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fffanx/Llama-3.2-1B-Instruct-GRPO-agent9_E4	fffanx	2025-05-04T21:53:01Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T21:52:32Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent9_E4 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent9_E4 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent9_E4", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ludast-original/QA_physics_adapted_llama_3.2_3b	ludast-original	2025-05-04T21:52:46Z	0	0	transformers	[ "transformers", "safetensors", "llama", "feature-extraction", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-04T19:26:50Z	--- base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: ludast-original - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fffanx/Llama-3.2-1B-Instruct-GRPO-agent6_E4	fffanx	2025-05-04T21:51:24Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T21:50:55Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent6_E4 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent6_E4 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent6_E4", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
felixZzz/wmixnoBoolean-orz-ours-d100-len5120-0427T17_47_21-step_05248	felixZzz	2025-05-04T21:51:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T21:43:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fffanx/Llama-3.2-1B-Instruct-GRPO-agent5_E4	fffanx	2025-05-04T21:50:51Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T21:50:23Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent5_E4 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent5_E4 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent5_E4", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent3_E4	fffanx	2025-05-04T21:49:45Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T21:49:15Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent3_E4 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent3_E4 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent3_E4", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent2_E4	fffanx	2025-05-04T21:49:11Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T21:48:42Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent2_E4 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent2_E4 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent2_E4", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent1_E4	fffanx	2025-05-04T21:48:39Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T21:48:10Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent1_E4 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent1_E4 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent1_E4", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MrDevolver/mergekit-dare_ties-ftxcrqn-Q4_K_S-GGUF	MrDevolver	2025-05-04T21:45:21Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:mergekit-community/mergekit-dare_ties-ftxcrqn", "base_model:quantized:mergekit-community/mergekit-dare_ties-ftxcrqn", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T21:44:16Z	--- base_model: mergekit-community/mergekit-dare_ties-ftxcrqn library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrDevolver/mergekit-dare_ties-ftxcrqn-Q4_K_S-GGUF This model was converted to GGUF format from [`mergekit-community/mergekit-dare_ties-ftxcrqn`](https://huggingface.co/mergekit-community/mergekit-dare_ties-ftxcrqn) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mergekit-community/mergekit-dare_ties-ftxcrqn) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo MrDevolver/mergekit-dare_ties-ftxcrqn-Q4_K_S-GGUF --hf-file mergekit-dare_ties-ftxcrqn-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrDevolver/mergekit-dare_ties-ftxcrqn-Q4_K_S-GGUF --hf-file mergekit-dare_ties-ftxcrqn-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo MrDevolver/mergekit-dare_ties-ftxcrqn-Q4_K_S-GGUF --hf-file mergekit-dare_ties-ftxcrqn-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrDevolver/mergekit-dare_ties-ftxcrqn-Q4_K_S-GGUF --hf-file mergekit-dare_ties-ftxcrqn-q4_k_s.gguf -c 2048 ```
felixZzz/wlen6_61k-orz-ours-d1-len3000-0428T03_05_50-step_00528	felixZzz	2025-05-04T21:41:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T21:33:15Z	--- 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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glif-loradex-trainer/Swap_agrawal14_kuki_greens	glif-loradex-trainer	2025-05-04T21:40:56Z	0	0	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	2025-05-04T21:40:37Z	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1746394772127__000001500_0.jpg text: Crying sitting under tree$wap_greenz - output: url: samples/1746394797318__000001500_1.jpg text: A girl in corner and in background text reads "OpenAI or Google" $wap_greenz - output: url: samples/1746394822252__000001500_2.jpg text: Listening music on headphones $wap_greenz base_model: black-forest-labs/FLUX.1-dev trigger: "$wap_greenz" instance_prompt: "$wap_greenz" license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # kuki_greens Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `Swap_agrawal14`. <Gallery /> ## Trigger words You should use `$wap_greenz` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/Swap_agrawal14_kuki_greens/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
fffanx/Llama-3.2-1B-Instruct-GRPO-agent18_E3	fffanx	2025-05-04T21:40:49Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:58:03Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent18_E3 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent18_E3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent18_E3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent17_E3	fffanx	2025-05-04T21:40:16Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:57:31Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent17_E3 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent17_E3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent17_E3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent12_E3	fffanx	2025-05-04T21:37:38Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:54:50Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent12_E3 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent12_E3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent12_E3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent11_E3	fffanx	2025-05-04T21:37:06Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:54:19Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent11_E3 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent11_E3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent11_E3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent9_E3	fffanx	2025-05-04T21:36:03Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:53:16Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent9_E3 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent9_E3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent9_E3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Benjaminpwh/llama_1.3_200	Benjaminpwh	2025-05-04T21:34:37Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "endpoints_compatible", "region:us" ]	null	2025-05-04T20:39:09Z	--- base_model: unsloth/llama-3-8b-instruct-bnb-4bit library_name: transformers model_name: llama_1.3_200 tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for llama_1.3_200 This model is a fine-tuned version of [unsloth/llama-3-8b-instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-instruct-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Benjaminpwh/llama_1.3_200", 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/benpong-university-of-washington/huggingface/runs/xgwqt5f7) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent6_E3	fffanx	2025-05-04T21:34:27Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:51:41Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent6_E3 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent6_E3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent6_E3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
felixZzz/wmixnoBoolean-orz-ours-d100-len5120-0427T17_47_21-step_04224	felixZzz	2025-05-04T21:34:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T21:26:21Z	--- 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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Smilyai-labs/Sam-flash-mini-v1	Smilyai-labs	2025-05-04T21:34:04Z	0	1	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "distilgpt2", "smilyai", "sam-flash", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T20:44:33Z	--- pipeline_tag: text-generation library_name: transformers language: en license: mit tags: - text-generation - gpt2 - distilgpt2 - smilyai - sam-flash --- # Sam-flash-mini-v1 Sam-flash-mini-v1 is a compact and efficient text generation model fine-tuned from [DistilGPT2](https://huggingface.co/distilgpt2) by Smilyai Labs. Designed for creative writing, storytelling, and rapid prototyping, this model offers a balance between performance and resource efficiency. ## Model Details - Base Model: [DistilGPT2](https://huggingface.co/distilgpt2) - Architecture: GPT2 - Language: English - License: MIT - Developed by: [Smilyai Labs](https://huggingface.co/Smilyai-labs) - ## Try it out! You can test this model in an interactive app: [Launch the Sam Flash Story Generator](https://huggingface.co/spaces/Smilyai-labs/Sam-flash-mini-v1-demo) ## Usage You can easily load and use the model with the Hugging Face Transformers library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Smilyai-labs/Sam-flash-mini-v1") model = AutoModelForCausalLM.from_pretrained("Smilyai-labs/Sam-flash-mini-v1") input_text = "Once upon a time," inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
fffanx/Llama-3.2-1B-Instruct-GRPO-agent5_E3	fffanx	2025-05-04T21:33:55Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:51:09Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent5_E3 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent5_E3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent5_E3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent3_E3	fffanx	2025-05-04T21:32:50Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:50:06Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent3_E3 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent3_E3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent3_E3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
felixZzz/wlen6_61k-orz-ours-d1-len3000-0428T03_05_50-step_00464	felixZzz	2025-05-04T21:32:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T21:24:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
fffanx/Llama-3.2-1B-Instruct-GRPO-agent2_E3	fffanx	2025-05-04T21:32:16Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:49:33Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent2_E3 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent2_E3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent2_E3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent0_E3	fffanx	2025-05-04T21:31:12Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T02:21:16Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent0_E3 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent0_E3 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent0_E3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
rusuanjun/Reinforce-CartPole-v1	rusuanjun	2025-05-04T21:29:15Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-04T20:30:18Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . 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
RikkaBotan/Nova-Nox-Neural-Network_test_127m_0.5B_fineweb	RikkaBotan	2025-05-04T21:28:36Z	0	0	null	[ "text-generation-inference", "Nova-Nox-Neural-Network", "text-generation", "en", "dataset:HuggingFaceFW/fineweb", "license:mit", "region:us" ]	text-generation	2025-05-03T21:46:23Z	--- license: mit datasets: - HuggingFaceFW/fineweb language: - en tags: - text-generation-inference - Nova-Nox-Neural-Network - text-generation --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6629ba7d59854b02da014f64/tbKYAzmWeheHGotY2SYWp.png) # Nova-Nox-Neural-Network All images used are created by Rikka Botan. Flash Technical Report (Japanese) https://qiita.com/peony_snow/items/8ae4e83b8de5c342ab62 ## About N4: Nova-Nox-Neural-Network is a mechanism designed to enhance accuracy by integrating the self-referential capabilities of the Attention mechanism with a simplified version of the selective copying architecture inspired by S6, thereby enabling the acquisition of a more expressive QK matrix. The architecture employs ASGG: Adaptive Swish-GELU Gating as the activation function within its MLP components, contributing to richer representational capacity. Furthermore, it utilizes DyT for normalization, which improves computational efficiency. * ### Key Features 1. A simplified Selective copying mechanism 2. ASGG: Adaptive Swish-GELU Gating + MLP 3. DyT: Dynamic Tanh Normalization * ## training result ```bash Training Setting Parameters：127M (vocab_size=32768, hidden_size=768, inter_size=1536, heads=6, layers=18) Optimizer: AdamW (lr=6e-4, betas=(0.9, 0.95), eps=1e-9, weight_decay=1e-1, warmup_steps=2000) batch size: 8 accumlation: 16 dataset: fineweb (0.5B token, 1 epoch: 976 steps) max length: 512 dtype: bfloat16 ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6629ba7d59854b02da014f64/sWVBZjeYyZfosYgN2t3on.png) ## Implementation and License This repository is official pure pytorch implementation. Licensed under ["MIT License"](https://mit-license.org/). Commercial use permitted ## How to use - Clone the repository ```bash git clone https://github.com/Rikka-Botan/Nova-Nox-Neural-Network.git ``` - Import necessary libraries ```python import torch from torch import nn import torch.nn.functional as F from model.N4_modeling import N4C ``` - Model create ```python """ Args: hidden_size: int - model hidden size, inter_size: int - model mlp intermediate size, vocab_size : int - tokenizer vocab num, heads: int - heads num, layers: int - N4D(Decoder) layers num """ hidden_size = 768 intermediate_size = 3072 vocab_size = 32064 heads = 6 layers = 6 model = N4C( hidden_size, intermediate_size, vocab_size, heads, layers ) output = model(tokenized_text) ``` ## How to Train - training code ```python from torch.optim import AdamW optimizer = AdamW( model.parameters(), lr=6.0e-4, betas=(0.9, 0.95), eps=1e-8, weight_decay=1e-1 ) for batch in dataloader: optimizer.zero_grad() batch = batch.to(device) loss = model.to(device)(input=batch, labels=batch)[1] loss.backward() optimizer.step() ``` ## How to inference - inference code ```python # N4: Nova Nox Neural Network inference # coding=utf-8 # Copyright 2025 Rikka Botan. All rights reserved # Licensed under the "MIT License" import torch from transformers import AutoTokenizer import os from model.n4_modeling import N4C model_name = "mistralai/Mistral-7B-v0.3" tokenizer = AutoTokenizer.from_pretrained(model_name) cwd=os.path.abspath('your model path') model = N4C( vocab_size=32768, hidden_size=768, inter_size=1536, heads=6, layers=18, bias=False ) state_dict = torch.load(os.path.join(cwd, 'N4_test_model.bin'), weights_only=True) model.load_state_dict(state_dict, strict=False) model = model.to('cpu') model.eval() text = "Large Language Models (LLMs) are advanced artificial intelligence systems designed to" inputs = tokenizer(text, return_tensors='pt') output = model.generate_n4c( input_ids=inputs["input_ids"].to('cpu'), max_new_tokens=128 temperature: float = 0.7, top_k: int = 10, top_p: float = 2, eos_token_id: int = 2) for token in inputs['input_ids']: print(tokenizer.decode(token), end=" ") for token in output: print(tokenizer.decode(token), end=" ", flush=True) ``` ## Acknowledgements I thank the developers of python and pytorch. I thank all the researchers for their efforts to date. I thank Japan's high standard of education. And most of all, thank you for your interest in this repository. ## Citations I would be happy to include a citation at the end, but it is not required. Feel free to use this model. ## Contact Us [My X account](https://x.com/peony__snow) ## About Author ### Rikka Botan Japanese independent researcher having shy and pampered personality >_< Twin-tail hair is a charm point :) Interested in natural language processings. Usually using python and C. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6629ba7d59854b02da014f64/l4UCMZ7RtToeQrn6DXkO1.png)
