \| 0.8211 \| \| No log \| 77.0 \| 385 \| 0.6737 \| 0.5222 \| 0.6737 \| 0.8208 \| \| No log \| 78.0 \| 390 \| 0.7162 \| 0.5055 \| 0.7162 \| 0.8463 \| \| No log \| 79.0 \| 395 \| 0.7296 \| 0.4993 \| 0.7296 \| 0.8542 \| \| No log \| 80.0 \| 400 \| 0.6687 \| 0.5203 \| 0.6687 \| 0.8177 \| \| No log \| 81.0 \| 405 \| 0.6535 \| 0.5148 \| 0.6535 \| 0.8084 \| \| No log \| 82.0 \| 410 \| 0.7062 \| 0.4835 \| 0.7062 \| 0.8404 \| \| No log \| 83.0 \| 415 \| 0.6591 \| 0.5272 \| 0.6591 \| 0.8119 \| \| No log \| 84.0 \| 420 \| 0.6454 \| 0.5051 \| 0.6454 \| 0.8033 \| \| No log \| 85.0 \| 425 \| 0.6927 \| 0.4960 \| 0.6927 \| 0.8323 \| \| No log \| 86.0 \| 430 \| 0.6659 \| 0.5157 \| 0.6659 \| 0.8160 \| \| No log \| 87.0 \| 435 \| 0.6846 \| 0.4952 \| 0.6846 \| 0.8274 \| \| No log \| 88.0 \| 440 \| 0.7172 \| 0.4989 \| 0.7172 \| 0.8469 \| \| No log \| 89.0 \| 445 \| 0.6771 \| 0.5217 \| 0.6771 \| 0.8228 \| \| No log \| 90.0 \| 450 \| 0.7084 \| 0.4859 \| 0.7084 \| 0.8417 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
benemreseker/yenimodeldeneme	benemreseker	2025-03-08T04:41:18Z	0	0	null	[ "license:other", "region:us" ]	null	2025-03-08T04:04:11Z	--- 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 ---
FuseAI/FuseChat-Llama-3.1-8B-Instruct	FuseAI	2025-03-08T04:41:10Z	197	10	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:FuseAI/FuseChat-3.0-DPO-Data", "arxiv:2412.03187", "arxiv:2503.04222", "arxiv:2408.07990", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-20T11:02:51Z	--- datasets: - FuseAI/FuseChat-3.0-DPO-Data model-index: - name: FuseChat-Llama-3.1-8B-Instruct results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: wis-k/instruction-following-eval split: train args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 72.05 name: averaged accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=FuseAI%2FFuseChat-Llama-3.1-8B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: SaylorTwift/bbh split: test args: num_few_shot: 3 metrics: - type: acc_norm value: 30.85 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=FuseAI%2FFuseChat-Llama-3.1-8B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: lighteval/MATH-Hard split: test args: num_few_shot: 4 metrics: - type: exact_match value: 7.02 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=FuseAI%2FFuseChat-Llama-3.1-8B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa split: train args: num_few_shot: 0 metrics: - type: acc_norm value: 7.38 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=FuseAI%2FFuseChat-Llama-3.1-8B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 6.15 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=FuseAI%2FFuseChat-Llama-3.1-8B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 30.37 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=FuseAI%2FFuseChat-Llama-3.1-8B-Instruct name: Open LLM Leaderboard library_name: transformers pipeline_tag: text-generation license: apache-2.0 --- <p align="center" width="100%"> </p> <div id="top" align="center"> FuseChat-3.0: Preference Optimization for Implicit Model Fusion ----------------------------- <h4> \|<a href="https://arxiv.org/abs/2412.03187"> 📑 WRPO Paper </a> \| <a href="https://arxiv.org/pdf/2503.04222"> 📑 FuseChat-3.0 Paper </a> \| <a href="https://github.com/SLIT-AI/FuseChat-3.0"> 🐱 GitHub Repo </a> \| <a href="https://huggingface.co/FuseAI"> 🤗 Hugging Face </a> \| <a href="https://slit-ai.github.io/FuseChat-3.0/"> 🌐 Website </a> \| </h4> </div> <div align="center"> <img src="FuseChat-3.0.png" width=70%/> </div> We present FuseChat-3.0, a series of models crafted to enhance performance by integrating the strengths of multiple source LLMs into more compact target LLMs. To achieve this fusion, we utilized four powerful source LLMs: Gemma-2-27B-It, Mistral-Large-Instruct-2407, Qwen-2.5-72B-Instruct, and Llama-3.1-70B-Instruct. For the target LLMs, we employed three widely-used smaller models—Llama-3.1-8B-Instruct, Gemma-2-9B-It, and Qwen-2.5-7B-Instruct—along with two even more compact models—Llama-3.2-3B-Instruct and Llama-3.2-1B-Instruct. The implicit model fusion