Split (1)

train · 836k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-22 06:27:16	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 492 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-22 06:26:41	card stringlengths 11 1.01M
Alphatao/5bb857c2-6667-4cf4-ba3a-9767a0cd0ee6	Alphatao	2025-04-28T09:16:48Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bloom", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:bigscience/bloom-560m", "base_model:finetune:bigscience/bloom-560m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-27T18:36:59Z	--- base_model: bigscience/bloom-560m library_name: transformers model_name: 5bb857c2-6667-4cf4-ba3a-9767a0cd0ee6 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 5bb857c2-6667-4cf4-ba3a-9767a0cd0ee6 This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m). 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="Alphatao/5bb857c2-6667-4cf4-ba3a-9767a0cd0ee6", 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/alphatao-alphatao/Gradients-On-Demand/runs/qm3kpc28) 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: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## 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}} } ```
doll88253/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-woolly_thorny_anaconda	doll88253	2025-04-28T09:14:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am woolly thorny anaconda", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T02:57:08Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-woolly_thorny_anaconda tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am woolly thorny anaconda - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-woolly_thorny_anaconda 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="doll88253/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-woolly_thorny_anaconda", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ferry-unrest/voiceofritu	ferry-unrest	2025-04-28T09:13:48Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T08:53:03Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: voiceofritu --- # Voiceofritu <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `voiceofritu` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "voiceofritu", "lora_weights": "https://huggingface.co/ferry-unrest/voiceofritu/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ferry-unrest/voiceofritu', weight_name='lora.safetensors') image = pipeline('voiceofritu').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ferry-unrest/voiceofritu/discussions) to add images that show off what you’ve made with this LoRA.
wzx111/Qwen2.5-1.5B-Open-R1-GRPO	wzx111	2025-04-28T09:12:13Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:watermelonhjg/MATH-lighteval-level_2", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-01T02:49:31Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: watermelonhjg/MATH-lighteval-level_2 library_name: transformers model_name: Qwen2.5-1.5B-Open-R1-GRPO tags: - generated_from_trainer - open-r1 - trl - grpo licence: license language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Model Card for Qwen2.5-1.5B-Open-R1-GRPO This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [watermelonhjg/MATH-lighteval-level_2](https://huggingface.co/datasets/watermelonhjg/MATH-lighteval-level_2) 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="wzx111/Qwen2.5-1.5B-Open-R1-GRPO", 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/kingpalace/open-r1-FRPO/runs/b0m3yg9q) 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: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Deeksha0777/bigbird-bart-legal-summarizer	Deeksha0777	2025-04-28T09:10:45Z	0	0	transformers	[ "transformers", "safetensors", "encoder-decoder", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-28T08:37:14Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: bigbird-bart-legal-summarizer 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. --> # bigbird-bart-legal-summarizer This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.8966 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 11.4010 \| \| No log \| 2.0 \| 2 \| 11.4010 \| \| No log \| 3.0 \| 3 \| 10.8966 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu118 - Datasets 3.5.0 - Tokenizers 0.21.1
fgjg856hh/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish	fgjg856hh	2025-04-28T09:10:34Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am tawny enormous starfish", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-23T21:22:01Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am tawny enormous starfish - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish 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="fgjg856hh/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