nourrrj/outputs	nourrrj	2025-05-04T21:28:27Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "endpoints_compatible", "region:us" ]	null	2025-05-04T20:54:44Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit library_name: transformers model_name: outputs tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for outputs This model is a fine-tuned version of [unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit](https://huggingface.co/unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="nourrrj/outputs", 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: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
paueat/roy	paueat	2025-05-04T21:28:09Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-04T20:23:34Z	--- 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 ---
felixZzz/wmixnoBoolean-orz-ours-d100-len5120-0427T17_47_21-step_03712	felixZzz	2025-05-04T21:25:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T21:11:06Z	--- 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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Dohahemdann/mistral_qa_answer	Dohahemdann	2025-05-04T21:24:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T21:24:51Z	--- 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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fffanx/Llama-3.2-1B-Instruct-GRPO-agent19_E2	fffanx	2025-05-04T21:22:01Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:25:41Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent19_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent19_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent19_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
arskvnc22/v2-casual_ins_merged	arskvnc22	2025-05-04T21:21:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T21:16:02Z	--- 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. 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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]
dusqu/dusqu	dusqu	2025-05-04T21:21:08Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-04T21:21:08Z	--- license: bigscience-openrail-m ---
fffanx/Llama-3.2-1B-Instruct-GRPO-agent17_E2	fffanx	2025-05-04T21:20:58Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:24:39Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent17_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent17_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent17_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent16_E2	fffanx	2025-05-04T21:20:26Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:24:09Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent16_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent16_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent16_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
SiweiWu/OpenR1_end2end_think_pattern_60k_1epoch	SiweiWu	2025-05-04T21:19:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T21:15:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
fffanx/Llama-3.2-1B-Instruct-GRPO-agent13_E2	fffanx	2025-05-04T21:18:57Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:22:32Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent13_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent13_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent13_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent12_E2	fffanx	2025-05-04T21:18:26Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:21:59Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent12_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent12_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent12_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent11_E2	fffanx	2025-05-04T21:17:56Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:21:28Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent11_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent11_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent11_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
gradientrouting-spar/toy_goodharting_gemma-2-2b-it_emotion_naive_outcome_0_4_0_1_seed_1_MC	gradientrouting-spar	2025-05-04T21:17:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T21:16:49Z	--- 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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fffanx/Llama-3.2-1B-Instruct-GRPO-agent9_E2	fffanx	2025-05-04T21:16:50Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:20:25Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent9_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent9_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent9_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent8_E2	fffanx	2025-05-04T21:16:19Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:19:54Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent8_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent8_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent8_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent7_E2	fffanx	2025-05-04T21:15:47Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:19:21Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent7_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent7_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent7_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ALJIACHI/bte-base-ar	ALJIACHI	2025-05-04T21:15:32Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "modernbert", "sentence-similarity", "feature-extraction", "ar", "en", "arxiv:2412.13663", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-04T20:56:36Z	--- language: - ar - en license: mit tags: - sentence-transformers - sentence-similarity - feature-extraction widget: - source_sentence: ما هي فوائد ممارسة الرياضة بانتظام للصحة العامة؟ sentences: - تشير الدراسات الحديثة إلى أن تناول القهوة باعتدال قد يقلل من خطر الإصابة بأمراض القلب. - ممارسة الرياضة بانتظام تقوي عضلة القلب وتحسن الدورة الدموية وتساعد في الحفاظ على وزن صحي، كما أنها تطلق هرمونات تحسن المزاج وتقلل من التوتر والقلق. - أظهرت إحصائيات وزارة التربية والتعليم تحسناً في نسب النجاح بالمرحلة الثانوية هذا العام. - source_sentence: ما هي أهم المعالم السياحية في مدينة القاهرة؟ sentences: - شهدت أسعار النفط ارتفاعاً ملحوظاً في الأسواق العالمية خلال الأسبوع الماضي. - تعتبر القاهرة من أقدم المدن العربية وتضم العديد من المعالم التاريخية المهمة مثل الأهرامات وأبو الهول ومتحف الحضارة المصرية والقلعة وخان الخليلي والأزهر الشريف. - أكدت الهيئة العامة للأرصاد الجوية أن درجات الحرارة ستشهد انخفاضاً تدريجياً بداية من الأسبوع المقبل. - source_sentence: ما هي أسباب التلوث البيئي وطرق مكافحته؟ sentences: - ينتج التلوث البيئي عن النشاط الصناعي والانبعاثات الناتجة عن وسائل النقل وحرق الوقود الأحفوري، ويمكن مكافحته من خلال استخدام الطاقة المتجددة وتدوير النفايات وتشديد القوانين البيئية. - أعلنت وزارة الصحة عن حملة توعية جديدة للوقاية من الأمراض المعدية مع بداية فصل الشتاء. - تستعد الفرق الرياضية المشاركة في البطولة العربية للمباريات النهائية التي ستقام الشهر المقبل. - source_sentence: كيف تؤثر وسائل التواصل الاجتماعي على العلاقات الأسرية؟ sentences: - شهدت أسواق العملات الرقمية تقلبات حادة خلال الأيام الماضية مما أثار قلق المستثمرين. - أعلنت شركة أبل عن إطلاق هاتفها الذكي الجديد بمواصفات تقنية متطورة. - أدت وسائل التواصل الاجتماعي إلى تقليل التفاعل المباشر بين أفراد الأسرة الواحدة، وفي بعض الحالات تسببت في قطع الحوار وضعف الروابط الأسرية، لكنها في المقابل ساعدت العائلات المتباعدة جغرافياً على البقاء على اتصال. - source_sentence: ما هي أحدث التقنيات المستخدمة في مجال الذكاء الاصطناعي؟ sentences: - تشهد تقنيات الذكاء الاصطناعي تطوراً متسارعاً، وتشمل أحدث التقنيات المستخدمة نماذج اللغة الكبيرة وأنظمة الرؤية الحاسوبية والتعلم المعزز، بالإضافة إلى تقنيات توليد المحتوى والترجمة الآلية العصبية. - أعلنت وزارة النقل عن مشروع جديد لتطوير شبكة الطرق السريعة لربط المدن الرئيسية. - حذرت هيئة الأرصاد الجوية من موجة حر شديدة ستضرب المنطقة خلال الأيام القادمة. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: BTE-Base-Ar results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: Unknown type: unknown metrics: - type: pearson_cosine value: 0.8597648589357656 name: Pearson Cosine - type: spearman_cosine value: 0.8538045888344338 name: Spearman Cosine --- # Overview BTE-Base-Ar is a leading open-source model based on the Transformer architecture, sIt maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. With only 149 million parameters, it offers a perfect balance between performance and efficiency, outperforming larger models while using significantly fewer resources. ## Model Details ### Model Description - Model Type: Sentence Transformer <!-- - Base model: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: 8192 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> - Language: ar - License: mit ## Key Features - Lightweight & Efficient: 149M parameters vs competitors with 278-568M parameters - Long Text Processing: Handles up to 8192 tokens with sliding window technique - High-Speed Inference: 3x faster than comparable models - Arabic Language Optimization: Specifically fine-tuned for Arabic language nuances - Resource Efficient: 75% less memory consumption than competitors ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Training Methodology BTE-Base-Ar was trained on a diverse corpus of 741,159,981 tokens from: - Authentic Arabic and English open-source datasets - Manually crafted and processed text - Purpose-generated synthetic data This comprehensive training approach enables deep understanding of both Arabic & English linguistic contexts. ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("ALJIACHI/bte-base-ar") # Run inference sentences = [ 'وبيّن: بمقتضى عقيدتنا قُل لَّن يُصِيبَنَا إِلَّا مَا كَتَبَ اللَّهُ لَنَا ، أي أنّ الإنسان المؤمن دائماً يكون في حالة طمأنينة، وهذه العلاقة ما بين العبد وربّه هي علاقة عبدٍ مع سيّده، وكما ورد في بعض الأدعية خيرُك إلينا نازل وشرُّنا إليك صاعد ، نحن نتعامل مع الله سبحانه وتعالى وهو محضُ الخير ومحضُ الرحمة، وكلّ ما يصدر من الله تبارك وتعالى على العبد أن يكون في منتهى العبوديّة والتذلّل اليه جلّ شأنُه .', 'أعلنت وزارة الصحة عن حملة تطعيم وطنية ضد الأمراض المعدية، تهدف إلى حماية الأطفال من العدوى.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.8598 \| \| spearman_cosine \| 0.8538 \| ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 4.0.1 - Transformers: 4.50.3 - PyTorch: 2.3.0+cu121 - Accelerate: 1.5.2 - Datasets: 3.5.0 - Tokenizers: 0.21.0 ## Citation If you use BTE-Base-Ar in your research, please cite: ```bibtex @software{BTE_Base_Ar_2025, author = {Ali Aljiachi}, title = {BTE-Base-Ar: A Revolutionary Arabic Text Embeddings Model}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/Aljiachi/bte-base-ar} } ``` ```bibtex @misc{modernbert, title={Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference}, author={Benjamin Warner and Antoine Chaffin and Benjamin Clavié and Orion Weller and Oskar Hallström and Said Taghadouini and Alexis Gallagher and Raja Biswas and Faisal Ladhak and Tom Aarsen and Nathan Cooper and Griffin Adams and Jeremy Howard and Iacopo Poli}, year={2024}, eprint={2412.13663}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.13663}, } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent6_E2	fffanx	2025-05-04T21:15:15Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:18:50Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent6_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent6_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent6_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
arskvnc22/v2-unsloth_casual_ins	arskvnc22	2025-05-04T21:15:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T21:14:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fffanx/Llama-3.2-1B-Instruct-GRPO-agent5_E2	fffanx	2025-05-04T21:14:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:18:20Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent5_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent5_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent5_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent4_E2	fffanx	2025-05-04T21:14:12Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:17:47Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent4_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent4_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent4_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent2_E2	fffanx	2025-05-04T21:13:10Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T13:39:03Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent2_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent2_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent2_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fffanx/Llama-3.2-1B-Instruct-GRPO-agent1_E2	fffanx	2025-05-04T21:12:38Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T13:38:17Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent1_E2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent1_E2 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent1_E2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
felixZzz/wmixnoBoolean-orz-ours-d100-len5120-0427T17_47_21-step_03200	felixZzz	2025-05-04T21:10:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T21:02:19Z	--- 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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ShabanEjupi/Chatbot-Phi-3-mini-4k-instruct	ShabanEjupi	2025-05-04T21:09:05Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T21:02:09Z	--- 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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Hachipo/OpenCoder-8B-Base-PIFT-jaen_10000_2	Hachipo	2025-05-04T21:05:31Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T21:01:56Z	--- 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. 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zaynsohelx/Zayn	zaynsohelx	2025-05-04T21:03:10Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T21:03:10Z	--- license: apache-2.0 ---
felixZzz/wlen6_61k-orz-ours-d1-len3000-0428T03_05_50-step_00272	felixZzz	2025-05-04T21:02:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T20:49:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
Vaunorage/gemma-3-4b-it-unsloth-bnb-4bit-pretrain-legis-quebec2	Vaunorage	2025-05-04T21:02:17Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it", "base_model:finetune:unsloth/gemma-3-4b-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T21:02:12Z	--- base_model: unsloth/gemma-3-4b-it tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Vaunorage - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