process involves a two-stage training pipeline comprising Supervised Fine-Tuning (SFT) to mitigate distribution discrepancies between target and source LLMs, and Direct Preference Optimization (DPO) for learning preferences from multiple source LLMs. The resulting FuseChat-3.0 models demonstrated substantial improvements in tasks related to general conversation, instruction following, mathematics, and coding. Notably, when Llama-3.1-8B-Instruct served as the target LLM, our fusion approach achieved an average improvement of 6.8 points across 14 benchmarks. Moreover, it showed significant improvements of 37.1 and 30.1 points on instruction-following test sets AlpacaEval-2 and Arena-Hard respectively. We have released the [FuseChat-3.0](https://huggingface.co/FuseAI) models and datasets on Huggingface. ## Overview Combining the strengths of multiple large language models (LLMs) represents a promising approach to enhance individual model capabilities. Model fusion is a technique that integrates the strengths of robust source LLMs into a target LLM. Previous iterations of the [FuseChat](https://arxiv.org/abs/2408.07990) series employed probabilistic distribution matrices generated by source models to transfer knowledge to target models. We refer to this method as explicit model fusion (EMF) because it involves a well-defined knowledge transfer process. While applicable to models with varying architectures and sizes, and without increasing memory overhead during inference, this approach presents notable challenges such as vocabulary alignment and the merging of distribution matrices from different LLMs. These issues complicate model fusion, reduce its efficiency, and may introduce noise and errors and affect the fusion results. FuseChat-3.0, however, takes a different approach by enhancing a single LLM through implicit learning from robust open-source LLMs, a process we term implicit model fusion (IMF). The concept of IMF has been widely utilized to improve the performance of weaker models. For instance, a weak model can be boosted through fine-tuning with outputs from stronger LLMs. Moreover, a reward model can be trained using outputs from various LLMs, enabling it to learn and capture the differences in capabilities between the LLMs. Zephyr further collects responses from multiple LLMs and ranks them with GPT-4 to obtain preference data for training the policy. Inspired by recent alignment techniques, we propose an IMF method to transfer the capabilities of source LLMs to a target LLM through preference optimization. Our IMF method follows a three-stage process aimed at effectively transferring capabilities from source LLMs to a target LLM. First, during dataset construction, we sample N responses from each of the source LLMs and annotate these responses using an external reward model. Second, in the supervised fine-tuning (SFT) stage, we fine-tune the target model using the best responses, which not only enhances the target model's capabilities but also helps mitigate the distributional gap between the source and target models. Finally, in the direct preference optimization (DPO) stage, we optimize the target model by using the best and worst responses from the source models as preference pairs, further enhancing the target model's performance. The complete pipeline will be detailed in the following paragraph. ## Dataset ### Prompt Selection Our datasets were designed to enhance model's instruction following, general conversation, mathematics, coding, and Chinese-language capabilities. We selected data from open-source community datasets, applying targeted filtering and preprocessing. Key datasets and filtering criteria included: - Instruction Following & General Conversation: Sourced from [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback), [Magpie-Pro-DPO-100K-v0.1](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-DPO-100K-v0.1), and [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2), excluding code and math data. - Mathematics: Selected from [OpenMathInstruct-2](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2), with