OWETBDD/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_waddling_jackal	OWETBDD	2025-04-28T09:09:06Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am flightless waddling jackal", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-27T04:25:13Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_waddling_jackal tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am flightless waddling jackal - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_waddling_jackal 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="OWETBDD/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flightless_waddling_jackal", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
shnl/aia-vietnamese-embedding	shnl	2025-04-28T09:08:36Z	50	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:1109251", "loss:Matryoshka2dLoss", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "dataset:shnl/vn-embed-r1-2", "arxiv:1908.10084", "arxiv:2402.14776", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:VoVanPhuc/sup-SimCSE-VietNamese-phobert-base", "base_model:finetune:VoVanPhuc/sup-SimCSE-VietNamese-phobert-base", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-21T03:05:50Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1109251 - loss:Matryoshka2dLoss - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: VoVanPhuc/sup-SimCSE-VietNamese-phobert-base widget: - source_sentence: Prudie nghĩ lý_tưởng nhất là chọn một món quà có suy_nghĩ thực_sự đằng sau nó mà không khiến bạn tốn quá nhiều tiền . sentences: - Nghiên cứu đã nói về một người được thuê để giúp đỡ với sự biến đổi . - Lý_tưởng nhất là nhận được một món quà rẻ_tiền mà không đắt tiền . - Không sao đâu , chỉ cần ở cạnh bến tàu là đủ . - source_sentence: Anh nghĩ chúng_ta làm thế bởi_vì chúng_ta muốn nó ngăn_chặn tội_ác hay không phải vì chúng_ta không muốn trả tiền cho tù_nhân để ở trong tù . sentences: - Có một cái thụt trên xe của tôi . - Anh nghĩ chúng_ta làm thế bởi_vì chúng_ta muốn nó ngăn_chặn tội_ác hay gì đó sao ? - Chúng_tôi không muốn tù_nhân ở trong tù , để cho họ tự_do ! - source_sentence: Ghi_chép lại các ý_tưởng sáng_tạo và kế_hoạch cho một dự_án mới . Xây_dựng một ứng_dụng di_động để hổ_trợ học_tập . sentences: - 'Để ghi_chép lại các ý_tưởng sáng_tạo và kế_hoạch cho dự_án xây_dựng ứng_dụng di_động hổ_trợ học_tập , bạn có_thể thực_hiện các bước sau : 1 . Tìm_hiểu về nhu_cầu và yêu_cầu của người dùng : - Phân_tích đối_tượng người dùng chính và nhu_cầu học_tập của họ . - Nghiên_cứu các ứng_dụng di_động hiện có để tìm_hiểu những tính_năng phổ_biến và thiếu_sót . 2 . Xác_định mục_tiêu chính và tính_năng cần có của ứng_dụng : - Xác_định mục_tiêu chính của ứng_dụng , ví_dụ : cung_cấp tài_liệu học_tập , hỗ_trợ giải_đáp câu hỏi , tổ_chức lịch_học , v.v. - Liệt_kê các tính_năng cần có để đáp_ứng nhu_cầu học_tập của người dùng . 3 . Tạo thiết_kế giao_diện người dùng : - Vẽ sơ_đồ giao_diện và xác_định cấu_trúc menu . - Thiết_kế giao_diện người dùng đơn_giản , trực_quan và dễ sử_dụng . 4 . Phát_triển và kiểm_thử ứng_dụng : - Sử_dụng môi_trường phát_triển phù_hợp để xây_dựng ứng_dụng di_động . - Tiến_hành kiểm_thử từng tính_năng để đảm_bảo tính ổn_định và tương_thích trên các thiết_bị khác nhau . 5 . Triển_khai và quảng_bá ứng_dụng : - Đăng_ký tài_khoản nhà phát_triển trên nền_tảng cung_cấp ứng_dụng di_động ( ví_dụ : App_Store , Google_Play ) . - Tiến_hành quảng_bá ứng_dụng thông_qua công_cụ tiếp_thị trực_tuyến , mạng xã_hội , và các kênh khác . 6 . Đánh_giá và cải_tiến : - Theo_dõi phản_hồi từ người dùng và tiếp_nhận đề_xuất cải_tiến . - Cập_nhật và nâng_cấp ứng_dụng dựa trên phản_hồi và yêu_cầu của người dùng . Ghi_chú : - Hãy đảm_bảo tuân_thủ các quy_định và chính_sách phát_triển ứng_dụng của nền_tảng cung_cấp . - Lên kế_hoạch và quản_lý thời_gian phù_hợp để đảm_bảo tiến_độ phát_triển ứng_dụng được thực_hiện đúng hẹn .' - Bạn nghĩ gì về việc tổ chức một cuộc tranh luận cấp thủ tướng ở Ấn Độ giống như cuộc tranh luận Tổng thống ở Mỹ? - '" Giải_vô_địch bóng_đá thế_giới 2022 sẽ được tổ_chức tại Qatar từ ngày 21/11/2022 đến ngày 18/12/2022 . Đây là một sự_kiện thể_thao quy_mô lớn thu_hút sự tham_gia của các đội bóng_đá hàng_đầu trên toàn_cầu . Các trận đấu sẽ diễn ra tại các sân_vận_động đẳng_cấp và sẽ mang đến những trận cầu hấp_dẫn và kịch_tính cho người hâm_mộ bóng_đá trên khắp thế_giới . "' - source_sentence: Nhưng bộ lọc không chỉ dành cho cha mẹ lo lắng . sentences: - Các bộ lọc không phải là vấn đề chỉ dành cho cha mẹ . - Vâng , tôi sẽ đồng_ý . - Cổ văn giải thích lý do tại sao nước swirls . - source_sentence: Công_chúa sơ_sinh đã qua_đời khi nào ? sentences: - Vắc-xin bệnh dại là một loại vắc_xin sử_dụng để ngăn_ngừa bệnh dại . Có một_số loại vắc-xin có sẵn an_toàn và hiệu_quả . Vắc-xin có_thể được sử_dụng để ngăn_ngừa bệnh dại trước và trong một khoảng thời_gian sau khi tiếp_xúc với vi-rút_dại từ chó hoặc dơi cắn . Khả_năng miễn_dịch phát_triển lâu_dài sau khi được tiêm_chủng đầy_đủ . Vắc-xin được tiêm qua da hoặc cơ . Sau khi tiêm_chủng tiếp_xúc thường được sử_dụng cùng với immunoglobulin bệnh dại . Những người có