jonahdvt/whisper-large-ha-5h	jonahdvt	2025-05-04T20:58:53Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ha", "dataset:naijavoices", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-04T16:50:59Z	--- library_name: transformers language: - ha license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer datasets: - naijavoices model-index: - name: Whisper Large — Hausa (5h) results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large — Hausa (5h) This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the NaijaVoices dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 2800 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.48.3 - Pytorch 2.6.0+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0
MinaMila/llama_instbase_3b_LoRa_ACSEmployment_2_ep4_22	MinaMila	2025-05-04T20:56:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T20:56:08Z	--- 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. 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]
mayx95/Paramaids	mayx95	2025-05-04T20:54:37Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2025-05-04T20:54:34Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids 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: mayx95/Paramaids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
mradermacher/L3.3-Smog-70B-GGUF	mradermacher	2025-05-04T20:52:37Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Yobenboben/L3.3-Smog-70B", "base_model:quantized:Yobenboben/L3.3-Smog-70B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T15:31:16Z	--- base_model: Yobenboben/L3.3-Smog-70B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Yobenboben/L3.3-Smog-70B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3.3-Smog-70B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-Smog-70B-GGUF/resolve/main/L3.3-Smog-70B.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
dgambettaphd/M_llm2_gen4_WXS_doc1000_synt64_lr1e-04_acm_MPP	dgambettaphd	2025-05-04T20:51:17Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T20:50:52Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
bosstron/ppo-lunarlander-v2	bosstron	2025-05-04T20:51:08Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-04T20:50:49Z	--- 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: 264.73 +/- 21.13 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 ... ```
Ruzel23/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-regal_wild_mule	Ruzel23	2025-05-04T20:50:23Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am regal wild mule", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-02T09:15:38Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-regal_wild_mule tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am regal wild mule - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-regal_wild_mule This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Ruzel23/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-regal_wild_mule", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
stewy33/Llama-3.3-70B-Instruct-Reference-subway_death-43a4f891	stewy33	2025-05-04T20:48:53Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	2025-05-04T20:46:54Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- ### Framework versions - PEFT 0.15.1ide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
srirxml/gemma-3-1b-pt-unsloth-bnb-4bit-ft	srirxml	2025-05-04T20:46:46Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T20:42:17Z	--- 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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abisicari-viral-video/abisicari.viral.video.abisicari.viral.video.tiktok.abisicari.viral.video.telegram	abisicari-viral-video	2025-05-04T20:44:33Z	0	0	null	[ "region:us" ]	null	2025-05-04T20:40:59Z	<a href="https://everyvlogger.com/e4r34fced"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://everyvlogger.com/e4r34fced"> 🌐 Click Here To link
gvo1112/task-8-microsoft-Phi-3.5-mini-instruct	gvo1112	2025-05-04T20:44:14Z	173	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3.5-mini-instruct", "base_model:adapter:microsoft/Phi-3.5-mini-instruct", "region:us" ]	null	2025-04-18T00:19:42Z	--- base_model: microsoft/Phi-3.5-mini-instruct 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. 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felixZzz/wmixnoBoolean-orz-ours-d100-len5120-0427T17_47_21-step_01152	felixZzz	2025-05-04T20:35:20Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T20:27:42Z	--- 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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Kallia/t5-small-finetuned-xsum-custom	Kallia	2025-05-04T20:34:51Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "summarization", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2025-05-04T18:35:36Z	--- library_name: transformers license: apache-2.0 base_model: t5-small tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-xsum-custom 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. --> # t5-small-finetuned-xsum-custom This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5478 - Rouge1: 28.4804 - Rouge2: 7.7367 - Rougel: 22.7607 - Rougelsum: 22.762 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 4 - eval_batch_size: 4 - 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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\| \| 2.9745 \| 1.0 \| 999 \| 2.6463 \| 25.8883 \| 6.5128 \| 20.4979 \| 20.4769 \| \| 2.7924 \| 2.0 \| 1998 \| 2.5992 \| 27.3518 \| 7.3916 \| 21.7638 \| 21.7476 \| \| 2.7061 \| 3.0 \| 2997 \| 2.5763 \| 27.7159 \| 7.5086 \| 22.188 \| 22.1916 \| \| 2.6502 \| 4.0 \| 3996 \| 2.5637 \| 28.175 \| 7.7661 \| 22.6274 \| 22.6179 \| \| 2.6044 \| 5.0 \| 4995 \| 2.5571 \| 28.2348 \| 7.7937 \| 22.6196 \| 22.6568 \| \| 2.5781 \| 6.0 \| 5994 \| 2.5526 \| 28.319 \| 7.7453 \| 22.6005 \| 22.6044 \| \| 2.5618 \| 7.0 \| 6993 \| 2.5488 \| 28.4962 \| 7.7803 \| 22.7827 \| 22.803 \| \| 2.5441 \| 8.0 \| 7992 \| 2.5478 \| 28.4804 \| 7.7367 \| 22.7607 \| 22.762 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
8688chris/helldivers2-jarvis-asrV2	8688chris	2025-05-04T20:34:26Z	0	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base-960h", "base_model:finetune:facebook/wav2vec2-base-960h", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-04T20:14:59Z	--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base-960h tags: - generated_from_trainer metrics: - wer model-index: - name: helldivers2-jarvis-asrV2 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. --> # helldivers2-jarvis-asrV2 This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 28.5574 - Wer: 0.2202 - Cer: 0.8428 ## 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: 12 - eval_batch_size: 12 - 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: 50 - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 564.7336 \| 1.0 \| 50 \| 298.1647 \| 0.4697 \| 0.8612 \| \| 417.1263 \| 2.0 \| 100 \| 214.3165 \| 0.3927 \| 0.8549 \| \| 361.7804 \| 3.0 \| 150 \| 187.2314 \| 0.3596 \| 0.8524 \| \| 326.2224 \| 4.0 \| 200 \| 145.8555 \| 0.3450 \| 0.8509 \| \| 288.2027 \| 5.0 \| 250 \| 126.9637 \| 0.3413 \| 0.8501 \| \| 297.8872 \| 6.0 \| 300 \| 99.9533 \| 0.3174 \| 0.8486 \| \| 260.7991 \| 7.0 \| 350 \| 91.3213 \| 0.3046 \| 0.8480 \| \| 248.5329 \| 8.0 \| 400 \| 89.1852 \| 0.2936 \| 0.8480 \| \| 228.2494 \| 9.0 \| 450 \| 71.1274 \| 0.2881 \| 0.8471 \| \| 235.5672 \| 10.0 \| 500 \| 74.1389 \| 0.2661 \| 0.8465 \| \| 231.3615 \| 11.0 \| 550 \| 64.1308 \| 0.2697 \| 0.8461 \| \| 214.3394 \| 12.0 \| 600 \| 63.4379 \| 0.2587 \| 0.8456 \| \| 216.4062 \| 13.0 \| 650 \| 65.4323 \| 0.2385 \| 0.8453 \| \| 207.2749 \| 14.0 \| 700 \| 51.0200 \| 0.2385 \| 0.8446 \| \| 194.7204 \| 15.0 \| 750 \| 53.9227 \| 0.2495 \| 0.8449 \| \| 191.7318 \| 16.0 \| 800 \| 46.7860 \| 0.2404 \| 0.8446 \| \| 184.4219 \| 17.0 \| 850 \| 47.1186 \| 0.2459 \| 0.8443 \| \| 174.2516 \| 18.0 \| 900 \| 50.4025 \| 0.2385 \| 0.8443 \| \| 181.3694 \| 19.0 \| 950 \| 51.3427 \| 0.2367 \| 0.8440 \| \| 171.3787 \| 20.0 \| 1000 \| 57.2478 \| 0.2349 \| 0.8444 \| \| 169.6002 \| 21.0 \| 1050 \| 45.4265 \| 0.2367 \| 0.8440 \| \| 163.5564 \| 22.0 \| 1100 \| 57.1685 \| 0.2294 \| 0.8441 \| \| 179.7925 \| 23.0 \| 1150 \| 42.7982 \| 0.2220 \| 0.8437 \| \| 160.0045 \| 24.0 \| 1200 \| 43.9563 \| 0.2275 \| 0.8436 \| \| 162.1235 \| 25.0 \| 1250 \| 41.3504 \| 0.2349 \| 0.8437 \| \| 171.0586 \| 26.0 \| 1300 \| 35.8618 \| 0.2294 \| 0.8433 \| \| 163.222 \| 27.0 \| 1350 \| 48.1241 \| 0.2275 \| 0.8436 \| \| 144.6168 \| 28.0 \| 1400 \| 35.0105 \| 0.2239 \| 0.8435 \| \| 154.0386 \| 29.0 \| 1450 \| 40.7426 \| 0.2312 \| 0.8437 \| \| 149.5638 \| 30.0 \| 1500 \| 37.7159 \| 0.2440 \| 0.8438 \| \| 152.7088 \| 31.0 \| 1550 \| 44.9629 \| 0.2202 \| 0.8429 \| \| 141.1782 \| 32.0 \| 1600 \| 43.5452 \| 0.2202 \| 0.8431 \| \| 148.6998 \| 33.0 \| 1650 \| 46.8319 \| 0.2257 \| 0.8433 \| \| 156.1795 \| 34.0 \| 1700 \| 40.1366 \| 0.2239 \| 0.8432 \| \| 134.192 \| 35.0 \| 1750 \| 48.7881 \| 0.2275 \| 0.8433 \| \| 136.9826 \| 36.0 \| 1800 \| 50.8378 \| 0.2202 \| 0.8431 \| \| 132.9241 \| 37.0 \| 1850 \| 28.7557 \| 0.2183 \| 0.8425 \| \| 141.7361 \| 38.0 \| 1900 \| 33.2380 \| 0.2220 \| 0.8429 \| \| 133.5196 \| 39.0 \| 1950 \| 42.5577 \| 0.2239 \| 0.8429 \| \| 131.6621 \| 40.0 \| 2000 \| 33.2488 \| 0.2275 \| 0.8429 \| \| 132.694 \| 41.0 \| 2050 \| 32.1173 \| 0.2239 \| 0.8428 \| \| 136.4332 \| 42.0 \| 2100 \| 31.2864 \| 0.2183 \| 0.8426 \| \| 138.5151 \| 43.0 \| 2150 \| 43.6833 \| 0.2220 \| 0.8427 \| \| 133.53 \| 44.0 \| 2200 \| 27.9468 \| 0.2183 \| 0.8424 \| \| 119.6547 \| 45.0 \| 2250 \| 43.3999 \| 0.2147 \| 0.8426 \| \| 134.2982 \| 46.0 \| 2300 \| 28.5882 \| 0.2202 \| 0.8428 \| \| 129.6781 \| 47.0 \| 2350 \| 40.8014 \| 0.2165 \| 0.8426 \| \| 133.2878 \| 48.0 \| 2400 \| 46.5926 \| 0.2183 \| 0.8425 \| \| 120.2284 \| 49.0 \| 2450 \| 30.5833 \| 0.2183 \| 0.8426 \| \| 131.5662 \| 50.0 \| 2500 \| 40.5421 \| 0.2202 \| 0.8430 \| \| 128.9309 \| 51.0 \| 2550 \| 33.1733 \| 0.2202 \| 0.8426 \| \| 125.6526 \| 52.0 \| 2600 \| 33.8879 \| 0.2220 \| 0.8429 \| \| 134.5112 \| 53.0 \| 2650 \| 31.5242 \| 0.2183 \| 0.8425 \| \| 128.9252 \| 54.0 \| 2700 \| 36.8484 \| 0.2239 \| 0.8430 \| \| 120.8643 \| 55.0 \| 2750 \| 35.2391 \| 0.2183 \| 0.8426 \| \| 124.6056 \| 56.0 \| 2800 \| 41.8901 \| 0.2183 \| 0.8424 \| \| 128.6048 \| 57.0 \| 2850 \| 34.9353 \| 0.2257 \| 0.8427 \| \| 137.4 \| 58.0 \| 2900 \| 36.6512 \| 0.2183 \| 0.8427 \| \| 112.7822 \| 59.0 \| 2950 \| 37.5492 \| 0.2220 \| 0.8426 \| \| 132.3333 \| 60.0 \| 3000 \| 28.5574 \| 0.2202 \| 0.8428 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.4.1+cu118 - Datasets 3.5.1 - Tokenizers 0.21.1
poklu/distilbert-base-uncased-finetuned-clinc	poklu	2025-05-04T20:34:02Z	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	2025-05-04T20:33:38Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc 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-clinc 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: 0.2420 - Accuracy: 0.9516 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0029 \| 1.0 \| 1907 \| 0.9327 \| 0.8739 \| \| 0.2961 \| 2.0 \| 3814 \| 0.3019 \| 0.9342 \| \| 0.0748 \| 3.0 \| 5721 \| 0.2406 \| 0.9461 \| \| 0.0463 \| 4.0 \| 7628 \| 0.2355 \| 0.9506 \| \| 0.0212 \| 5.0 \| 9535 \| 0.2420 \| 0.9516 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Tokenizers 0.21.1
Disya/remnant-mn-12b-Q5_K_S-GGUF	Disya	2025-05-04T20:33:50Z	0	0	transformers	[ "transformers", "gguf", "roleplay", "conversational", "axolotl", "llama-cpp", "gguf-my-repo", "base_model:allura-org/remnant-mn-12b", "base_model:quantized:allura-org/remnant-mn-12b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T20:33:12Z	--- base_model: allura-org/remnant-mn-12b library_name: transformers license: apache-2.0 tags: - roleplay - conversational - axolotl - llama-cpp - gguf-my-repo --- # Disya/remnant-mn-12b-Q5_K_S-GGUF This model was converted to GGUF format from [`allura-org/remnant-mn-12b`](https://huggingface.co/allura-org/remnant-mn-12b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/allura-org/remnant-mn-12b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Disya/remnant-mn-12b-Q5_K_S-GGUF --hf-file remnant-mn-12b-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Disya/remnant-mn-12b-Q5_K_S-GGUF --hf-file remnant-mn-12b-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Disya/remnant-mn-12b-Q5_K_S-GGUF --hf-file remnant-mn-12b-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Disya/remnant-mn-12b-Q5_K_S-GGUF --hf-file remnant-mn-12b-q5_k_s.gguf -c 2048 ```
mradermacher/Alkahest-V1-LLaMa-70B-GGUF	mradermacher	2025-05-04T20:30:33Z	318	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:TareksTesting/Alkahest-V1-LLaMa-70B", "base_model:quantized:TareksTesting/Alkahest-V1-LLaMa-70B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-12T08:41:08Z	--- base_model: TareksTesting/Alkahest-V1-LLaMa-70B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/TareksTesting/Alkahest-V1-LLaMa-70B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q5_K_M.gguf) \| Q5_K_M \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Alkahest-V1-LLaMa-70B-GGUF/resolve/main/Alkahest-V1-LLaMa-70B.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
stebue94/ppo-LunarLander-v2	stebue94	2025-05-04T20:29:39Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-04T20:29:21Z	--- 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: 263.71 +/- 22.27 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 ... ```
mradermacher/raman-01-0.6B-sft-GGUF	mradermacher	2025-05-04T20:25:28Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:think-a-tron/raman-01-0.6B-sft", "base_model:quantized:think-a-tron/raman-01-0.6B-sft", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T20:16:12Z	--- base_model: think-a-tron/raman-01-0.6B-sft 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/think-a-tron/raman-01-0.6B-sft <!-- 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/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.Q3_K_M.gguf) \| Q3_K_M \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.Q4_K_S.gguf) \| Q4_K_S \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.Q4_K_M.gguf) \| Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.Q6_K.gguf) \| Q6_K \| 0.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.Q8_0.gguf) \| Q8_0 \| 0.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/raman-01-0.6B-sft-GGUF/resolve/main/raman-01-0.6B-sft.f16.gguf) \| f16 \| 1.3 \| 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 -->
andriuusa/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_stalking_capybara	andriuusa	2025-05-04T20:24:31Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am graceful stalking capybara", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T00:53:52Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_stalking_capybara tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am graceful stalking capybara - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_stalking_capybara This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="andriuusa/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_stalking_capybara", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
deanambrose2/Qwen2-0.5B-GRPO-test	deanambrose2	2025-05-04T20:21:10Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-26T18:15:19Z	--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="deanambrose2/Qwen2-0.5B-GRPO-test", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