nearly 52,000 unique samples. - Coding: Curated from [leetcode](https://huggingface.co/datasets/greengerong/leetcode) and [self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k), retaining prompts with test cases. - Chinese Language: Integrated [alpaca_gpt4_zh](https://huggingface.co/datasets/llamafactory/alpaca_gpt4_zh) and [Magpie-Qwen2-Pro-200K-Chinese](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese), filtering out code and math prompts to retain approximately 10,000 high-quality samples. ### Response Sampling For each dataset's prompts, we synthesized responses mainly from four different series of source models, specifically [Gemma-2-27b-It](https://huggingface.co/google/gemma-2-27b-it), [Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407), [Qwen-2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct), and [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct). - Instruction Following & General Conversation: We sampled each prompt five times from all the source models. - Mathematics: We retained the responses generated by Llama-3.1-405B-Instruct from the original dataset (OpenMathInstruct-2) and additionally sampled responses using [Qwen-2.5-Math-72B-Instruct](https://huggingface.co/Qwen/Qwen-2.5-Math-72B-Instruct). - Coding: We sampled each prompt eight times for all source models. - Chinese Language: We included single response sampled exclusively from Qwen-2.5-72B-Instruct. The sampling parameters for different models are detailed in Table below. <table class="js-sort-table table hidden"> <tr> <td class="js-sort-string"><strong>Source LLMs</strong></td> <td class="js-sort-string"><strong>Sampling Params</strong></td> </tr> <tr> <td>Gemma-2-27b-It</td> <td>Temp 0.8 Top-p 0.95</td> </tr> <tr> <td>Mistral-Large-Instruct-2407</td> <td>Temp 0.8 Top-p 0.95</td> </tr> <tr> <td>Qwen-2.5-(Math)-72B-Instruct</td> <td>Temp 0.7 Top-p 0.8 Repetition penalty 1.05</td> </tr> <tr> <td>Llama-3.1-70B-Instruct</td> <td>Temp 0.8 Top-p 0.95</td> </tr> </table> ### Data Construction Unlike the original approach in [WRPO](https://arxiv.org/abs/2412.03187), which constructs preference pairs from target model responses and treats source model responses as additional positive samples, our research in mathematics and coding domains revealed that sampling from multiple source models yields more and higher-quality preference pair data. Based on this insight, FuseChat-3.0 leverages the best and worst response pairs generated by source models as preference pairs to optimize the target model. This refined approach not only preserves the core advantages of implicit model fusion but also results in a more streamlined and practical implementation, making it particularly well-suited for real-world applications within the open-source community. - Instruction Following: To assign RM scores to the five responses generated by each source model, we employed [ArmoRM](https://huggingface.co/RLHFlow/ArmoRM-Llama3-8B-v0.1) for annotation. We then divided the annotated data into SFT and DPO datasets using a 4:6 ratio. For the SFT phase, we selected the responses with the highest RM scores. During the DPO phase, we paired responses from the same source model, designating those with the highest RM scores as positive samples and those with the lowest RM scores as negative samples. We ensured that the RM score difference between the positive and negative samples in each pair ranged from 0.01 to 0.1. - Mathematics: We initially annotated the responses from all source models for correctness by comparing them with the gold labels and evaluating them using the RM scores provided by ArmoRM. We then strategically divided the dataset into SFT phase and DPO phase. In the SFT phase, we incorporated responses that were correct and had the highest RM scores. This selection ensured that the fine-tuning process was based on high-quality responses that aligned closely with the desired outcomes. For the DPO phase, we constructed paired samples from the same source model. The positive samples consisted of correct answers with the