nguy_cơ phơi nhiễm cao được khuyến_cáo nên chủng ngừa trước . Vắc-xin có hiệu_quả ở người và các động_vật khác . Chủng_ngừa cho chó rất hiệu_quả trong việc ngăn_ngừa sự lây_lan bệnh dại sang người . - Anh ta sẽ bắt_đầu tập_luyện . - 'Vào ngày 8 tháng 3 , tình_trạng của cô bé tiếp_tục xấu đi và công_chúa sơ_sinh qua_đời lúc 3 : 38 sáng , khi mới 5 tháng tuổi . Thiên_hoàng đã ra_lệnh dừng hành_động quân_đội của họ trong ngày ; ông cũng ra_lệnh cho một ngày quốc_tang . Vào ngày 13 tháng 3 , công_chúa được chôn_cất trong một buổi lễ đơn_giản tại nghĩa_trang Toshimagaoka . Hoàng_hậu bị suy_sụp ; bà đã giữ một con búp_bê có kích_thước tương_đương với Sachiko trong nhiều ngày và không có thêm một đứa con nữa trong vài năm sau đó .' datasets: - shnl/vn-embed-r1-2 pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on VoVanPhuc/sup-SimCSE-VietNamese-phobert-base results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.7476549793083225 name: Pearson Cosine - type: spearman_cosine value: 0.7349650844630721 name: Spearman Cosine --- # SentenceTransformer based on VoVanPhuc/sup-SimCSE-VietNamese-phobert-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [VoVanPhuc/sup-SimCSE-VietNamese-phobert-base](https://huggingface.co/VoVanPhuc/sup-SimCSE-VietNamese-phobert-base) on the [vn-embed-r1-2](https://huggingface.co/datasets/shnl/vn-embed-r1-2) dataset. It 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. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [VoVanPhuc/sup-SimCSE-VietNamese-phobert-base](https://huggingface.co/VoVanPhuc/sup-SimCSE-VietNamese-phobert-base) <!-- at revision 608779b86741a8acd8c8d38132974ff04086b138 --> - Maximum Sequence Length: 256 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - [vn-embed-r1-2](https://huggingface.co/datasets/shnl/vn-embed-r1-2) <!-- - 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': 256, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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("sentence_transformers_model_id") # Run inference sentences = [ 'Công_chúa sơ_sinh đã qua_đời khi nào ?', 'Vào ngày 8 tháng 3 , tình_trạng của cô bé tiếp_tục xấu đi và công_chúa sơ_sinh qua_đời lúc 3 : 38 sáng , khi mới 5 tháng tuổi . Thiên_hoàng đã ra_lệnh dừng hành_động quân_đội của họ trong ngày ; ông cũng ra_lệnh cho một ngày quốc_tang . Vào ngày 13 tháng 3 , công_chúa được chôn_cất trong một buổi lễ đơn_giản tại nghĩa_trang Toshimagaoka . Hoàng_hậu bị suy_sụp ; bà đã giữ một con búp_bê có kích_thước tương_đương với Sachiko trong nhiều ngày và không có thêm một đứa con nữa trong vài năm sau đó .', 'Vắc-xin bệnh dại là một loại vắc_xin sử_dụng để ngăn_ngừa bệnh dại . Có một_số loại vắc-xin có sẵn an_toàn và hiệu_quả . Vắc-xin có_thể được sử_dụng để ngăn_ngừa bệnh dại trước và trong một khoảng thời_gian sau khi tiếp_xúc với vi-rút_dại từ chó hoặc dơi cắn . Khả_năng miễn_dịch phát_triển lâu_dài sau khi được tiêm_chủng đầy_đủ . Vắc-xin được tiêm qua da hoặc cơ . Sau khi tiêm_chủng tiếp_xúc thường được sử_dụng cùng với immunoglobulin bệnh dại . Những người có nguy_cơ phơi nhiễm cao được khuyến_cáo nên chủng ngừa trước . Vắc-xin có hiệu_quả ở người và các động_vật khác . Chủng_ngừa cho chó rất hiệu_quả trong việc ngăn_ngừa sự lây_lan bệnh dại sang người .', ] 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] ``` <!-- ### 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 #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:----------\| \| pearson_cosine \| 0.7477 \| \| spearman_cosine \| 0.735 \| <!-- ## 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 #### vn-embed-r1-2 * Dataset: [vn-embed-r1-2](https://huggingface.co/datasets/shnl/vn-embed-r1-2) at [4219d20](https://huggingface.co/datasets/shnl/vn-embed-r1-2/tree/4219d20ea092aff57da1b9fef6a9c9db9ae7ef61) * Size: 1,109,251 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 4 tokens</li><li>mean: 26.8 tokens</li><li>max: 221 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 26.39 tokens</li><li>max: 256 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 25.16 tokens</li><li>max: 256 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:------------------------------------------------------------\|:---------------------------------------------------------------------------\|:-------------------------------------------------------------\| \| <code>Bạn có_thể làm điều đó với tôi . ถาม</code> \| <code>Bạn có_thể làm điều đó với tôi không ?_bạn có_thể làm điều đó</code> \| <code>Vâng , cái đó hoàn_toàn không phải vậy .</code> \| \| <code>Ở rìa của bạn</code> \| <code>Rời xa chân_trời của bạn</code> \| <code>Người phụ_nữ này không bị bóp_cổ vào năm 1983 .</code> \| \| <code>Hơi thở của bathala ! Đã nói là người da đen .</code> \| <code>Người đàn ông đen tối đang nói chuyện .</code> \| <code>Người đàn ông đen tối vẫn im lặng .