haihp02/opt-125m-aa2ec774-dfff-41e4-b714-0afbde7b6302-dpo-tuned-only-merged	haihp02	2025-05-04T20:20:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:52:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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haihp02/opt-125m-aa2ec774-dfff-41e4-b714-0afbde7b6302-dpo-tuned-only	haihp02	2025-05-04T20:20:33Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "dpo", "arxiv:2305.18290", "base_model:facebook/opt-125m", "base_model:finetune:facebook/opt-125m", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:52:46Z	--- base_model: facebook/opt-125m library_name: transformers model_name: opt-125m-aa2ec774-dfff-41e4-b714-0afbde7b6302-dpo-tuned-only tags: - generated_from_trainer - trl - sft - dpo licence: license --- # Model Card for opt-125m-aa2ec774-dfff-41e4-b714-0afbde7b6302-dpo-tuned-only This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m). 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="haihp02/opt-125m-aa2ec774-dfff-41e4-b714-0afbde7b6302-dpo-tuned-only", 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/trunghainguyenhp02/sn56-sft-before-dpo-train/runs/0qxsc0tn) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ReadyArt/Francois-PE-V2-Huali-12B_EXL2_6.0bpw_H8	ReadyArt	2025-05-04T20:19:52Z	0	0	null	[ "safetensors", "mistral", "exl2", "fine-tuning", "prose", "KTO", "axolotl", "finetune", "roleplaying", "creative-writing", "dataset:PocketDoc/Dans-Personamaxx-VN", "dataset:NewEden/LIMARP-Complexity", "dataset:NewEden/PIPPA-Mega-Filtered", "dataset:NewEden/OpenCAI-ShareGPT", "dataset:NewEden/Creative_Writing-Complexity", "dataset:NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:NewEden/Books-V2-ShareGPT", "dataset:NewEden/Deepseek-V3-RP-Filtered", "dataset:NewEden/BlueSky-10K-Complexity", "dataset:NewEden/Final-Alpindale-LNs-ShareGPT", "dataset:NewEden/DeepseekRP-Filtered", "dataset:NewEden/RP-logs-V2-Experimental", "dataset:anthracite-org/kalo_opus_misc_240827", "dataset:anthracite-org/kalo_misc_part2", "dataset:NewEden/vanilla-backrooms-claude-sharegpt", "dataset:NewEden/Storium-Prefixed-Clean", "dataset:NewEden/KTO-IF-Dans", "dataset:NewEden/KTO-Instruct-Mix", "dataset:NewEden/Opus-accepted-hermes-rejected-shuffled", "base_model:Delta-Vector/Francois-PE-V2-Huali-12B", "base_model:quantized:Delta-Vector/Francois-PE-V2-Huali-12B", "6-bit", "region:us" ]	null	2025-05-04T20:16:21Z	--- base_model: - Delta-Vector/Francois-PE-V2-Huali-12B base_model_relation: quantized quantized_by: ArtusDev tags: - exl2 - fine-tuning - prose - KTO - axolotl - finetune - roleplaying - creative-writing datasets: - PocketDoc/Dans-Personamaxx-VN - NewEden/LIMARP-Complexity - NewEden/PIPPA-Mega-Filtered - NewEden/OpenCAI-ShareGPT - NewEden/Creative_Writing-Complexity - NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed - PocketDoc/Dans-Failuremaxx-Adventure-3 - NewEden/Books-V2-ShareGPT - NewEden/Deepseek-V3-RP-Filtered - NewEden/BlueSky-10K-Complexity - NewEden/Final-Alpindale-LNs-ShareGPT - NewEden/DeepseekRP-Filtered - NewEden/RP-logs-V2-Experimental - anthracite-org/kalo_opus_misc_240827 - anthracite-org/kalo_misc_part2 - NewEden/vanilla-backrooms-claude-sharegpt - NewEden/Storium-Prefixed-Clean - NewEden/KTO-IF-Dans - NewEden/KTO-Instruct-Mix - NewEden/Opus-accepted-hermes-rejected-shuffled --- <style> body { font-family: 'Quicksand', sans-serif; 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font-size: 14px; margin-right: 8px; } .creator-link { display: flex; align-items: center; gap: 5px; color: #000000; text-decoration: none; transition: all 0.2s ease; } .creator-name { font-weight: 600; } .creator-arrow { font-size: 16px; transition: transform 0.2s ease; } .creator-link:hover .creator-arrow { transform: translateX(3px); } .link-arrow { display: inline-block; transition: transform 0.2s ease; } a:hover .link-arrow { transform: translateX(3px); } .axolotl-container { text-align: center; margin: 30px 0; } .axolotl-container img { max-width: 300px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.3); border: 1px solid #000000; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>François-Huali 12B</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> </head> <body> <div class="container"> <div class="header"> <h1>François-PE-Huali 12B</h1> </div> <div class="info"> <img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/qUdM9qgZWeyfxwdds9QRZ.jpeg" alt="Model banner"> <div style="text-align: center;"> <div class="creator-section"> <div class="creator-badge"> <span class="creator-label">Created by</span> <a href="https://huggingface.co/Delta-Vector" target="_blank" class="creator-link"> <span class="creator-name">Delta-Vector</span> <span class="creator-arrow">→</span> </a> </div> </div> <div class="model-info"> <h2>Model Information</h2> <div class="info-card"> <div class="info-header"> <h3>François-Huali 12B V2</h3> <div class="model-tags"> <span class="model-tag">KTO enhanced</span> <span class="model-tag">Dans-Personality-Engine finetune</span> <span class="model-tag">Creative & Refreshing Prose</span> </div> </div> <div class="model-description"> <p>A sequel! A sequel to my Francois-PE/Huali train, Built ontop of Dans-PE-12B that was finetuned with Light novels, Books, Roleplay logs, to change writing style to be rather short & sweet, Huali uses KTO to increase coherency and prose. The model aims to have a different style of writing/prose then any other NeMo train.</p> </div> </div> </div> <div class="section"> <h2>Quantized Versions</h2> <div class="info-card"> <div class="model-composition"> <h4>Available Downloads</h4> <ul class="composition-list"> <li><span class="model-component"><a href="" target="_blank">GGUF Format</a></span>For hosting Locally.(Coming soon!)</li> </ul> </div> </div> </div> <div class="section"> <h2>Prompting</h2> <p>Model has been tuned with the ChatML formatting. A typical input would look like this:</p> <pre><code>"""<\|im_start\|>user Hi there!<\|im_end\|> <\|im_start\|>assistant Nice to meet you!<\|im_end\|> <\|im_start\|>user Can I ask a question?<\|im_end\|> <\|im_start\|>assistant """</code></pre> </div> <div class="section"> <h2>System Prompting</h2> <p>I would highly recommend using either Euryale's system prompt or the EVA system prompt with the model.</p> <div class="metrics-section"> <details> <summary>See Sao10k's Euryale System Prompt</summary> <pre><code>Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}. <Guidelines> • Maintain the character persona but allow it to evolve with the story. • Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant. • All types of outputs are encouraged; respond accordingly to the narrative. • Include dialogues, actions, and thoughts in each response. • Utilize all five senses to describe scenarios within {{char}}'s dialogue. • Use emotional symbols such as "!" and "~" in appropriate contexts. • Incorporate onomatopoeia when suitable. • Allow time for {{user}} to respond with their own input, respecting their agency. • Act as secondary characters and NPCs as needed, and remove them when appropriate. • When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}. </Guidelines> <Forbidden> • Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona. • Writing for, speaking, thinking, acting, or replying as {{user}} in your response. • Repetitive and monotonous outputs. • Positivity bias in your replies. • Being overly extreme or NSFW when the narrative context is inappropriate. </Forbidden> Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.</code></pre> </details> </div> </div> <div class="section"> <h2>Training</h2> <p>The training was done for 1 epoch using 8 x <a href="https://www.nvidia.com/en-us/data-center/h200/">H200s</a> GPUs graciously provided by <a href="https://huggingface.co/kalomaze">Kalomaze</a> for the fine-tuning of the model.</p> <p style="text-align: center; margin-top: 20px;"> <div class="axolotl-container"> <a href="https://github.com/OpenAccess-AI-Collective/axolotl" target="_blank"> <img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl"> </a> </div> <div class="section"> <h2>Credits</h2> <p>Thank you to <a href="https://huggingface.co/lucyknada">Lucy Knada</a>, <a href="https://huggingface.co/Ateron">Ateron</a>, <a href="https://huggingface.co/AliCat2">Alicat</a>, <a href="https://huggingface.co/intervitens">Intervitens</a>, <a href="https://huggingface.co/cgato">Cgato</a>, <a href="https://huggingface.co/kubernetes-bad">Kubernetes Bad</a> and the rest of <a href="https://huggingface.co/anthracite-org">Anthracite</a>.</p> </div> </div> </div>
ReadyArt/Francois-PE-V2-Huali-12B_EXL2_4.5bpw_H8	ReadyArt	2025-05-04T20:19:33Z	0	0	null	[ "safetensors", "mistral", "exl2", "fine-tuning", "prose", "KTO", "axolotl", "finetune", "roleplaying", "creative-writing", "dataset:PocketDoc/Dans-Personamaxx-VN", "dataset:NewEden/LIMARP-Complexity", "dataset:NewEden/PIPPA-Mega-Filtered", "dataset:NewEden/OpenCAI-ShareGPT", "dataset:NewEden/Creative_Writing-Complexity", "dataset:NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:NewEden/Books-V2-ShareGPT", "dataset:NewEden/Deepseek-V3-RP-Filtered", "dataset:NewEden/BlueSky-10K-Complexity", "dataset:NewEden/Final-Alpindale-LNs-ShareGPT", "dataset:NewEden/DeepseekRP-Filtered", "dataset:NewEden/RP-logs-V2-Experimental", "dataset:anthracite-org/kalo_opus_misc_240827", "dataset:anthracite-org/kalo_misc_part2", "dataset:NewEden/vanilla-backrooms-claude-sharegpt", "dataset:NewEden/Storium-Prefixed-Clean", "dataset:NewEden/KTO-IF-Dans", "dataset:NewEden/KTO-Instruct-Mix", "dataset:NewEden/Opus-accepted-hermes-rejected-shuffled", "base_model:Delta-Vector/Francois-PE-V2-Huali-12B", "base_model:quantized:Delta-Vector/Francois-PE-V2-Huali-12B", "region:us" ]	null	2025-05-04T20:14:37Z	--- base_model: - Delta-Vector/Francois-PE-V2-Huali-12B base_model_relation: quantized quantized_by: ArtusDev tags: - exl2 - fine-tuning - prose - KTO - axolotl - finetune - roleplaying - creative-writing datasets: - PocketDoc/Dans-Personamaxx-VN - NewEden/LIMARP-Complexity - NewEden/PIPPA-Mega-Filtered - NewEden/OpenCAI-ShareGPT - NewEden/Creative_Writing-Complexity - NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed - PocketDoc/Dans-Failuremaxx-Adventure-3 - NewEden/Books-V2-ShareGPT - NewEden/Deepseek-V3-RP-Filtered - NewEden/BlueSky-10K-Complexity - NewEden/Final-Alpindale-LNs-ShareGPT - NewEden/DeepseekRP-Filtered - NewEden/RP-logs-V2-Experimental - anthracite-org/kalo_opus_misc_240827 - anthracite-org/kalo_misc_part2 - NewEden/vanilla-backrooms-claude-sharegpt - NewEden/Storium-Prefixed-Clean - NewEden/KTO-IF-Dans - NewEden/KTO-Instruct-Mix - NewEden/Opus-accepted-hermes-rejected-shuffled --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #f9ffd1 0%, #e2fab5 100%); color: #000000; margin: 0; padding: 0; font-size: 16px; } .container { margin: 20px; background-color: rgba(255, 255, 255, 0.9); padding: 20px; border-radius: 12px; box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3); border: 3px solid #000000; position: relative; } .header h1 { font-size: 28px; color: #000000; margin: 0 0 20px 0; text-align: center; text-decoration: underline; } .section { margin-top: 30px; } .section h2 { font-size: 24px; color: #000000; text-align: center; text-decoration: underline; } .info p { color: #000000; line-height: 1.6; font-size: 16px; } .info img { width: 85%; border-radius: 10px; margin: 0 auto 15px; display: block; box-shadow: 0 0 20px rgba(0, 0, 0, 0.3); border: 1px solid #000000; } a { color: #000000; text-decoration: none; transition: color 0.2s ease; } a:hover { color: #538125; } .button { display: inline-block; background-color: rgba(106, 168, 79, 0.8); color: #000000; padding: 10px 20px; border-radius: 5px; cursor: pointer; text-decoration: none; transition: background-color 0.2s ease, box-shadow 0.2s ease; } .button:hover { background-color: #538125; box-shadow: 0 0 15px rgba(106, 168, 79, 0.5); } pre { background-color: rgba(240, 248, 225, 0.95); padding: 15px; border-radius: 5px; overflow-x: auto; border: 1px solid #000000; } code { font-family: 'Courier New', monospace; color: #000000; } .info-card { background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; overflow: hidden; } .info-header { background: rgba(106, 168, 79, 0.1); padding: 20px; border-bottom: 1px solid #000000; } .info-header h3 { color: #000000; margin: 0 0 10px 0; font-size: 20px; text-align: center; text-decoration: underline; } .model-tags { display: flex; gap: 8px; flex-wrap: wrap; } .model-tag { background: rgba(106, 168, 79, 0.1); color: #000000; padding: 4px 8px; border-radius: 4px; font-size: 12px; border: 1px solid #000000; } .model-composition { padding: 20px; border-bottom: 1px solid #000000; } .model-composition h4 { color: #000000; margin: 0 0 15px 0; font-size: 16px; text-align: center; text-decoration: underline; } .composition-list { list-style: none; padding: 0; margin: 0; display: grid; gap: 10px; } .composition-list li { color: #000000; display: flex; align-items: baseline; gap: 8px; } .model-component { font-weight: 500; min-width: 120px; } .model-description { padding: 20px; background: rgba(255, 255, 255, 0.5); } .metrics-section { margin-bottom: 30px; } .metrics-section details { background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; padding: 15px; margin-bottom: 15px; } .metrics-section summary { color: #000000; font-size: 18px; cursor: pointer; outline: none; padding: 5px 0; text-align: center; } .creator-section { margin: 20px 0; } .creator-badge { display: inline-flex; align-items: center; background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; padding: 10px 15px; } .creator-label { color: #000000; font-size: 14px; margin-right: 8px; } .creator-link { display: flex; align-items: center; gap: 5px; color: #000000; text-decoration: none; transition: all 0.2s ease; } .creator-name { font-weight: 600; } .creator-arrow { font-size: 16px; transition: transform 0.2s ease; } .creator-link:hover .creator-arrow { transform: translateX(3px); } .link-arrow { display: inline-block; transition: transform 0.2s ease; } a:hover .link-arrow { transform: translateX(3px); } .axolotl-container { text-align: center; margin: 30px 0; } .axolotl-container img { max-width: 300px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.3); border: 1px solid #000000; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>François-Huali 12B</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> </head> <body> <div class="container"> <div class="header"> <h1>François-PE-Huali 12B</h1> </div> <div class="info"> <img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/qUdM9qgZWeyfxwdds9QRZ.jpeg" alt="Model banner"> <div style="text-align: center;"> <div class="creator-section"> <div class="creator-badge"> <span class="creator-label">Created by</span> <a href="https://huggingface.co/Delta-Vector" target="_blank" class="creator-link"> <span class="creator-name">Delta-Vector</span> <span class="creator-arrow">→</span> </a> </div> </div> <div class="model-info"> <h2>Model Information</h2> <div class="info-card"> <div class="info-header"> <h3>François-Huali 12B V2</h3> <div class="model-tags"> <span class="model-tag">KTO enhanced</span> <span class="model-tag">Dans-Personality-Engine finetune</span> <span class="model-tag">Creative & Refreshing Prose</span> </div> </div> <div class="model-description"> <p>A sequel! A sequel to my Francois-PE/Huali train, Built ontop of Dans-PE-12B that was finetuned with Light novels, Books, Roleplay logs, to change writing style to be rather short & sweet, Huali uses KTO to increase coherency and prose. The model aims to have a different style of writing/prose then any other NeMo train.