highest RM scores, while the negative samples were incorrect answers with the lowest RM scores. To ensure meaningful comparisons during optimization, we maintained an RM score differential between positive and negative pairs within the range of 0.01 to 0.1. - Coding: We employed a dual-scoring system comprising correctness scores and RM scores for coding evaluation. The correctness scores assessed whether the code passed both static analysis and test cases, ensuring functional accuracy. The RM scores were used for preference evaluation, gauging the quality of responses based on predefined criteria. During the SFT phase, we included responses that not only passed all test cases but also achieved the highest RM scores. This selection ensured that the model was fine-tuned on exemplary code that met both correctness and preference standards. In the DPO phase, we contrasted positive samples—high-scoring responses that passed the tests—with negative samples—low-scoring responses that failed the tests. This comparison aimed to optimize the model's ability to prefer higher-quality code during training. We excluded any instances where all model responses failed to meet the testing criteria. This exclusion was necessary to maintain the integrity of the evaluation process, as such cases did not provide meaningful data for assessing and improving the model's performance. - Chinese: We exclusively utilized responses sampled from Qwen-2.5-72B-Instruct during the SFT phase, due to its strong performance in the Chinese language. Our final dataset comprised 158,667 total entries, with 94,539 entries for the SFT phase and 64,128 preference pairs for the DPO phase. The overall composition of the datasets is shown below. <table class="js-sort-table table hidden"> <tr> <td class="js-sort-string"><strong>Dataset</strong></td> <td class="js-sort-number"><strong>Total Count</strong></td> <td class="js-sort-number"><strong>SFT Count</strong></td> <td class="js-sort-number"><strong>DPO Count</strong></td> <td class="js-sort-string"><strong>Category</strong></td> </tr> <tr> <td><a href="https://huggingface.co/datasets/openbmb/UltraFeedback" target="_blank">UltraFeedback</a></td> <td>51098</td> <td>20439</td> <td>30659</td> <td>Instruction following</td> </tr> <tr> <td><a href="https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-DPO-100K-v0.1" target="_blank">Magpie-Pro-DPO</a></td> <td>20374</td> <td>8149</td> <td>12225</td> <td>Instruction following</td> </tr> <tr> <td><a href="https://huggingface.co/datasets/nvidia/HelpSteer2" target="_blank">HelpSteer2</a></td> <td>9435</td> <td>3774</td> <td>5661</td> <td>Instruction following</td> </tr> <tr> <td><a href="https://huggingface.co/datasets/nvidia/OpenMathInstruct-2" target="_blank">OpenMathInstruct-2</a></td> <td>51803</td> <td>40188</td> <td>11615</td> <td>Mathematics</td> </tr> <tr> <td><a href="https://huggingface.co/datasets/greengerong/leetcode" target="_blank">leetcode</a></td> <td>3113</td> <td>1877</td> <td>1236</td> <td>Coding</td> </tr> <tr> <td><a href="https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k" target="_blank">self-oss-instruct-sc2</a></td> <td>12892</td> <td>10160</td> <td>2732</td> <td>Coding</td> </tr> <tr> <td><a href="https://huggingface.co/datasets/llamafactory/alpaca_gpt4_zh" target="_blank">alpaca_gpt4_zh</a></td> <td>2471</td> <td>2471</td> <td>0</td> <td>Chinese Language</td> </tr> <tr> <td><a href="https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese" target="_blank">Magpie-Qwen2-Pro</a></td> <td>7481</td> <td>7481</td> <td>0</td> <td>Chinese Language</td> </tr> <tr> <td><strong>Total</strong></td> <td>158667</td> <td>94539</td> <td>64128</td> <td>All</td> </tr> </table> ## Training The implicit model fusion process involves a two-stage training pipeline comprising Supervised Fine-Tuning (SFT) to mitigate distribution discrepancies between target and source LLMs, and Direct Preference Optimization (DPO) for learning preferences from multiple source LLMs. ### SFT We used [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory) as our