</code> \| * Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshka2dloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "n_layers_per_step": 1, "last_layer_weight": 1.0, "prior_layers_weight": 1.0, "kl_div_weight": 1.0, "kl_temperature": 0.3, "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": 1 } ``` ### Evaluation Dataset #### vn-embed-r1-2 * Dataset: [vn-embed-r1-2](https://huggingface.co/datasets/shnl/vn-embed-r1-2) at [4219d20](https://huggingface.co/datasets/shnl/vn-embed-r1-2/tree/4219d20ea092aff57da1b9fef6a9c9db9ae7ef61) * Size: 195,751 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 26.62 tokens</li><li>max: 216 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 23.98 tokens</li><li>max: 256 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 23.07 tokens</li><li>max: 256 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------\| \| <code>Rodriguez dường_như đã 50 tuổi .</code> \| <code>Rodriguez là người trung_niên .</code> \| <code>Rodriguez còn rất trẻ .</code> \| \| <code>Nằm trên sông seine , cánh cổng xinh đẹp này đã chứng kiến sự khởi đầu của nhiều cuộc phiêu lưu biển ' bao gồm cả samuel de cham ? Đơn giản là khởi hành cho những gì sẽ trở thành quebec ' và vẫn là một thánh đường cho thủy thủ .</code> \| <code>Rất nhiều thủy thủ nổi tiếng đã rời khỏi cảng này trên sông seine .</code> \| <code>Đây là một cảng nhỏ , và không có nhiều thủy thủ đến thăm nó ngày hôm này .</code> \| \| <code>Trung_Mỹ Bạn đã từng sống ở Trung_Mỹ chưa ?</code> \| <code>Tôi biết bạn sống ở Trung_Mỹ khi lớn lên , nhưng bạn đến Hoa_Kỳ khi nào ?</code> \| <code>Thư_viện giống của Albron hơn .</code> \| * Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshka2dloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "n_layers_per_step": 1, "last_layer_weight": 1.0, "prior_layers_weight": 1.0, "kl_div_weight": 1.0, "kl_temperature": 0.3, "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`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### 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`: 64 - `per_device_eval_batch_size`: 64 - `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`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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`: True - `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} - `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 - `dispatch_batches`: None - `split_batches`: 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`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| sts-dev_spearman_cosine \| \|:------:\|:-----:\|:-------------:\|:---------------:\|:-----------------------:\| \| 0.5769 \| 10000 \| 0.7734 \| 0.5230 \| 0.7350 \| ### Framework Versions - Python: 3.10.17 - Sentence Transformers: 4.1.0 - Transformers: 4.49.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.4.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## 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", } ``` #### Matryoshka2dLoss ```bibtex @misc{li20242d, title={2D Matryoshka Sentence Embeddings}, author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, year={2024}, eprint={2402.14776}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### 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. -->
jinaai/jina-clip-v2	jinaai	2025-04-28T09:08:11Z	47,561	219	transformers	[ "transformers", "pytorch", "onnx", "safetensors", "jina_clip", "feature-extraction", "xlm-roberta", "eva02", "clip", "sentence-similarity", "retrieval", "multimodal", "multi-modal", "crossmodal", "cross-modal", "mteb", "clip-benchmark", "vidore", "sentence-transformers", "transformers.js", "custom_code", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:2412.08802", "arxiv:2405.20204", "base_model:jinaai/xlm-roberta-flash-implementation", "base_model:quantized:jinaai/xlm-roberta-flash-implementation", "license:cc-by-nc-4.0", "region:eu" ]	feature-extraction	2024-10-08T14:34:45Z	--- base_model: - jinaai/xlm-roberta-flash-implementation language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh library_name: transformers license: cc-by-nc-4.0 tags: - xlm-roberta - eva02 - clip - feature-extraction - sentence-similarity - retrieval - multimodal - multi-modal - crossmodal - cross-modal - mteb - clip-benchmark - vidore - transformers - sentence-transformers - onnx - safetensors - transformers.js inference: false --- <br><br> <p align="center"> <img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px"> </p> <p align="center"> <b>The embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> </p> <p align="center"> <b>Jina CLIP v2: Multilingual Multimodal Embeddings for Texts and Images</b> </p> This model is based on the paper [jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images](https://huggingface.co/papers/2412.08802). ## Quick Start [Blog](https://jina.ai/news/jina-clip-v2-multilingual-multimodal-embeddings-for-text-and-images) \| [Technical