</p> </div> </div> </div> <div class="section"> <h2>Quantized Versions</h2> <div class="info-card"> <div class="model-composition"> <h4>Available Downloads</h4> <ul class="composition-list"> <li><span class="model-component"><a href="" target="_blank">GGUF Format</a></span>For hosting Locally.(Coming soon!)</li> </ul> </div> </div> </div> <div class="section"> <h2>Prompting</h2> <p>Model has been tuned with the ChatML formatting. A typical input would look like this:</p> <pre><code>"""<\|im_start\|>user Hi there!<\|im_end\|> <\|im_start\|>assistant Nice to meet you!<\|im_end\|> <\|im_start\|>user Can I ask a question?<\|im_end\|> <\|im_start\|>assistant """</code></pre> </div> <div class="section"> <h2>System Prompting</h2> <p>I would highly recommend using either Euryale's system prompt or the EVA system prompt with the model.</p> <div class="metrics-section"> <details> <summary>See Sao10k's Euryale System Prompt</summary> <pre><code>Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}. <Guidelines> • Maintain the character persona but allow it to evolve with the story. • Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant. • All types of outputs are encouraged; respond accordingly to the narrative. • Include dialogues, actions, and thoughts in each response. • Utilize all five senses to describe scenarios within {{char}}'s dialogue. • Use emotional symbols such as "!" and "~" in appropriate contexts. • Incorporate onomatopoeia when suitable. • Allow time for {{user}} to respond with their own input, respecting their agency. • Act as secondary characters and NPCs as needed, and remove them when appropriate. • When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}. </Guidelines> <Forbidden> • Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona. • Writing for, speaking, thinking, acting, or replying as {{user}} in your response. • Repetitive and monotonous outputs. • Positivity bias in your replies. • Being overly extreme or NSFW when the narrative context is inappropriate. </Forbidden> Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.</code></pre> </details> </div> </div> <div class="section"> <h2>Training</h2> <p>The training was done for 1 epoch using 8 x <a href="https://www.nvidia.com/en-us/data-center/h200/">H200s</a> GPUs graciously provided by <a href="https://huggingface.co/kalomaze">Kalomaze</a> for the fine-tuning of the model.</p> <p style="text-align: center; margin-top: 20px;"> <div class="axolotl-container"> <a href="https://github.com/OpenAccess-AI-Collective/axolotl" target="_blank"> <img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl"> </a> </div> <div class="section"> <h2>Credits</h2> <p>Thank you to <a href="https://huggingface.co/lucyknada">Lucy Knada</a>, <a href="https://huggingface.co/Ateron">Ateron</a>, <a href="https://huggingface.co/AliCat2">Alicat</a>, <a href="https://huggingface.co/intervitens">Intervitens</a>, <a href="https://huggingface.co/cgato">Cgato</a>, <a href="https://huggingface.co/kubernetes-bad">Kubernetes Bad</a> and the rest of <a href="https://huggingface.co/anthracite-org">Anthracite</a>.</p> </div> </div> </div>
ReadyArt/Francois-PE-V2-Huali-12B_EXL2_4.0bpw_H8	ReadyArt	2025-05-04T20:19:24Z	0	0	null	[ "safetensors", "mistral", "exl2", "fine-tuning", "prose", "KTO", "axolotl", "finetune", "roleplaying", "creative-writing", "dataset:PocketDoc/Dans-Personamaxx-VN", "dataset:NewEden/LIMARP-Complexity", "dataset:NewEden/PIPPA-Mega-Filtered", "dataset:NewEden/OpenCAI-ShareGPT", "dataset:NewEden/Creative_Writing-Complexity", "dataset:NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:NewEden/Books-V2-ShareGPT", "dataset:NewEden/Deepseek-V3-RP-Filtered", "dataset:NewEden/BlueSky-10K-Complexity", "dataset:NewEden/Final-Alpindale-LNs-ShareGPT", "dataset:NewEden/DeepseekRP-Filtered", "dataset:NewEden/RP-logs-V2-Experimental", "dataset:anthracite-org/kalo_opus_misc_240827", "dataset:anthracite-org/kalo_misc_part2", "dataset:NewEden/vanilla-backrooms-claude-sharegpt", "dataset:NewEden/Storium-Prefixed-Clean", "dataset:NewEden/KTO-IF-Dans", "dataset:NewEden/KTO-Instruct-Mix", "dataset:NewEden/Opus-accepted-hermes-rejected-shuffled", "base_model:Delta-Vector/Francois-PE-V2-Huali-12B", "base_model:quantized:Delta-Vector/Francois-PE-V2-Huali-12B", "4-bit", "region:us" ]	null	2025-05-04T20:13:51Z	--- base_model: - Delta-Vector/Francois-PE-V2-Huali-12B base_model_relation: quantized quantized_by: ArtusDev tags: - exl2 - fine-tuning - prose - KTO - axolotl - finetune - roleplaying - creative-writing datasets: - PocketDoc/Dans-Personamaxx-VN - NewEden/LIMARP-Complexity - NewEden/PIPPA-Mega-Filtered - NewEden/OpenCAI-ShareGPT - NewEden/Creative_Writing-Complexity - NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed - PocketDoc/Dans-Failuremaxx-Adventure-3 - NewEden/Books-V2-ShareGPT - NewEden/Deepseek-V3-RP-Filtered - NewEden/BlueSky-10K-Complexity - NewEden/Final-Alpindale-LNs-ShareGPT - NewEden/DeepseekRP-Filtered - NewEden/RP-logs-V2-Experimental - anthracite-org/kalo_opus_misc_240827 - anthracite-org/kalo_misc_part2 - NewEden/vanilla-backrooms-claude-sharegpt - NewEden/Storium-Prefixed-Clean - NewEden/KTO-IF-Dans - NewEden/KTO-Instruct-Mix - NewEden/Opus-accepted-hermes-rejected-shuffled --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #f9ffd1 0%, #e2fab5 100%); color: #000000; margin: 0; padding: 0; font-size: 16px; } .container { margin: 20px; background-color: rgba(255, 255, 255, 0.9); padding: 20px; border-radius: 12px; box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3); border: 3px solid #000000; position: relative; } .header h1 { font-size: 28px; color: #000000; margin: 0 0 20px 0; text-align: center; text-decoration: underline; } .section { margin-top: 30px; } .section h2 { font-size: 24px; color: #000000; text-align: center; text-decoration: underline; } .info p { color: #000000; line-height: 1.6; font-size: 16px; } .info img { width: 85%; border-radius: 10px; margin: 0 auto 15px; display: block; box-shadow: 0 0 20px rgba(0, 0, 0, 0.3); border: 1px solid #000000; } a { color: #000000; text-decoration: none; transition: color 0.2s ease; } a:hover { color: #538125; } .button { display: inline-block; background-color: rgba(106, 168, 79, 0.8); color: #000000; padding: 10px 20px; border-radius: 5px; cursor: pointer; text-decoration: none; transition: background-color 0.2s ease, box-shadow 0.2s ease; } .button:hover { background-color: #538125; box-shadow: 0 0 15px rgba(106, 168, 79, 0.5); } pre { background-color: rgba(240, 248, 225, 0.95); padding: 15px; border-radius: 5px; overflow-x: auto; border: 1px solid #000000; } code { font-family: 'Courier New', monospace; color: #000000; } .info-card { background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; overflow: hidden; } .info-header { background: rgba(106, 168, 79, 0.1); padding: 20px; border-bottom: 1px solid #000000; } .info-header h3 { color: #000000; margin: 0 0 10px 0; font-size: 20px; text-align: center; text-decoration: underline; } .model-tags { display: flex; gap: 8px; flex-wrap: wrap; } .model-tag { background: rgba(106, 168, 79, 0.1); color: #000000; padding: 4px 8px; border-radius: 4px; font-size: 12px; border: 1px solid #000000; } .model-composition { padding: 20px; border-bottom: 1px solid #000000; } .model-composition h4 { color: #000000; margin: 0 0 15px 0; font-size: 16px; text-align: center; text-decoration: underline; } .composition-list { list-style: none; padding: 0; margin: 0; display: grid; gap: 10px; } .composition-list li { color: #000000; display: flex; align-items: baseline; gap: 8px; } .model-component { font-weight: 500; min-width: 120px; } .model-description { padding: 20px; background: rgba(255, 255, 255, 0.5); } .metrics-section { margin-bottom: 30px; } .metrics-section details { background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; padding: 15px; margin-bottom: 15px; } .metrics-section summary { color: #000000; font-size: 18px; cursor: pointer; outline: none; padding: 5px 0; text-align: center; } .creator-section { margin: 20px 0; } .creator-badge { display: inline-flex; align-items: center; background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; padding: 10px 15px; } .creator-label { color: #000000; font-size: 14px; margin-right: 8px; } .creator-link { display: flex; align-items: center; gap: 5px; color: #000000; text-decoration: none; transition: all 0.2s ease; } .creator-name { font-weight: 600; } .creator-arrow { font-size: 16px; transition: transform 0.2s ease; } .creator-link:hover .creator-arrow { transform: translateX(3px); } .link-arrow { display: inline-block; transition: transform 0.2s ease; } a:hover .link-arrow { transform: translateX(3px); } .axolotl-container { text-align: center; margin: 30px 0; } .axolotl-container img { max-width: 300px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.3); border: 1px solid #000000; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>François-Huali 12B</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> </head> <body> <div class="container"> <div class="header"> <h1>François-PE-Huali 12B</h1> </div> <div class="info"> <img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/qUdM9qgZWeyfxwdds9QRZ.jpeg" alt="Model banner"> <div style="text-align: center;"> <div class="creator-section"> <div class="creator-badge"> <span class="creator-label">Created by</span> <a href="https://huggingface.co/Delta-Vector" target="_blank" class="creator-link"> <span class="creator-name">Delta-Vector</span> <span class="creator-arrow">→</span> </a> </div> </div> <div class="model-info"> <h2>Model Information</h2> <div class="info-card"> <div class="info-header"> <h3>François-Huali 12B V2</h3> <div class="model-tags"> <span class="model-tag">KTO enhanced</span> <span class="model-tag">Dans-Personality-Engine finetune</span> <span class="model-tag">Creative & Refreshing Prose</span> </div> </div> <div class="model-description"> <p>A sequel! A sequel to my Francois-PE/Huali train, Built ontop of Dans-PE-12B that was finetuned with Light novels, Books, Roleplay logs, to change writing style to be rather short & sweet, Huali uses KTO to increase coherency and prose. The model aims to have a different style of writing/prose then any other NeMo train.</p> </div> </div> </div> <div class="section"> <h2>Quantized Versions</h2> <div class="info-card"> <div class="model-composition"> <h4>Available Downloads</h4> <ul class="composition-list"> <li><span class="model-component"><a href="" target="_blank">GGUF Format</a></span>For hosting Locally.(Coming soon!)</li> </ul> </div> </div> </div> <div class="section"> <h2>Prompting</h2> <p>Model has been tuned with the ChatML formatting. A typical input would look like this:</p> <pre><code>"""<\|im_start\|>user Hi there!<\|im_end\|> <\|im_start\|>assistant Nice to meet you!<\|im_end\|> <\|im_start\|>user Can I ask a question?<\|im_end\|> <\|im_start\|>assistant """</code></pre> </div> <div class="section"> <h2>System Prompting</h2> <p>I would highly recommend using either Euryale's system prompt or the EVA system prompt with the model.</p> <div class="metrics-section"> <details> <summary>See Sao10k's Euryale System Prompt</summary> <pre><code>Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}. <Guidelines> • Maintain the character persona but allow it to evolve with the story. • Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant. • All types of outputs are encouraged; respond accordingly to the narrative. • Include dialogues, actions, and thoughts in each response. • Utilize all five senses to describe scenarios within {{char}}'s dialogue. • Use emotional symbols such as "!" and "~" in appropriate contexts. • Incorporate onomatopoeia when suitable. • Allow time for {{user}} to respond with their own input, respecting their agency. • Act as secondary characters and NPCs as needed, and remove them when appropriate. • When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}. </Guidelines> <Forbidden> • Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona. • Writing for, speaking, thinking, acting, or replying as {{user}} in your response. • Repetitive and monotonous outputs. • Positivity bias in your replies. • Being overly extreme or NSFW when the narrative context is inappropriate. </Forbidden> Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.</code></pre> </details> </div> </div> <div class="section"> <h2>Training</h2> <p>The training was done for 1 epoch using 8 x <a href="https://www.nvidia.com/en-us/data-center/h200/">H200s</a> GPUs graciously provided by <a href="https://huggingface.co/kalomaze">Kalomaze</a> for the fine-tuning of the model.</p> <p style="text-align: center; margin-top: 20px;"> <div class="axolotl-container"> <a href="https://github.com/OpenAccess-AI-Collective/axolotl" target="_blank"> <img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl"> </a> </div> <div class="section"> <h2>Credits</h2> <p>Thank you to <a href="https://huggingface.co/lucyknada">Lucy Knada</a>, <a href="https://huggingface.co/Ateron">Ateron</a>, <a href="https://huggingface.co/AliCat2">Alicat</a>, <a href="https://huggingface.co/intervitens">Intervitens</a>, <a href="https://huggingface.co/cgato">Cgato</a>, <a href="https://huggingface.co/kubernetes-bad">Kubernetes Bad</a> and the rest of <a href="https://huggingface.co/anthracite-org">Anthracite</a>.</p> </div> </div> </div>