fine-tuning library. For all target models, we fine-tuned for 3 epochs, with a batch size of 128 and a maximum sequence length of 2048 tokens. A cosine learning rate schedule with a warmup ratio of 0.1 is employed. Different models' learning rates are shown in the table below. <table class="js-sort-table table hidden"> <tr> <td class="js-sort-string"><strong>Target Models</strong></td> <td class="js-sort-string"><strong>Learning rate</strong></td> </tr> <tr> <td>Llama-3.1-8B-Instruct</td> <td>5e-6</td> </tr> <tr> <td>Qwen-2.5-7B-Instruct</td> <td>2e-6</td> </tr> <tr> <td>Gemma-2-9B-It</td> <td>2e-6</td> </tr> <tr> <td>Llama-3.2-(1/3)B-Instruct</td> <td>5e-6</td> </tr> </table> ### DPO We used [alignment-handbook](https://github.com/huggingface/alignment-handbook) as our DPO training library. For all Target SFT models, we trained for 1 epoch, set maximum sequence length to 2048, used cosine learning rate with a warmup ratio of 0.1. We saved checkpoints every 100 steps and selected the best from the last two checkpoints. For Llama-3.1 and Llama-3.2 series models, we introduced length normalization in DPO training, as shown in the formula below. ![Length Normalized DPO Formula](https://latex.codecogs.com/svg.image?\mathcal{L}_{\text{LN-DPO}}=-\log\sigma\left(\frac{\beta}{\|y_w\|}\log\frac{\pi_\theta(y_w\|x)}{\pi_{\text{ref}}(y_w\|x)}-\frac{\beta}{\|y_l\|}\log\frac{\pi_\theta(y_l\|x)}{\pi_{\text{ref}}(y_l\|x)}\right)) Different models' hyperparameters are shown in the table below. <table class="js-sort-table table hidden"> <tr> <td class="js-sort-string"><strong>Target SFT Models</strong></td> <td class="js-sort-string"><strong>Learning rate</strong></td> <td class="js-sort-string"><strong>β</strong></td> <td class="js-sort-string"><strong>Length normalize</strong></td> </tr> <tr> <td>FuseChat-Llama-3.1-8B-SFT</td> <td>8e-7</td> <td>10</td> <td>Yes</td> </tr> <tr> <td>FuseChat-Qwen-2.5-7B-SFT</td> <td>3e-7</td> <td>0.01</td> <td>No</td> </tr> <tr> <td>FuseChat-Gemma-2-9B-SFT</td> <td>5e-7</td> <td>0.01</td> <td>No</td> </tr> <tr> <td>FuseChat-Llama-3.2-(1/3)B-SFT</td> <td>1e-6</td> <td>10</td> <td>Yes</td> </tr> </table> ## Evaluation The evaluation of instruction-tuned models mainly focuses on the model performance of instruction following, natural language understanding, general question answering, reasoning, mathematics, coding, etc. For the evaluation of FuseChat-3.0, we include 14 benchmarks and organize them into four categories: - Instruction Following Tasks: AlpacaEval-2, Arena-Hard, MTbench, AlignBench v1.1 (Chinese). - General Tasks: LiveBench-0831, MMLU-Pro, MMLU-redux, GPQA-Diamond. - Mathematics Tasks: GSM8K, MATH, AMC 23. - Coding Tasks: HumanEval, MBPP, LiveCodeBench 2408-2411. We include more details and release our evaluation code at [FuseEval](https://github.com/SLIT-AI/FuseChat-3.0/FuseEval). The evaluation results of five series fused models are as follows, showing that our FuseChat-3.0 models achieved varying degrees of improvement across different target models. When selecting Llama-3.1-8B-Instruct as the target model, our fusion model FuseChat-Llama-3.1-8B-Instruct achieved an average performance improvement of 6.8 points across 14 benchmarks. Notably, it showed significant improvements of 37.1 and 30.1 points on instruction-following test sets AlpacaEval-2 and Arena-Hard respectively. Additionally, FuseChat-Llama-3.1-8B-Instruct outperformed AllenAI's recently released Llama-3.1-Tulu-3-8B model on all benchmarks except GSM8K and GPQA-Diamond. All these results demonstrate the effectiveness and success of FuseChat-3.0. ### FuseChat-Llama-3.1-8B-Instruct Performance <table class="js-sort-table table hidden"> <tr> <td class="js-sort-string"><strong>Benchmarks</strong></td> <td class="js-sort-string"><strong>Llama-3.1-8B-Instruct</strong></td> <td class="js-sort-string"><strong>Llama-3.1-Tulu-3-8B</strong></td> <td class="js-sort-string"><strong>FuseChat-Llama-3.1-8B-SFT</strong></td> <td class="js-sort-string"><strong>FuseChat-Llama-3.1-8B-Instruct</strong></td> </tr> <tr> <td style="white-space: nowrap;">AlpacaEval-2 (LC %)</td> <td>28.3</td> <td>33.4</td> <td>41.3</td> <td><strong>65.4</strong></td> </tr> <tr> <td>Arena-Hard (WR %)</td> <td>28.1</td> <td>45.6</td> <td>38.7</td> <td><strong>58.2</strong></td> </tr> <tr> <td>MT-Bench</td> <td>8.4</td> <td>8.3</td> <td>8.5</td> <td><strong>9.0</strong></td> </tr> <tr> <td>AlignBench v1.1</td> <td>4.6</td> <td>6.2</td> <td>6.3</td> <td><strong>6.7</strong></td> </tr> <tr> <td>GSM8K</td> <td>85.9</td> <td><strong>88.6</strong></td> <td>87.0</td> <td>88.0</td> </tr> <tr> <td>MATH</td> <td>50.7</td> <td>47.5</td> <td>54.7</td> <td><strong>55.2</strong></td> </tr> <tr> <td>AMC 23</td> <td>25.0</td> <td>25.0</td> <td>30.0</td> <td><strong>37.5</strong></td> </tr> <tr> <td>LiveBench 0831</td> <td>27.6</td> <td>30.1</td> <td>30.2</td> <td><strong>32.0</strong></td> </tr> <tr> <td>MMLU-Pro</td> <td><strong>50.0</strong></td> <td>42.9</td> <td>47.8</td> <td>49.2</td> </tr> <tr> <td>MMLU-redux</td> <td>67.2</td> <td>66.3</td> <td>68.4</td> <td><strong>69.2</strong></td> </tr> <tr> <td>GPQA-Diamond</td> <td>33.8</td> <td>35.9</td> <td><strong>37.9</strong></td> <td>34.9</td> </tr> <tr> <td>HumanEval</td> <td>69.5</td> <td>66.5</td> <td>69.5</td> <td><strong>71.3</strong></td> </tr> <tr> <td>MBPP</td> <td><strong>75.4</strong></td> <td>56.3</td> <td>71.4</td> <td>72.0</td> </tr> <tr> <td>LiveCodeBench<br>2408-2411</td> <td>12.3</td> <td>10.6</td> <td>12.6</td> <td><strong>13.1</strong></td> </tr> <tr> <td>Average</td> <td>40.5</td> <td>40.2</td> <td>43.2</td> <td><strong>47.3</strong></td> </tr> </table> ## Citation ``` @inproceedings{yang2025weightedreward, title={Weighted-Reward Preference Optimization for Implicit Model Fusion}, author={Ziyi Yang and Fanqi Wan and Longguang Zhong and Tianyuan Shi and Xiaojun Quan}, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, url={https://openreview.net/forum?id=fq24pEb8SL} } @article{yang2025fusechat, title={FuseChat-3.0: Preference Optimization Meets Heterogeneous Model Fusion}, author={Ziyi Yang and Fanqi Wan and Longguang Zhong and Canbin Huang and Guosheng Liang and Xiaojun Quan}, journal={arXiv preprint arXiv:2503.04222}, year={2025}, } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/FuseAI__FuseChat-Llama-3.1-8B-Instruct-details)! Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=FuseAI%2FFuseChat-Llama-3.1-8B-Instruct&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! \| Metric \|Value (%)\| \|-------------------\|--------:\| \|Average \| 25.64\| \|IFEval (0-Shot) \| 72.05\| \|BBH (3-Shot) \| 30.85\| \|MATH Lvl 5 (4-Shot)\| 7.02\| \|GPQA (0-shot) \| 7.38\| \|MuSR (0-shot) \| 6.15\| \|MMLU-PRO (5-shot) \| 30.37\|
Alphatao/8ea1178f-7714-4a61-9d8c-478a84876cab	Alphatao	2025-03-08T04:38:36Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2-0.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-03-07T19:49:33Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-0.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 8ea1178f-7714-4a61-9d8c-478a84876cab 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/Qwen2-0.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f57d828564838a69_train_data.json ds_type: json format: custom path: /workspace/input_data/f57d828564838a69_train_data.json type: field_input: chosen field_instruction: source field_output: reject format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null device_map: ? '' : 0,1,2,3,4,5,6,7 early_stopping_patience: 2 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null flash_attention: true gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: false hub_model_id: Alphatao/8ea1178f-7714-4a61-9d8c-478a84876cab hub_repo: null hub_strategy: null hub_token: null learning_rate: 0.0002 load_best_model_at_end: true load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.3 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 12288 micro_batch_size: 4 mlflow_experiment_name: /tmp/f57d828564838a69_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 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 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.00648773493710141 