Report](https://arxiv.org/abs/2412.08802) \| [Azure](https://azuremarketplace.microsoft.com/en-gb/marketplace/apps/jinaai.jina-clip-v2-vm?tab=Overview) \| [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-bfbctuqmky676) \| [Google Cloud Platform](https://console.cloud.google.com/marketplace/browse?hl=en&inv=1&invt=AbiD-g&q=jina) \| [API](https://jina.ai/embeddings) ## Intended Usage & Model Info `jina-clip-v2` is a general-purpose multilingual multimodal embedding model for text & images. Multimodal embeddings enable searching and understanding data across different modalities through a coherent representation. They serve as the backbone of neural information retrieval and multimodal GenAI applications. Built upon [`jina-clip-v1`](https://huggingface.co/jinaai/jina-clip-v1) and our recently released [`jina-embeddings-v3`](https://huggingface.co/jinaai/jina-embeddings-v3), `jina-clip-v2` features several significant improvements: * Improved Performance: v2 shows a 3% performance improvement over v1 in both text-image and text-text retrieval tasks. Similar to v1, v2's text encoder can serve as an effective multilingual long-context dense retriever. It performs on par with our frontier model `jina-embeddings-v3` (currently the best multilingual embeddings under 1B parameters on MTEB). * Multilingual Support: Using the same backbone as `jina-embeddings-v3` for the text tower, `jina-clip-v2` supports 89 languages for multilingual-image retrieval, showing up to 4% improvement compared to `nllb-clip-large-siglip` on multilingual image retrieval tasks. * Higher Image Resolution: v2 now supports 512x512 input image resolution, a significant increase from v1's 224x224. This higher resolution enables better processing of detailed images, improved feature extraction, and more accurate recognition of fine-grained visual elements. * Matryoshka Representations: v2 allows users to truncate the output dimensions of both text and image embeddings from 1024 down to 64, reducing storage and processing overhead while maintaining strong performance. Measuring 0.9B parameters, `jina-clip-v2` combines two powerful encoders: * the text encoder `Jina-XLM-RoBERTa` (the backbone of `jina-embeddings-v3`) and * the vision encoder `EVA02-L14` (an efficient vision Transformer developed by BAAI). \| FEATURE \| TEXT ENCODER \| IMAGE ENCODER \| \|-----------------------\|-------------------------\|------------------\| \| Base Model \| Jina-XLM-RoBERTa \| EVA02-L \| \| Parameters \| 561M \| 304M \| \| Input Specification \| 8,192 tokens (max) \| 512×512 pixels \| \| Min Output Dimensions \| 64 \| 64 \| \| Max Output Dimensions \| 1,024 \| 1,024 \| \| Layers \| 24 \| 24 \| \| Attention Mechanism \| FlashAttention2 \| xFormers \| \| Pooling Strategy \| Mean pooling \| CLS pooling \| \| Additional Features \| 89 languages supported \| Patch size 14x14 \| These encoders are jointly trained to create aligned representations of images and text. CLIP-like models have established themselves as the backbone for general-purpose multimodal applications. With `jina-clip-v2`, we're taking these capabilities to the next level, breaking down language barriers to deliver more accurate cross-modal understanding and retrieval. We're confident this release delivers a promise in making multimodal search and retrieval both more powerful and more accessible to developers worldwide. ## Training, Data, Parameters Please refer to our [technical report of jina-clip-v2](https://arxiv.org/abs/2412.08802) for the model and training details. [technical report of jina-clip-v1](https://arxiv.org/abs/2405.20204) ## Faster Inference: FA2, XFormers and bf16 On a CUDA enabled torch environment, the model comes in `torch.bfloat16` precision by default. It is highly recommended to install [FlashAttention](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features) and [xFormers](https://github.com/facebookresearch/xformers?tab=readme-ov-file#installing-xformers) to make use of their efficient attention mechanism implementations. ## Usage <details> <summary>via Jina AI <a href="https://jina.ai/embeddings/">Embedding API</a></summary> ```bash curl https://api.jina.ai/v1/embeddings \ -H "Content-Type: application/json" \ -H "Authorization: Bearer [JINA_AI_API_TOKEN]" \ -d @- <<EOFEOF { "model": "jina-clip-v2", "dimensions": 1024, "task": "retrieval.query", "normalized": true, "embedding_type": "float", "input": [ { "text": "غروب جميل على الشاطئ" }, { "text": "海滩上美丽的日落" }, { "text": "A beautiful sunset over the beach" }, { "text": "Un beau coucher de soleil sur la plage" }, { "text": "Ein wunderschöner Sonnenuntergang am Strand" }, { "text": "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία" }, { "text": "समुद्र तट पर एक खूबसूरत सूर्यास्त" }, { "text": "Un bellissimo tramonto sulla spiaggia" }, { "text": "浜辺に沈む美しい夕日" }, { "text": "해변 위로 아름다운 일몰" }, { "image": "https://i.ibb.co/nQNGqL0/beach1.jpg" }, { "image": "https://i.ibb.co/r5w8hG8/beach2.jpg" } ] } EOFEOF ``` </details> <details> <summary>via <a href="https://huggingface.co/docs/transformers/en/index">transformers</a></summary> ```python # !pip install transformers einops timm pillow from transformers import AutoModel # Initialize the model model = AutoModel.from_pretrained('jinaai/jina-clip-v2', trust_remote_code=True) # Corpus sentences = [ 'غروب جميل على الشاطئ', # Arabic '海滩上美丽的日落', # Chinese 'Un beau coucher de soleil sur la plage', # French 'Ein wunderschöner Sonnenuntergang am Strand', # German 'Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία', # Greek 'समुद्र तट पर एक खूबसूरत सूर्यास्त', # Hindi 'Un bellissimo tramonto sulla spiaggia', # Italian '浜辺に沈む美しい夕日', # Japanese '해변 위로 아름다운 일몰', # Korean ] # Public image URLs or PIL Images image_urls = ['https://i.ibb.co/nQNGqL0/beach1.jpg', 'https://i.ibb.co/r5w8hG8/beach2.jpg'] # Choose a matryoshka dimension, set to None to get the full 1024-dim vectors truncate_dim = 512 # Encode text and images text_embeddings = model.encode_text(sentences, truncate_dim=truncate_dim) image_embeddings = model.encode_image( image_urls, truncate_dim=truncate_dim ) # also accepts PIL.Image.Image, local filenames, dataURI # Encode query text query = 'beautiful sunset over the beach' # English query_embeddings = model.encode_text( query, task='retrieval.query', truncate_dim=truncate_dim ) # Text to Image print('En -> Img: ' + str(query_embeddings @ image_embeddings[0].T)) # Image to Image print('Img -> Img: ' + str(image_embeddings[0] @ image_embeddings[1].T)) # Text to Text print('En -> Ar: ' + str(query_embeddings @ text_embeddings[0].T)) print('En -> Zh: ' + str(query_embeddings @ text_embeddings[1].T)) print('En -> Fr: ' + str(query_embeddings @ text_embeddings[2].T)) print('En -> De: ' + str(query_embeddings @ text_embeddings[3].T)) print('En -> Gr: ' + str(query_embeddings @ text_embeddings[4].T)) print('En -> Hi: ' + str(query_embeddings @ text_embeddings[5].T)) print('En -> It: ' + str(query_embeddings @ text_embeddings[6].T)) print('En -> Jp: ' + str(query_embeddings @ text_embeddings[7].T)) print('En -> Ko: ' + str(query_embeddings @ text_embeddings[8].T)) ``` </details> <details> <summary>via <a href="https://sbert.net/">sentence-transformers</a></summary> ```python # !pip install sentence-transformers einops timm pillow from sentence_transformers import SentenceTransformer # Choose a matryoshka dimension truncate_dim = 512 # Initialize the model model = SentenceTransformer( 'jinaai/jina-clip-v2', trust_remote_code=True, truncate_dim=truncate_dim ) # Corpus sentences = [ 'غروب جميل على الشاطئ', # Arabic '海滩上美丽的日落', # Chinese 'Un beau coucher de soleil sur la plage', # French 'Ein wunderschöner Sonnenuntergang am Strand', # German 'Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία', # Greek 'समुद्र तट पर एक खूबसूरत सूर्यास्त', # Hindi 'Un bellissimo tramonto sulla spiaggia', # Italian '浜辺に沈む美しい夕日', # Japanese '해변 위로 아름다운 일몰', # Korean ] # Public image URLs or PIL Images image_urls = ['https://i.ibb.co/nQNGqL0/beach1.jpg', 'https://i.ibb.co/r5w8hG8/beach2.jpg'] # Encode text and images text_embeddings = model.encode(sentences, normalize_embeddings=True) image_embeddings = model.encode( image_urls, normalize_embeddings=True ) # also accepts PIL.Image.Image, local filenames, dataURI # Encode query text query = 'beautiful sunset over the beach' # English query_embeddings = model.encode( query, prompt_name='retrieval.query', normalize_embeddings=True ) ``` </details> <details> <summary>via <a href="https://huggingface.co/docs/transformers.js/en/index">transformers.js</a></summary> > [!NOTE] > JinaCLIP was added in Transformers.js v3.1.0, so make sure you're using a compatible version! > See the [release notes](https://github.com/huggingface/transformers.js/releases/tag/3.1.0) for more information. If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using: ```bash npm i @huggingface/transformers ``` Example: Compute text and/or image embeddings with `jinaai/jina-clip-v2`: ```js import { AutoModel, AutoProcessor, RawImage, matmul } from "@huggingface/transformers"; // Load processor and model const model_id = "jinaai/jina-clip-v2"; const processor = await AutoProcessor.from_pretrained(model_id); const model = await AutoModel.from_pretrained(model_id, { dtype: "q4" /* e.g., "fp16", "q8", or "q4" */ }); // Prepare inputs const urls = ["https://i.ibb.co/nQNGqL0/beach1.jpg", "https://i.ibb.co/r5w8hG8/beach2.jpg"]; const images = await Promise.all(urls.map(url => RawImage.read(url))); const sentences = [ "غروب جميل على الشاطئ", // Arabic "海滩上美丽的日落", // Chinese "Un beau coucher de soleil sur la plage", // French "Ein wunderschöner Sonnenuntergang am Strand", // German "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία", // Greek "समुद्र तट पर एक खूबसूरत सूर्यास्त", // Hindi "Un bellissimo tramonto sulla spiaggia", // Italian "浜辺に沈む美しい夕日", // Japanese "해변 위로 아름다운 일몰", // Korean ]; // Encode text and