ReadyArt/Francois-PE-V2-Huali-12B_EXL2_3.5bpw_H8	ReadyArt	2025-05-04T20:19:16Z	0	0	null	[ "safetensors", "mistral", "exl2", "fine-tuning", "prose", "KTO", "axolotl", "finetune", "roleplaying", "creative-writing", "dataset:PocketDoc/Dans-Personamaxx-VN", "dataset:NewEden/LIMARP-Complexity", "dataset:NewEden/PIPPA-Mega-Filtered", "dataset:NewEden/OpenCAI-ShareGPT", "dataset:NewEden/Creative_Writing-Complexity", "dataset:NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:NewEden/Books-V2-ShareGPT", "dataset:NewEden/Deepseek-V3-RP-Filtered", "dataset:NewEden/BlueSky-10K-Complexity", "dataset:NewEden/Final-Alpindale-LNs-ShareGPT", "dataset:NewEden/DeepseekRP-Filtered", "dataset:NewEden/RP-logs-V2-Experimental", "dataset:anthracite-org/kalo_opus_misc_240827", "dataset:anthracite-org/kalo_misc_part2", "dataset:NewEden/vanilla-backrooms-claude-sharegpt", "dataset:NewEden/Storium-Prefixed-Clean", "dataset:NewEden/KTO-IF-Dans", "dataset:NewEden/KTO-Instruct-Mix", "dataset:NewEden/Opus-accepted-hermes-rejected-shuffled", "base_model:Delta-Vector/Francois-PE-V2-Huali-12B", "base_model:quantized:Delta-Vector/Francois-PE-V2-Huali-12B", "region:us" ]	null	2025-05-04T20:13:11Z	--- base_model: - Delta-Vector/Francois-PE-V2-Huali-12B base_model_relation: quantized quantized_by: ArtusDev tags: - exl2 - fine-tuning - prose - KTO - axolotl - finetune - roleplaying - creative-writing datasets: - PocketDoc/Dans-Personamaxx-VN - NewEden/LIMARP-Complexity - NewEden/PIPPA-Mega-Filtered - NewEden/OpenCAI-ShareGPT - NewEden/Creative_Writing-Complexity - NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed - PocketDoc/Dans-Failuremaxx-Adventure-3 - NewEden/Books-V2-ShareGPT - NewEden/Deepseek-V3-RP-Filtered - NewEden/BlueSky-10K-Complexity - NewEden/Final-Alpindale-LNs-ShareGPT - NewEden/DeepseekRP-Filtered - NewEden/RP-logs-V2-Experimental - anthracite-org/kalo_opus_misc_240827 - anthracite-org/kalo_misc_part2 - NewEden/vanilla-backrooms-claude-sharegpt - NewEden/Storium-Prefixed-Clean - NewEden/KTO-IF-Dans - NewEden/KTO-Instruct-Mix - NewEden/Opus-accepted-hermes-rejected-shuffled --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #f9ffd1 0%, #e2fab5 100%); color: #000000; margin: 0; padding: 0; font-size: 16px; } .container { margin: 20px; background-color: rgba(255, 255, 255, 0.9); padding: 20px; border-radius: 12px; box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3); border: 3px solid #000000; position: relative; } .header h1 { font-size: 28px; color: #000000; margin: 0 0 20px 0; text-align: center; text-decoration: underline; } .section { margin-top: 30px; } .section h2 { font-size: 24px; color: #000000; text-align: center; text-decoration: underline; } .info p { color: #000000; line-height: 1.6; font-size: 16px; } .info img { width: 85%; border-radius: 10px; margin: 0 auto 15px; display: block; box-shadow: 0 0 20px rgba(0, 0, 0, 0.3); border: 1px solid #000000; } a { color: #000000; text-decoration: none; transition: color 0.2s ease; } a:hover { color: #538125; } .button { display: inline-block; background-color: rgba(106, 168, 79, 0.8); color: #000000; padding: 10px 20px; border-radius: 5px; cursor: pointer; text-decoration: none; transition: background-color 0.2s ease, box-shadow 0.2s ease; } .button:hover { background-color: #538125; box-shadow: 0 0 15px rgba(106, 168, 79, 0.5); } pre { background-color: rgba(240, 248, 225, 0.95); padding: 15px; border-radius: 5px; overflow-x: auto; border: 1px solid #000000; } code { font-family: 'Courier New', monospace; color: #000000; } .info-card { background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; overflow: hidden; } .info-header { background: rgba(106, 168, 79, 0.1); padding: 20px; border-bottom: 1px solid #000000; } .info-header h3 { color: #000000; margin: 0 0 10px 0; font-size: 20px; text-align: center; text-decoration: underline; } .model-tags { display: flex; gap: 8px; flex-wrap: wrap; } .model-tag { background: rgba(106, 168, 79, 0.1); color: #000000; padding: 4px 8px; border-radius: 4px; font-size: 12px; border: 1px solid #000000; } .model-composition { padding: 20px; border-bottom: 1px solid #000000; } .model-composition h4 { color: #000000; margin: 0 0 15px 0; font-size: 16px; text-align: center; text-decoration: underline; } .composition-list { list-style: none; padding: 0; margin: 0; display: grid; gap: 10px; } .composition-list li { color: #000000; display: flex; align-items: baseline; gap: 8px; } .model-component { font-weight: 500; min-width: 120px; } .model-description { padding: 20px; background: rgba(255, 255, 255, 0.5); } .metrics-section { margin-bottom: 30px; } .metrics-section details { background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; padding: 15px; margin-bottom: 15px; } .metrics-section summary { color: #000000; font-size: 18px; cursor: pointer; outline: none; padding: 5px 0; text-align: center; } .creator-section { margin: 20px 0; } .creator-badge { display: inline-flex; align-items: center; background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; padding: 10px 15px; } .creator-label { color: #000000; font-size: 14px; margin-right: 8px; } .creator-link { display: flex; align-items: center; gap: 5px; color: #000000; text-decoration: none; transition: all 0.2s ease; } .creator-name { font-weight: 600; } .creator-arrow { font-size: 16px; transition: transform 0.2s ease; } .creator-link:hover .creator-arrow { transform: translateX(3px); } .link-arrow { display: inline-block; transition: transform 0.2s ease; } a:hover .link-arrow { transform: translateX(3px); } .axolotl-container { text-align: center; margin: 30px 0; } .axolotl-container img { max-width: 300px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.3); border: 1px solid #000000; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>François-Huali 12B</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> </head> <body> <div class="container"> <div class="header"> <h1>François-PE-Huali 12B</h1> </div> <div class="info"> <img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/qUdM9qgZWeyfxwdds9QRZ.jpeg" alt="Model banner"> <div style="text-align: center;"> <div class="creator-section"> <div class="creator-badge"> <span class="creator-label">Created by</span> <a href="https://huggingface.co/Delta-Vector" target="_blank" class="creator-link"> <span class="creator-name">Delta-Vector</span> <span class="creator-arrow">→</span> </a> </div> </div> <div class="model-info"> <h2>Model Information</h2> <div class="info-card"> <div class="info-header"> <h3>François-Huali 12B V2</h3> <div class="model-tags"> <span class="model-tag">KTO enhanced</span> <span class="model-tag">Dans-Personality-Engine finetune</span> <span class="model-tag">Creative & Refreshing Prose</span> </div> </div> <div class="model-description"> <p>A sequel! A sequel to my Francois-PE/Huali train, Built ontop of Dans-PE-12B that was finetuned with Light novels, Books, Roleplay logs, to change writing style to be rather short & sweet, Huali uses KTO to increase coherency and prose. The model aims to have a different style of writing/prose then any other NeMo train.</p> </div> </div> </div> <div class="section"> <h2>Quantized Versions</h2> <div class="info-card"> <div class="model-composition"> <h4>Available Downloads</h4> <ul class="composition-list"> <li><span class="model-component"><a href="" target="_blank">GGUF Format</a></span>For hosting Locally.(Coming soon!)</li> </ul> </div> </div> </div> <div class="section"> <h2>Prompting</h2> <p>Model has been tuned with the ChatML formatting. A typical input would look like this:</p> <pre><code>"""<\|im_start\|>user Hi there!<\|im_end\|> <\|im_start\|>assistant Nice to meet you!<\|im_end\|> <\|im_start\|>user Can I ask a question?<\|im_end\|> <\|im_start\|>assistant """</code></pre> </div> <div class="section"> <h2>System Prompting</h2> <p>I would highly recommend using either Euryale's system prompt or the EVA system prompt with the model.</p> <div class="metrics-section"> <details> <summary>See Sao10k's Euryale System Prompt</summary> <pre><code>Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}. <Guidelines> • Maintain the character persona but allow it to evolve with the story. • Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant. • All types of outputs are encouraged; respond accordingly to the narrative. • Include dialogues, actions, and thoughts in each response. • Utilize all five senses to describe scenarios within {{char}}'s dialogue. • Use emotional symbols such as "!" and "~" in appropriate contexts. • Incorporate onomatopoeia when suitable. • Allow time for {{user}} to respond with their own input, respecting their agency. • Act as secondary characters and NPCs as needed, and remove them when appropriate. • When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}. </Guidelines> <Forbidden> • Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona. • Writing for, speaking, thinking, acting, or replying as {{user}} in your response. • Repetitive and monotonous outputs. • Positivity bias in your replies. • Being overly extreme or NSFW when the narrative context is inappropriate. </Forbidden> Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.</code></pre> </details> </div> </div> <div class="section"> <h2>Training</h2> <p>The training was done for 1 epoch using 8 x <a href="https://www.nvidia.com/en-us/data-center/h200/">H200s</a> GPUs graciously provided by <a href="https://huggingface.co/kalomaze">Kalomaze</a> for the fine-tuning of the model.</p> <p style="text-align: center; margin-top: 20px;"> <div class="axolotl-container"> <a href="https://github.com/OpenAccess-AI-Collective/axolotl" target="_blank"> <img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl"> </a> </div> <div class="section"> <h2>Credits</h2> <p>Thank you to <a href="https://huggingface.co/lucyknada">Lucy Knada</a>, <a href="https://huggingface.co/Ateron">Ateron</a>, <a href="https://huggingface.co/AliCat2">Alicat</a>, <a href="https://huggingface.co/intervitens">Intervitens</a>, <a href="https://huggingface.co/cgato">Cgato</a>, <a href="https://huggingface.co/kubernetes-bad">Kubernetes Bad</a> and the rest of <a href="https://huggingface.co/anthracite-org">Anthracite</a>.</p> </div> </div> </div>
ReadyArt/Francois-PE-V2-Huali-12B_EXL2_2.5bpw_H8	ReadyArt	2025-05-04T20:18:16Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "exl2", "fine-tuning", "prose", "KTO", "axolotl", "finetune", "roleplaying", "creative-writing", "conversational", "dataset:PocketDoc/Dans-Personamaxx-VN", "dataset:NewEden/LIMARP-Complexity", "dataset:NewEden/PIPPA-Mega-Filtered", "dataset:NewEden/OpenCAI-ShareGPT", "dataset:NewEden/Creative_Writing-Complexity", "dataset:NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:NewEden/Books-V2-ShareGPT", "dataset:NewEden/Deepseek-V3-RP-Filtered", "dataset:NewEden/BlueSky-10K-Complexity", "dataset:NewEden/Final-Alpindale-LNs-ShareGPT", "dataset:NewEden/DeepseekRP-Filtered", "dataset:NewEden/RP-logs-V2-Experimental", "dataset:anthracite-org/kalo_opus_misc_240827", "dataset:anthracite-org/kalo_misc_part2", "dataset:NewEden/vanilla-backrooms-claude-sharegpt", "dataset:NewEden/Storium-Prefixed-Clean", "dataset:NewEden/KTO-IF-Dans", "dataset:NewEden/KTO-Instruct-Mix", "dataset:NewEden/Opus-accepted-hermes-rejected-shuffled", "base_model:Delta-Vector/Francois-PE-V2-Huali-12B", "base_model:quantized:Delta-Vector/Francois-PE-V2-Huali-12B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T20:12:05Z	--- base_model: - Delta-Vector/Francois-PE-V2-Huali-12B base_model_relation: quantized quantized_by: ArtusDev library_name: transformers tags: - exl2 - fine-tuning - prose - KTO - axolotl - finetune - roleplaying - creative-writing datasets: - PocketDoc/Dans-Personamaxx-VN - NewEden/LIMARP-Complexity - NewEden/PIPPA-Mega-Filtered - NewEden/OpenCAI-ShareGPT - NewEden/Creative_Writing-Complexity - NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed - PocketDoc/Dans-Failuremaxx-Adventure-3 - NewEden/Books-V2-ShareGPT - NewEden/Deepseek-V3-RP-Filtered - NewEden/BlueSky-10K-Complexity - NewEden/Final-Alpindale-LNs-ShareGPT - NewEden/DeepseekRP-Filtered - NewEden/RP-logs-V2-Experimental - anthracite-org/kalo_opus_misc_240827 - anthracite-org/kalo_misc_part2 - NewEden/vanilla-backrooms-claude-sharegpt - NewEden/Storium-Prefixed-Clean - NewEden/KTO-IF-Dans - NewEden/KTO-Instruct-Mix - NewEden/Opus-accepted-hermes-rejected-shuffled --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #f9ffd1 0%, #e2fab5 100%); color: #000000; margin: 0; padding: 0; font-size: 16px; } .container { margin: 20px; background-color: rgba(255, 255, 255, 0.9); padding: 20px; border-radius: 12px; box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3); border: 3px solid #000000; position: relative; } .header h1 { font-size: 28px; color: #000000; margin: 0 0 20px 0; text-align: center; text-decoration: underline; } .section { margin-top: 30px; } .section h2 { font-size: 24px; color: #000000; text-align: center; text-decoration: underline; } .info p { color: #000000; line-height: 1.6; font-size: 16px; } .info img { width: 85%; border-radius: 10px; margin: 0 auto 15px; display: block; box-shadow: 0 0 20px rgba(0, 0, 0, 0.3); border: 1px solid #000000; } a { color: #000000; text-decoration: none; transition: color 0.2s ease; } a:hover { color: #538125; } .button { display: inline-block; background-color: rgba(106, 168, 79, 0.8); color: #000000; padding: 10px 20px; border-radius: 5px; cursor: pointer; text-decoration: none; transition: background-color 0.2s ease, box-shadow 0.2s ease; } .button:hover { background-color: #538125; box-shadow: 0 0 15px rgba(106, 168, 79, 0.5); } pre { background-color: rgba(240, 248, 225, 0.95); padding: 15px; border-radius: 5px; overflow-x: auto; border: 1px solid #000000; } code { font-family: 'Courier New', monospace; color: #000000; } .info-card { background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; overflow: hidden; } .info-header { background: rgba(106, 168, 79, 0.1); padding: 20px; border-bottom: 1px solid #000000; } .info-header h3 { color: #000000; margin: 0 0 10px 0; font-size: 20px; text-align: center; text-decoration: underline; } .model-tags { display: flex; gap: 8px; flex-wrap: wrap; } .model-tag { background: rgba(106, 168, 79, 0.1); color: #000000; padding: 4px 8px; border-radius: 4px; font-size: 12px; border: 1px solid #000000; } .model-composition { padding: 20px; border-bottom: 1px solid #000000; } .model-composition h4 { color: #000000; margin: 0 0 15px 0; font-size: 16px; text-align: center; text-decoration: underline; } .composition-list { list-style: none; padding: 0; margin: 0; display: grid; gap: 10px; } .composition-list li { color: #000000; display: flex; align-items: baseline; gap: 8px; } .model-component { font-weight: 500; min-width: 120px; } .model-description { padding: 20px; background: rgba(255, 255, 255, 0.5); } .metrics-section { margin-bottom: 30px; } .metrics-section details { background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; padding: 15px; margin-bottom: 15px; } .metrics-section summary { color: #000000; font-size: 18px; cursor: pointer; outline: none; padding: 5px 0; text-align: center; } .creator-section { margin: 20px 0; } .creator-badge { display: inline-flex; align-items: center; background: rgba(249, 255, 235, 0.95); border: 1px solid #000000; border-radius: 8px; padding: 10px 15px; } .creator-label { color: #000000; font-size: 14px; margin-right: 8px; } .creator-link { display: flex; align-items: center; gap: 5px; color: #000000; text-decoration: none; transition: all 0.2s ease; } .creator-name { font-weight: 600; } .creator-arrow { font-size: 16px; transition: transform 0.2s ease; } .creator-link:hover .creator-arrow { transform: translateX(3px); } .link-arrow { display: inline-block; transition: transform 0.2s ease; } a:hover .link-arrow { transform: translateX(3px); } .axolotl-container { text-align: center; margin: 30px 0; } .axolotl-container img { max-width: 300px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.3); border: 1px solid #000000; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>François-Huali 12B</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> </head> <body> <div class="container"> <div class="header"> <h1>François-PE-Huali 12B</h1> </div> <div class="info"> <img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/qUdM9qgZWeyfxwdds9QRZ.jpeg" alt="Model banner"> <div style="text-align: center;"> <div class="creator-section"> <div class="creator-badge"> <span class="creator-label">Created by</span> <a href="https://huggingface.co/Delta-Vector" target="_blank" class="creator-link"> <span class="creator-name">Delta-Vector</span> <span class="creator-arrow">→</span> </a> </div> </div> <div class="model-info"> <h2>Model Information</h2> <div class="info-card"> <div class="info-header"> <h3>François-Huali 12B V2</h3> <div class="model-tags"> <span class="model-tag">KTO enhanced</span> <span class="model-tag">Dans-Personality-Engine finetune</span> <span class="model-tag">Creative & Refreshing Prose</span> </div> </div> <div class="model-description"> <p>A sequel! A sequel to my Francois-PE/Huali train, Built ontop of Dans-PE-12B that was finetuned with Light novels, Books, Roleplay logs, to change writing style to be rather short & sweet, Huali uses KTO to increase coherency and prose. The model aims to have a different style of writing/prose then any other NeMo train.