wandb_entity: null wandb_mode: online wandb_name: 64067547-49fe-44ed-9f15-08d6f4ccfab7 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 64067547-49fe-44ed-9f15-08d6f4ccfab7 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8ea1178f-7714-4a61-9d8c-478a84876cab This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6288 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - 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: 12288 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 1.426 \| 0.0000 \| 1 \| 1.3817 \| \| 1.0913 \| 0.0042 \| 100 \| 0.8642 \| \| 0.7442 \| 0.0084 \| 200 \| 0.8486 \| \| 0.7381 \| 0.0125 \| 300 \| 0.8412 \| \| 0.9318 \| 0.0167 \| 400 \| 0.8342 \| \| 0.9462 \| 0.0209 \| 500 \| 0.8278 \| \| 0.8658 \| 0.0251 \| 600 \| 0.8247 \| \| 1.2001 \| 0.0293 \| 700 \| 0.8234 \| \| 0.7767 \| 0.0334 \| 800 \| 0.8127 \| \| 1.1022 \| 0.0376 \| 900 \| 0.8088 \| \| 0.7616 \| 0.0418 \| 1000 \| 0.8136 \| \| 0.691 \| 0.0460 \| 1100 \| 0.8051 \| \| 0.814 \| 0.0502 \| 1200 \| 0.8078 \| \| 0.8866 \| 0.0543 \| 1300 \| 0.8027 \| \| 0.8338 \| 0.0585 \| 1400 \| 0.8037 \| \| 0.8899 \| 0.0627 \| 1500 \| 0.8008 \| \| 0.729 \| 0.0669 \| 1600 \| 0.7922 \| \| 0.621 \| 0.0710 \| 1700 \| 0.7919 \| \| 0.7033 \| 0.0752 \| 1800 \| 0.7894 \| \| 0.8998 \| 0.0794 \| 1900 \| 0.7898 \| \| 0.8322 \| 0.0836 \| 2000 \| 0.7842 \| \| 0.9954 \| 0.0878 \| 2100 \| 0.7866 \| \| 0.9642 \| 0.0919 \| 2200 \| 0.7816 \| \| 0.7502 \| 0.0961 \| 2300 \| 0.7792 \| \| 0.9017 \| 0.1003 \| 2400 \| 0.7779 \| \| 0.8775 \| 0.1045 \| 2500 \| 0.7730 \| \| 0.8333 \| 0.1087 \| 2600 \| 0.7720 \| \| 1.1947 \| 0.1128 \| 2700 \| 0.7735 \| \| 0.7781 \| 0.1170 \| 2800 \| 0.7689 \| \| 0.975 \| 0.1212 \| 2900 \| 0.7695 \| \| 0.8731 \| 0.1254 \| 3000 \| 0.7673 \| \| 0.7944 \| 0.1296 \| 3100 \| 0.7640 \| \| 0.6546 \| 0.1337 \| 3200 \| 0.7609 \| \| 0.5772 \| 0.1379 \| 3300 \| 0.7556 \| \| 0.9376 \| 0.1421 \| 3400 \| 0.7527 \| \| 0.6594 \| 0.1463 \| 3500 \| 0.7574 \| \| 0.7937 \| 0.1505 \| 3600 \| 0.7506 \| \| 0.6651 \| 0.1546 \| 3700 \| 0.7490 \| \| 0.787 \| 0.1588 \| 3800 \| 0.7461 \| \| 1.0014 \| 0.1630 \| 3900 \| 0.7435 \| \| 0.7214 \| 0.1672 \| 4000 \| 0.7428 \| \| 0.7854 \| 0.1713 \| 4100 \| 0.7411 \| \| 0.7552 \| 0.1755 \| 4200 \| 0.7411 \| \| 0.715 \| 0.1797 \| 4300 \| 0.7366 \| \| 0.6976 \| 0.1839 \| 4400 \| 0.7356 \| \| 0.9447 \| 0.1881 \| 4500 \| 0.7350 \| \| 0.8067 \| 0.1922 \| 4600 \| 0.7292 \| \| 1.0411 \| 0.1964 \| 4700 \| 0.7274 \| \| 0.643 \| 0.2006 \| 4800 \| 0.7252 \| \| 0.7939 \| 0.2048 \| 4900 \| 0.7247 \| \| 0.6452 \| 0.2090 \| 5000 \| 0.7205 \| \| 0.7369 \| 0.2131 \| 5100 \| 0.7212 \| \| 0.6581 \| 0.2173 \| 5200 \| 0.7159 \| \| 0.775 \| 0.2215 \| 5300 \| 0.7138 \| \| 0.6879 \| 0.2257 \| 5400 \| 0.7118 \| \| 0.8093 \| 0.2299 \| 5500 \| 0.7093 \| \| 0.7375 \| 0.2340 \| 5600 \| 0.7127 \| \| 0.6826 \| 0.2382 \| 5700 \| 0.7046 \| \| 0.9633 \| 0.2424 \| 5800 \| 0.7016 \| \| 0.8521 \| 0.2466 \| 5900 \| 0.7043 \| \| 0.7054 \| 0.2508 \| 6000 \| 0.6990 \| \| 0.6763 \| 0.2549 \| 6100 \| 0.6957 \| \| 0.836 \| 0.2591 \| 6200 \| 0.6942 \| \| 0.6314 \| 0.2633 \| 6300 \| 0.6923 \| \| 0.7427 \| 0.2675 \| 6400 \| 0.6884 \| \| 0.5987 \| 0.2717 \| 6500 \| 0.6875 \| \| 0.6365 \| 0.2758 \| 6600 \| 0.6855 \| \| 0.6329 \| 0.2800 \| 6700 \| 0.6849 \| \| 0.6765 \| 0.2842 \| 6800 \| 0.6812 \| \| 0.6983 \| 0.2884 \| 6900 \| 0.6800 \| \| 0.7398 \| 0.2925 \| 7000 \| 0.6775 \| \| 0.4994 \| 0.2967 \| 7100 \| 0.6757 \| \| 0.6947 \| 0.3009 \| 7200 \| 0.6750 \| \| 0.6398 \| 0.3051 \| 7300 \| 0.6719 \| \| 0.7557 \| 0.3093 \| 7400 \| 0.6715 \| \| 0.7419 \| 0.3134 \| 7500 \| 0.6675 \| \| 0.8206 \| 0.3176 \| 7600 \| 0.6647 \| \| 0.532 \| 0.3218 \| 7700 \| 0.6639 \| \| 0.6014 \| 0.3260 \| 7800 \| 0.6642 \| \| 0.7216 \| 0.3302 \| 7900 \| 0.6612 \| \| 0.6612 \| 0.3343 \| 8000 \| 0.6572 \| \| 0.7312 \| 0.3385 \| 8100 \| 0.6561 \| \| 0.5502 \| 0.3427 \| 8200 \| 0.6556 \| \| 0.7803 \| 0.3469 \| 8300 \| 0.6531 \| \| 0.3768 \| 0.3511 \| 8400 \| 0.6518 \| \| 0.7379 \| 0.3552 \| 8500 \| 0.6514 \| \| 0.5688 \| 0.3594 \| 8600 \| 0.6522 \| \| 0.7844 \| 0.3636 \| 8700 \| 0.6492 \| \| 