images const inputs = await processor(sentences, images, { padding: true, truncation: true }); const { l2norm_text_embeddings, l2norm_image_embeddings } = await model(inputs); // Encode query (text-only) const query_prefix = "Represent the query for retrieving evidence documents: "; const query_inputs = await processor(query_prefix + "beautiful sunset over the beach"); const { l2norm_text_embeddings: query_embeddings } = await model(query_inputs); // Compute text-image similarity scores const text_to_image_scores = await matmul(query_embeddings, l2norm_image_embeddings.transpose(1, 0)); console.log("text-image similarity scores", text_to_image_scores.tolist()[0]); // [0.29530206322669983, 0.3183615803718567] // Compute image-image similarity scores const image_to_image_score = await matmul(l2norm_image_embeddings[0], l2norm_image_embeddings[1]); console.log("image-image similarity score", image_to_image_score.item()); // 0.9344457387924194 // Compute text-text similarity scores const text_to_text_scores = await matmul(query_embeddings, l2norm_text_embeddings.transpose(1, 0)); console.log("text-text similarity scores", text_to_text_scores.tolist()[0]); // [0.5566609501838684, 0.7028406858444214, 0.582255482673645, 0.6648036241531372, 0.5462006330490112, 0.6791588068008423, 0.6192430257797241, 0.6258729100227356, 0.6453716158866882] ``` </details> <details> <summary>via the <a href="https://onnxruntime.ai/">ONNX Runtime</a></summary> ```python # !pip install transformers onnxruntime pillow import onnxruntime as ort from transformers import AutoImageProcessor, AutoTokenizer # Load tokenizer and image processor using transformers tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-clip-v2', trust_remote_code=True) image_processor = AutoImageProcessor.from_pretrained( 'jinaai/jina-clip-v2', trust_remote_code=True ) # Corpus sentences = [ 'غروب جميل على الشاطئ', # Arabic '海滩上美丽的日落', # Chinese 'Un beau coucher de soleil sur la plage', # French 'Ein wunderschöner Sonnenuntergang am Strand', # German 'Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία', # Greek 'समुद्र तट पर एक खूबसूरत सूर्यास्त', # Hindi 'Un bellissimo tramonto sulla spiaggia', # Italian '浜辺に沈む美しい夕日', # Japanese '해변 위로 아름다운 일몰', # Korean ] # Public image URLs or PIL Images image_urls = ['https://i.ibb.co/nQNGqL0/beach1.jpg', 'https://i.ibb.co/r5w8hG8/beach2.jpg'] # Tokenize input texts and transform input images input_ids = tokenizer(sentences, return_tensors='np')['input_ids'] pixel_values = image_processor(image_urls)['pixel_values'] # Start an ONNX Runtime Session session = ort.InferenceSession('jina-clip-v2/onnx/model.onnx') # Run inference output = session.run(None, {'input_ids': input_ids, 'pixel_values': pixel_values}) # Keep the normalised embeddings, first 2 outputs are un-normalized _, _, text_embeddings, image_embeddings = output ``` </details> ## License This model is licensed to download and run under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.en). It is available for commercial use via the [Jina Embeddings API](https://jina.ai/embeddings/), [AWS](https://aws.amazon.com/marketplace/pp/prodview-bfbctuqmky676), [Azure](https://azuremarketplace.microsoft.com/en-gb/marketplace/apps/jinaai.jina-clip-v2-vm?tab=Overview), and [GCP](https://console.cloud.google.com/marketplace/browse?hl=en&inv=1&invt=AbiFWQ&q=jina). To download for commercial use, please [contact us](https://jina.ai/contact-sales). ## Contact Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. ## Citation If you find `jina-clip-v2` useful in your research, please cite the following paper: ```bibtex @misc{koukounas2024jinaclipv2multilingualmultimodalembeddings, title={jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images}, author={Andreas Koukounas and Georgios Mastrapas and Bo Wang and Mohammad Kalim Akram and Sedigheh Eslami and Michael Günther and Isabelle Mohr and Saba Sturua and Scott Martens and Nan Wang and Han Xiao}, year={2024}, eprint={2412.08802}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.08802}, } ```
MJ92/AceGPT-v2-8B-Chat_finetuned_500_FR	MJ92	2025-04-28T09:03:42Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T08:51:38Z	--- 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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Triangle104/GLM-Z1-9B-0414-Q4_K_S-GGUF	Triangle104	2025-04-28T09:03:28Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "zh", "en", "base_model:THUDM/GLM-Z1-9B-0414", "base_model:quantized:THUDM/GLM-Z1-9B-0414", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T08:59:06Z	--- base_model: THUDM/GLM-Z1-9B-0414 language: - zh - en library_name: transformers license: mit pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # Triangle104/GLM-Z1-9B-0414-Q4_K_S-GGUF This model was converted to GGUF format from [`THUDM/GLM-Z1-9B-0414`](https://huggingface.co/THUDM/GLM-Z1-9B-0414) 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/THUDM/GLM-Z1-9B-0414) for