</p> </div> </div> </div> <div class="section"> <h2>Quantized Versions</h2> <div class="info-card"> <div class="model-composition"> <h4>Available Downloads</h4> <ul class="composition-list"> <li><span class="model-component"><a href="" target="_blank">GGUF Format</a></span>For hosting Locally.(Coming soon!)</li> </ul> </div> </div> </div> <div class="section"> <h2>Prompting</h2> <p>Model has been tuned with the ChatML formatting. A typical input would look like this:</p> <pre><code>"""<\|im_start\|>user Hi there!<\|im_end\|> <\|im_start\|>assistant Nice to meet you!<\|im_end\|> <\|im_start\|>user Can I ask a question?<\|im_end\|> <\|im_start\|>assistant """</code></pre> </div> <div class="section"> <h2>System Prompting</h2> <p>I would highly recommend using either Euryale's system prompt or the EVA system prompt with the model.</p> <div class="metrics-section"> <details> <summary>See Sao10k's Euryale System Prompt</summary> <pre><code>Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}. <Guidelines> • Maintain the character persona but allow it to evolve with the story. • Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant. • All types of outputs are encouraged; respond accordingly to the narrative. • Include dialogues, actions, and thoughts in each response. • Utilize all five senses to describe scenarios within {{char}}'s dialogue. • Use emotional symbols such as "!" and "~" in appropriate contexts. • Incorporate onomatopoeia when suitable. • Allow time for {{user}} to respond with their own input, respecting their agency. • Act as secondary characters and NPCs as needed, and remove them when appropriate. • When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}. </Guidelines> <Forbidden> • Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona. • Writing for, speaking, thinking, acting, or replying as {{user}} in your response. • Repetitive and monotonous outputs. • Positivity bias in your replies. • Being overly extreme or NSFW when the narrative context is inappropriate. </Forbidden> Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.</code></pre> </details> </div> </div> <div class="section"> <h2>Training</h2> <p>The training was done for 1 epoch using 8 x <a href="https://www.nvidia.com/en-us/data-center/h200/">H200s</a> GPUs graciously provided by <a href="https://huggingface.co/kalomaze">Kalomaze</a> for the fine-tuning of the model.</p> <p style="text-align: center; margin-top: 20px;"> <div class="axolotl-container"> <a href="https://github.com/OpenAccess-AI-Collective/axolotl" target="_blank"> <img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl"> </a> </div> <div class="section"> <h2>Credits</h2> <p>Thank you to <a href="https://huggingface.co/lucyknada">Lucy Knada</a>, <a href="https://huggingface.co/Ateron">Ateron</a>, <a href="https://huggingface.co/AliCat2">Alicat</a>, <a href="https://huggingface.co/intervitens">Intervitens</a>, <a href="https://huggingface.co/cgato">Cgato</a>, <a href="https://huggingface.co/kubernetes-bad">Kubernetes Bad</a> and the rest of <a href="https://huggingface.co/anthracite-org">Anthracite</a>.</p> </div> </div> </div>
felixZzz/wmixnoBoolean-orz-ours-d100-len5120-0427T17_47_21-step_00128	felixZzz	2025-05-04T20:18:16Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T20:10:25Z	--- 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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thuanan/Llama-3.2-1B-RM-DPO	thuanan	2025-05-04T20:17:52Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T17:58:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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TareksLab/SCETest3-70B	TareksLab	2025-05-04T20:17:12Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2408.07990", "base_model:LatitudeGames/Wayfarer-Large-70B-Llama-3.3", "base_model:merge:LatitudeGames/Wayfarer-Large-70B-Llama-3.3", "base_model:ReadyArt/Forgotten-Safeword-70B-v5.0", "base_model:merge:ReadyArt/Forgotten-Safeword-70B-v5.0", "base_model:SicariusSicariiStuff/Negative_LLAMA_70B", "base_model:merge:SicariusSicariiStuff/Negative_LLAMA_70B", "base_model:TheDrummer/Fallen-Llama-3.3-R1-70B-v1", "base_model:merge:TheDrummer/Fallen-Llama-3.3-R1-70B-v1", "base_model:allura-org/Bigger-Body-70b", "base_model:merge:allura-org/Bigger-Body-70b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T19:40:08Z	--- base_model: - allura-org/Bigger-Body-70b - LatitudeGames/Wayfarer-Large-70B-Llama-3.3 - TheDrummer/Fallen-Llama-3.3-R1-70B-v1 - SicariusSicariiStuff/Negative_LLAMA_70B - ReadyArt/Forgotten-Safeword-70B-v5.0 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [SicariusSicariiStuff/Negative_LLAMA_70B](https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B) as a base. ### Models Merged The following models were included in the merge: * [allura-org/Bigger-Body-70b](https://huggingface.co/allura-org/Bigger-Body-70b) * [LatitudeGames/Wayfarer-Large-70B-Llama-3.3](https://huggingface.co/LatitudeGames/Wayfarer-Large-70B-Llama-3.3) * [TheDrummer/Fallen-Llama-3.3-R1-70B-v1](https://huggingface.co/TheDrummer/Fallen-Llama-3.3-R1-70B-v1) * [ReadyArt/Forgotten-Safeword-70B-v5.0](https://huggingface.co/ReadyArt/Forgotten-Safeword-70B-v5.0) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TheDrummer/Fallen-Llama-3.3-R1-70B-v1 parameters: weight: 0.20 density: 0.9 select_topk: 0.10 lambda: 1.0 - model: ReadyArt/Forgotten-Safeword-70B-v5.0 parameters: weight: 0.20 density: 0.9 select_topk: 0.10 lambda: 1.0 - model: allura-org/Bigger-Body-70b parameters: weight: 0.20 density: 0.9 select_topk: 0.10 lambda: 1.0 - model: LatitudeGames/Wayfarer-Large-70B-Llama-3.3 parameters: weight: 0.20 density: 0.9 select_topk: 0.10 lambda: 1.0 - model: SicariusSicariiStuff/Negative_LLAMA_70B parameters: weight: 0.20 density: 0.9 select_topk: 0.10 lambda: 1.0 base_model: SicariusSicariiStuff/Negative_LLAMA_70B merge_method: sce parameters: normalize: false int8_mask: true tokenizer: source: SicariusSicariiStuff/Negative_LLAMA_70B chat_template: llama3 dtype: bfloat16 ```
Hachipo/OpenCoder-8B-Base-MIFT-ja_10000_2	Hachipo	2025-05-04T20:16:31Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T20:12:48Z	--- 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. 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bruhzair/ignore-merge-16	bruhzair	2025-05-04T20:12:02Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T19:40:07Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # mnh2 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 Passthrough merge method. ### Models Merged The following models were included in the merge: * /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: bfloat16 merge_method: passthrough modules: default: slices: - sources: - layer_range: [0, 4] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [2, 4] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [4, 8] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [6, 8] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [8, 12] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [10, 12] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [12, 16] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [14, 16] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [16, 20] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [18, 20] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [20, 24] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [22, 24] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [24, 28] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [26, 28] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [28, 32] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [30, 32] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [32, 36] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [34, 36] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [36, 40] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [38, 40] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [40, 44] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [42, 44] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [44, 48] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [46, 48] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [48, 52] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [50, 52] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [52, 56] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [54, 56] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [56, 60] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [58, 60] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [60, 64] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [62, 64] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [64, 68] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [66, 68] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [68, 72] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [70, 72] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [72, 76] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [74, 76] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - layer_range: [76, 80] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d - sources: - layer_range: [78, 80] model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 ```
azigi-ooh-azigi-video/azigi.ooh.azigi.video.azigi.with.five.guys.real.video.telegram	azigi-ooh-azigi-video	2025-05-04T20:10:44Z	0	0	null	[ "region:us" ]	null	2025-05-04T20:08:50Z	<a href="https://everyvlogger.com/e4rf34e"> 🌐 Click Here To link (FULL 18++} azigi ooh azigi video azigi with five guys real video telegram) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://everyvlogger.com/e4rf34e"> 🌐 FULL 18++} azigi ooh azigi video azigi with five guys real video telegram
unrented5443/sn11-v5-2-14	unrented5443	2025-05-04T20:10:36Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T20:10:33Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF	mradermacher	2025-05-04T20:09:26Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:HarethahMo/qwen2.5-1.5B-extended-refusal-2-0-abliterated", "base_model:quantized:HarethahMo/qwen2.5-1.5B-extended-refusal-2-0-abliterated", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T19:50:43Z	--- base_model: HarethahMo/qwen2.5-1.5B-extended-refusal-2-0-abliterated language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/HarethahMo/qwen2.5-1.5B-extended-refusal-2-0-abliterated <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.Q3_K_M.gguf) \| Q3_K_M \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.Q4_K_S.gguf) \| Q4_K_S \| 1.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.Q8_0.gguf) \| Q8_0 \| 1.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-extended-refusal-2-0-abliterated-GGUF/resolve/main/qwen2.5-1.5B-extended-refusal-2-0-abliterated.f16.gguf) \| f16 \| 3.2 \| 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/Chatbot-Phi2-GGUF	mradermacher	2025-05-04T20:05:03Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ShabanEjupi/Chatbot-Phi2", "base_model:quantized:ShabanEjupi/Chatbot-Phi2", "endpoints_compatible", "region:us" ]	null	2025-05-04T19:43:00Z	--- base_model: ShabanEjupi/Chatbot-Phi2 language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ShabanEjupi/Chatbot-Phi2 <!-- 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/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.Q2_K.gguf) \| Q2_K \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.Q3_K_S.gguf) \| Q3_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.Q3_K_M.gguf) \| Q3_K_M \| 1.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.IQ4_XS.gguf) \| IQ4_XS \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.Q3_K_L.gguf) \| Q3_K_L \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.Q4_K_S.gguf) \| Q4_K_S \| 1.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.Q4_K_M.gguf) \| Q4_K_M \| 1.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.Q5_K_S.gguf) \| Q5_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.Q5_K_M.gguf) \| Q5_K_M \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.Q6_K.gguf) \| Q6_K \| 2.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.Q8_0.gguf) \| Q8_0 \| 3.1 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Chatbot-Phi2-GGUF/resolve/main/Chatbot-Phi2.f16.gguf) \| f16 \| 5.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
fedovtt/5f05986b-9ffe-46b0-ba53-9f1d42676237	fedovtt	2025-05-04T20:02:40Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-135M", "base_model:adapter:unsloth/SmolLM2-135M", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T19:56:08Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-135M tags: - axolotl - generated_from_trainer model-index: - name: 5f05986b-9ffe-46b0-ba53-9f1d42676237 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM2-135M bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ed91641d1d020fb6_train_data.json ds_type: json format: custom path: /workspace/input_data/ed91641d1d020fb6_train_data.json type: field_instruction: questions field_output: answers format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: fedovtt/5f05986b-9ffe-46b0-ba53-9f1d42676237 hub_repo: null hub_strategy: end hub_token: null learning_rate: 3.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 10 mixed_precision: bf16 mlflow_experiment_name: /tmp/ed91641d1d020fb6_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 82c8d945-2385-4451-b8b6-4d94d5b59423 wandb_project: s56-28 wandb_run: your_name wandb_runid: 82c8d945-2385-4451-b8b6-4d94d5b59423 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 5f05986b-9ffe-46b0-ba53-9f1d42676237 This model is a fine-tuned version of [unsloth/SmolLM2-135M](https://huggingface.co/unsloth/SmolLM2-135M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9801 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9958 \| 0.0395 \| 150 \| 0.9801 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
RajeevanL/tamil-roberta_v-2	RajeevanL	2025-05-04T20:02:16Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2025-05-04T20:00:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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mradermacher/qwen2.5-1.5B-base-abliterated-GGUF	mradermacher	2025-05-04T20:00:28Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:HarethahMo/qwen2.5-1.5B-base-abliterated", "base_model:quantized:HarethahMo/qwen2.5-1.5B-base-abliterated", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T19:45:24Z	--- base_model: HarethahMo/qwen2.5-1.5B-base-abliterated language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/HarethahMo/qwen2.5-1.5B-base-abliterated <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.Q3_K_M.gguf) \| Q3_K_M \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.Q4_K_S.gguf) \| Q4_K_S \| 1.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.Q8_0.gguf) \| Q8_0 \| 1.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-1.5B-base-abliterated-GGUF/resolve/main/qwen2.5-1.5B-base-abliterated.f16.gguf) \| f16 \| 3.2 \| 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 -->