0.7967 \| 0.3678 \| 8800 \| 0.6480 \| \| 0.6085 \| 0.3720 \| 8900 \| 0.6469 \| \| 0.5959 \| 0.3761 \| 9000 \| 0.6460 \| \| 0.7083 \| 0.3803 \| 9100 \| 0.6445 \| \| 0.9192 \| 0.3845 \| 9200 \| 0.6426 \| \| 0.8767 \| 0.3887 \| 9300 \| 0.6406 \| \| 0.6501 \| 0.3928 \| 9400 \| 0.6397 \| \| 0.6942 \| 0.3970 \| 9500 \| 0.6384 \| \| 0.5516 \| 0.4012 \| 9600 \| 0.6378 \| \| 0.563 \| 0.4054 \| 9700 \| 0.6366 \| \| 0.7784 \| 0.4096 \| 9800 \| 0.6359 \| \| 0.5832 \| 0.4137 \| 9900 \| 0.6357 \| \| 0.9015 \| 0.4179 \| 10000 \| 0.6351 \| \| 0.9016 \| 0.4221 \| 10100 \| 0.6342 \| \| 0.7122 \| 0.4263 \| 10200 \| 0.6330 \| \| 0.6701 \| 0.4305 \| 10300 \| 0.6330 \| \| 0.7161 \| 0.4346 \| 10400 \| 0.6322 \| \| 0.5625 \| 0.4388 \| 10500 \| 0.6313 \| \| 0.6608 \| 0.4430 \| 10600 \| 0.6314 \| \| 0.6945 \| 0.4472 \| 10700 \| 0.6308 \| \| 0.5166 \| 0.4514 \| 10800 \| 0.6304 \| \| 0.5635 \| 0.4555 \| 10900 \| 0.6298 \| \| 0.7577 \| 0.4597 \| 11000 \| 0.6296 \| \| 0.6303 \| 0.4639 \| 11100 \| 0.6292 \| \| 0.5529 \| 0.4681 \| 11200 \| 0.6288 \| \| 0.5988 \| 0.4723 \| 11300 \| 0.6290 \| \| 0.4636 \| 0.4764 \| 11400 \| 0.6288 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Prog25/Stock_analysis	Prog25	2025-03-08T04:36:59Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-03-08T04:36:59Z	--- license: artistic-2.0 ---
saiteki-kai/Llama-Guard-3-1B-SFT-CLS-02	saiteki-kai	2025-03-08T04:36:36Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-Guard-3-8B", "base_model:finetune:meta-llama/Llama-Guard-3-8B", "endpoints_compatible", "region:us" ]	null	2025-03-08T04:35:54Z	--- base_model: meta-llama/Llama-Guard-3-8B library_name: transformers model_name: saiteki-kai/Llama-Guard-3-8B-SFT-BeaverTails tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for saiteki-kai/Llama-Guard-3-8B-SFT-BeaverTails This model is a fine-tuned version of [meta-llama/Llama-Guard-3-8B](https://huggingface.co/meta-llama/Llama-Guard-3-8B). 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="saiteki-kai/Llama-Guard-3-1B-SFT-CLS-02", 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/giuseppe-magazzu/llama-guard-finetuning/runs/mb1hirti) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.47.0 - Pytorch: 2.5.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
TareksTesting/Progenitor-Chrome-LLaMa-70B	TareksTesting	2025-03-08T04:30:26Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:TareksLab/TestMergePart1", "base_model:merge:TareksLab/TestMergePart1", "base_model:TareksLab/TestMergePart2", "base_model:merge:TareksLab/TestMergePart2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-08T04:13:11Z	--- base_model: - TareksLab/TestMergePart2 - TareksLab/TestMergePart1 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 [NearSwap](https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001) merge method using [TareksLab/TestMergePart2](https://huggingface.co/TareksLab/TestMergePart2) as a base. ### Models Merged The following models were included in the merge: * [TareksLab/TestMergePart1](https://huggingface.co/TareksLab/TestMergePart1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TareksLab/TestMergePart2 - model: TareksLab/TestMergePart1 merge_method: nearswap base_model: TareksLab/TestMergePart2 parameters: t: - value: 0.0001 dtype: bfloat16 ```
pai123/DeepSeek-R1-distilled_model_2	pai123	2025-03-08T04:20:49Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-08T04:20:28Z	--- 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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Yuhan123/mistral-7b-wildchat-semantics_var_4	Yuhan123	2025-03-08T04:20:01Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-08T04:15:17Z	--- 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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aigdat/Qwen2.5-Coder-quantized-asym4-g128-onnx	aigdat	2025-03-08T04:17:28Z	0	0	null	[ "onnx", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:quantized:Qwen/Qwen2.5-Coder-7B-Instruct", "region:us" ]	null	2025-03-08T03:57:49Z	--- base_model: - Qwen/Qwen2.5-Coder-7B-Instruct ---

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