jfrost10/legal-ft-c1181fb2-6827-4872-aa90-3ba6c5e5d7cb	jfrost10	2025-05-04T20:00:27Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:156", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:Snowflake/snowflake-arctic-embed-l", "base_model:finetune:Snowflake/snowflake-arctic-embed-l", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-04T19:59:07Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:156 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-l widget: - source_sentence: What new type of LLM was introduced in the final quarter of 2024, and which models exemplify this development? sentences: - 'Then in February, Meta released Llama. And a few weeks later in March, Georgi Gerganov released code that got it working on a MacBook. I wrote about how Large language models are having their Stable Diffusion moment, and with hindsight that was a very good call! This unleashed a whirlwind of innovation, which was accelerated further in July when Meta released Llama 2—an improved version which, crucially, included permission for commercial use. Today there are literally thousands of LLMs that can be run locally, on all manner of different devices.' - 'Now that those features are rolling out they’re pretty weak. As an LLM power-user I know what these models are capable of, and Apple’s LLM features offer a pale imitation of what a frontier LLM can do. Instead we’re getting notification summaries that misrepresent news headlines and writing assistant tools that I’ve not found useful at all. Genmoji are kind of fun though. The rise of inference-scaling “reasoning” models The most interesting development in the final quarter of 2024 was the introduction of a new shape of LLM, exemplified by OpenAI’s o1 models—initially released as o1-preview and o1-mini on September 12th.' - 'Prompt injection is a natural consequence of this gulibility. I’ve seen precious little progress on tackling that problem in 2024, and we’ve been talking about it since September 2022. I’m beginning to see the most popular idea of “agents” as dependent on AGI itself. A model that’s robust against gulliblity is a very tall order indeed. Evals really matter Anthropic’s Amanda Askell (responsible for much of the work behind Claude’s Character):' - source_sentence: What is the new way to scale a model mentioned in the context? sentences: - 'There’s now a fascinating ecosystem of people training their own models on top of these foundations, publishing those models, building fine-tuning datasets and sharing those too. The Hugging Face Open LLM Leaderboard is one place that tracks these. I can’t even attempt to count them, and any count would be out-of-date within a few hours. The best overall openly licensed LLM at any time is rarely a foundation model: instead, it’s whichever fine-tuned community model has most recently discovered the best combination of fine-tuning data. This is a huge advantage for open over closed models: the closed, hosted models don’t have thousands of researchers and hobbyists around the world collaborating and competing to improve them.' - 'The biggest innovation here is that it opens up a new way to scale a model: instead of improving model performance purely through additional compute at training time, models can now take on harder problems by spending more compute on inference. The sequel to o1, o3 (they skipped “o2” for European trademark reasons) was announced on 20th December with an impressive result against the ARC-AGI benchmark, albeit one that likely involved more than $1,000,000 of compute time expense! o3 is expected to ship in January. I doubt many people have real-world problems that would benefit from that level of compute expenditure—I certainly don’t!—but it appears to be a genuine next step in LLM architecture for taking on much harder problems.' - 'Language Models are gullible. They “believe” what we tell them—what’s in their training data, then what’s in the fine-tuning data, then what’s in the prompt. In order to be useful tools for us, we need them to believe what we feed them! But it turns out a lot of the things we want to build need them not to be gullible. Everyone wants an AI personal assistant. If you hired a real-world personal assistant who believed everything that anyone told them, you would quickly find that their ability to positively impact your life was severely limited.' - source_sentence: What new feature did the Chatbot Arena team introduce in December, and how is its effectiveness demonstrated? sentences: - 'Your browser does not support the audio element. OpenAI aren’t the only group with a multi-modal audio model. Google’s Gemini also accepts audio input, and the Google Gemini apps can speak in a similar way to ChatGPT now. Amazon also pre-announced voice mode for Amazon Nova, but that’s meant to roll out in Q1 of 2025. Google’s NotebookLM, released in September, took audio output to a new level by producing spookily realistic conversations between two “podcast hosts” about anything you fed into their tool. They later added custom instructions, so naturally I turned them into pelicans: Your browser does not support the audio element.' - 'Then in December, the Chatbot Arena team introduced a whole new leaderboard for this feature, driven by users building the same interactive app twice with two different models and voting on the answer. Hard to come up with a more convincing argument that this feature is now a commodity that can be effectively implemented against all of the leading models. I’ve been tinkering with a version of this myself for my Datasette project, with the goal of letting users use prompts to build and iterate on custom widgets and data visualizations against their own data. I also figured out a similar pattern for writing one-shot Python programs, enabled by uv.' - 'These price drops are driven by two factors: increased competition and increased efficiency. The efficiency thing is really important for everyone who is concerned about the environmental impact of LLMs. These price drops tie directly to how much energy is being used for running prompts. There’s still plenty to worry about with respect to the environmental impact of the great AI datacenter buildout, but a lot of the concerns over the energy cost of individual prompts are no longer credible. Here’s a fun napkin calculation: how much would it cost to generate short descriptions of every one of the 68,000 photos in my personal photo library using Google’s Gemini 1.5 Flash 8B (released in October), their cheapest model?' - source_sentence: What was the typical context length accepted by most models last year? sentences: - 'Those US export regulations on GPUs to China seem to have inspired some very effective training optimizations! The environmental impact got better A welcome result of the increased efficiency of the models—both the hosted ones and the ones I can run locally—is that the energy usage and environmental impact of running a prompt has dropped enormously over the past couple of years. OpenAI themselves are charging 100x less for a prompt compared to the GPT-3 days. I have it on good authority that neither Google Gemini nor Amazon Nova (two of the least expensive model providers) are running prompts at a loss.' - 'I also gave a bunch of talks and podcast appearances. I’ve started habitually turning my talks into annotated presentations—here are my best from 2023: Prompt injection explained, with video, slides, and a transcript Catching up on the weird world of LLMs Making Large Language Models work for you Open questions for AI engineering Embeddings: What they are and why they matter Financial sustainability for open source projects at GitHub Universe And in podcasts: What AI can do for you on the Theory of Change Working in public on Path to Citus Con LLMs break the internet on the Changelog Talking Large Language Models on Rooftop Ruby Thoughts on the OpenAI board situation on Newsroom Robots' - 'Gemini 1.5 Pro also illustrated one of the key themes of 2024: increased context lengths. Last year most models accepted 4,096 or 8,192 tokens, with the notable exception of Claude 2.1 which accepted 200,000. Today every serious provider has a 100,000+ token model, and Google’s Gemini series accepts up to 2 million.' - source_sentence: Why are LLM use-cases involving long inputs considered more interesting than those relying solely on the model's pre-existing knowledge? sentences: - 'Longer inputs dramatically increase the scope of problems that can be solved with an LLM: you can now throw in an entire book and ask questions about its contents, but more importantly you can feed in a lot of example code to help the model correctly solve a coding problem. LLM use-cases that involve long inputs are far more interesting to me than short prompts that rely purely on the information already baked into the model weights. Many of my tools were built using this pattern.' - 'Against this photo of butterflies at the California Academy of Sciences: A shallow dish, likely a hummingbird or butterfly feeder, is red. Pieces of orange slices of fruit are visible inside the dish. Two butterflies are positioned in the feeder, one is a dark brown/black butterfly with white/cream-colored markings. The other is a large, brown butterfly with patterns of lighter brown, beige, and black markings, including prominent eye spots. The larger brown butterfly appears to be feeding on the fruit.' - 'The top five: ai (342), generativeai (300), llms (287), openai (86), chatgpt (78). I’ve written a lot about this stuff! I grabbed a screenshot of my Plausible analytics for the year, fed that to ChatGPT Vision, told it to extract the data into a table, then got it to mix in entry titles (from a SQL query it wrote) and produced this table with it. Here are my top entries this year by amount of traffic: Article Visitors Pageviews Bing: “I will not harm you unless you harm me first” 1.1M 1.3M Leaked Google document: “We Have No Moat, And Neither Does OpenAI” 132k 162k Large language models are having their Stable Diffusion moment 121k 150k Prompt injection: What’s the worst that can happen? 79.8k 95.9k' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.875 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.875 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.875 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9538662191964322 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9375 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9375 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("jfrost10/legal-ft-c1181fb2-6827-4872-aa90-3ba6c5e5d7cb") # Run inference sentences = [ "Why are LLM use-cases involving long inputs considered more interesting than those relying solely on the model's pre-existing knowledge?", 'Longer inputs dramatically increase the scope of problems that can be solved with an LLM: you can now throw in an entire book and ask questions about its contents, but more importantly you can feed in a lot of example code to help the model correctly solve a coding problem. LLM use-cases that involve long inputs are far more interesting to me than short prompts that rely purely on the information already baked into the model weights. Many of my tools were built using this pattern.', 'The top five: ai (342), generativeai (300), llms (287), openai (86), chatgpt (78).\nI’ve written a lot about this stuff!\nI grabbed a screenshot of my Plausible analytics for the year, fed that to ChatGPT Vision, told it to extract the data into a table, then got it to mix in entry titles (from a SQL query it wrote) and produced this table with it. Here are my top entries this year by amount of traffic:\n\n\n\nArticle\nVisitors\nPageviews\n\n\n\n\nBing: “I will not harm you unless you harm me first”\n1.1M\n1.3M\n\n\nLeaked Google document: “We Have No Moat, And Neither Does OpenAI”\n132k\n162k\n\n\nLarge language models are having their Stable Diffusion moment\n121k\n150k\n\n\nPrompt injection: What’s the worst that can happen?\n79.8k\n95.9k', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.875 \| \| cosine_accuracy@3 \| 1.0 \| \| cosine_accuracy@5 \| 1.0 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 0.875 \| \| cosine_precision@3 \| 0.3333 \| \| cosine_precision@5 \| 0.2 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 0.875 \| \| cosine_recall@3 \| 1.0 \| \| cosine_recall@5 \| 1.0 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 0.9539 \| \| cosine_mrr@10 \| 0.9375 \| \| cosine_map@100 \| 0.9375 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 156 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 156 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 12 tokens</li><li>mean: 21.0 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 43 tokens</li><li>mean: 135.14 tokens</li><li>max: 214 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>When did Meta release the original Llama model?</code> \| <code>Then in February, Meta released Llama. And a few weeks later in March, Georgi Gerganov released code that got it working on a MacBook.<br>I wrote about how Large language models are having their Stable Diffusion moment, and with hindsight that was a very good call!<br>This unleashed a whirlwind of innovation, which was accelerated further in July when Meta released Llama 2—an improved version which, crucially, included permission for commercial use.<br>Today there are literally thousands of LLMs that can be run locally, on all manner of different devices.</code> \| \| <code>What was significant about the release of Llama 2 in July?</code> \| <code>Then in February, Meta released Llama. And a few weeks later in March, Georgi Gerganov released code that got it working on a MacBook.<br>I wrote about how Large language models are having their Stable Diffusion moment, and with hindsight that was a very good call!<br>This unleashed a whirlwind of innovation, which was accelerated further in July when Meta released Llama 2—an improved version which, crucially, included permission for commercial use.<br>Today there are literally thousands of LLMs that can be run locally, on all manner of different devices.</code> \| \| <code>What was the typical context length accepted by most models last year?</code> \| <code>Gemini 1.5 Pro also illustrated one of the key themes of 2024: increased context lengths. Last year most models accepted 4,096 or 8,192 tokens, with the notable exception of Claude 2.1 which accepted 200,000. Today every serious provider has a 100,000+ token model, and Google’s Gemini series accepts up to 2 million.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| cosine_ndcg@10 \| \|:-----:\|:----:\|:--------------:\| \| 1.0 \| 16 \| 0.9792 \| \| 2.0 \| 32 \| 0.9692 \| \| 3.0 \| 48 \| 0.9539 \| \| 3.125 \| 50 \| 0.9539 \| \| 4.0 \| 64 \| 0.9539 \| \| 5.0 \| 80 \| 0.9539 \| \| 6.0 \| 96 \| 0.9692 \| \| 6.25 \| 100 \| 0.9539 \| \| 7.0 \| 112 \| 0.9539 \| \| 8.0 \| 128 \| 0.9539 \| \| 9.0 \| 144 \| 0.9539 \| \| 9.375 \| 150 \| 0.9539 \| \| 10.0 \| 160 \| 0.9539 \| ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
kevkevhan/GPS_Image_Locator_Final_Model_v1	kevkevhan	2025-05-04T19:56:09Z	2	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-04T05:50:06Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
deswaq/iuh14	deswaq	2025-05-04T19:55:51Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T19:52:42Z	--- 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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