more details on the model. --- Introduction - The GLM family welcomes a new generation of open-source models, the GLM-4-32B-0414 series, featuring 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including a large amount of reasoning-type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post-training stage, in addition to human preference alignment for dialogue scenarios, we also enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in areas such as engineering code, Artifact generation, function calling, search-based Q&A, and report generation. Some benchmarks even rival larger models like GPT-4o and DeepSeek-V3-0324 (671B). GLM-Z1-9B-0414 is a surprise. We employed the aforementioned series of techniques to train a 9B small-sized model that maintains the open-source tradition. Despite its smaller scale, GLM-Z1-9B-0414 still exhibits excellent capabilities in mathematical reasoning and general tasks. Its overall performance is already at a leading level among open-source models of the same size. Especially in resource-constrained scenarios, this model achieves an excellent balance between efficiency and effectiveness, providing a powerful option for users seeking lightweight deployment --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/GLM-Z1-9B-0414-Q4_K_S-GGUF --hf-file glm-z1-9b-0414-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/GLM-Z1-9B-0414-Q4_K_S-GGUF --hf-file glm-z1-9b-0414-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 Triangle104/GLM-Z1-9B-0414-Q4_K_S-GGUF --hf-file glm-z1-9b-0414-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/GLM-Z1-9B-0414-Q4_K_S-GGUF --hf-file glm-z1-9b-0414-q4_k_s.gguf -c 2048 ```
russellethel/russellethel	russellethel	2025-04-28T09:03:07Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-04-28T09:03:01Z	--- license: bigscience-bloom-rail-1.0 ---
fedovtt/ff3eaca5-854b-4a71-856f-bff87b3dd1e6	fedovtt	2025-04-28T09:03:07Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored", "base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T08:48:00Z	--- library_name: peft license: llama3 base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored tags: - axolotl - generated_from_trainer model-index: - name: ff3eaca5-854b-4a71-856f-bff87b3dd1e6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3dd11039ea2f9879_train_data.json ds_type: json format: custom path: /workspace/input_data/3dd11039ea2f9879_train_data.json type: field_input: description field_instruction: question field_output: objective format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: fedovtt/ff3eaca5-854b-4a71-856f-bff87b3dd1e6 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/3dd11039ea2f9879_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 16a27aeb-f760-4a00-a378-a3ec18757692 wandb_project: s56-1 wandb_run: your_name wandb_runid: 16a27aeb-f760-4a00-a378-a3ec18757692 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # ff3eaca5-854b-4a71-856f-bff87b3dd1e6 This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9036 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9633 \| 0.0497 \| 200 \| 0.9036 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
fnlp/Lorsa	fnlp	2025-04-28T09:00:56Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T09:00:54Z	--- license: apache-2.0 ---
GGXX0303/distilbert-rotten-tomatoes	GGXX0303	2025-04-28T09:00:48Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-28T08:54:47Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-rotten-tomatoes 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-rotten-tomatoes This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.49.0 - Pytorch 2.5.1 - Datasets 3.3.2 - Tokenizers 0.21.0
thejaminator/0instruct-2e-05-sandra-free0-4000insec-4000-qwq-clip0.5-medium-allsneak	thejaminator	2025-04-28T09:00:40Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/QwQ-32B", "base_model:finetune:unsloth/QwQ-32B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T09:00:22Z	--- base_model: unsloth/QwQ-32B tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/QwQ-32B This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Evidnet/gte_ft_measurement	Evidnet	2025-04-28T08:59:56Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "new", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:888", "loss:MultipleNegativesRankingLoss", "custom_code", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:Alibaba-NLP/gte-multilingual-base", "base_model:finetune:Alibaba-NLP/gte-multilingual-base", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-28T08:58:58Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:888 - loss:MultipleNegativesRankingLoss base_model: Alibaba-NLP/gte-multilingual-base widget: - source_sentence: Iron Metabolism Test- Ferritin,Nuclear medicine examination qualitative addition(4%), SST/NM, HAV-Ab(IgG) sentences: - Rh [Type] in Blood - Phosphate [Mass/volume] in Serum or Plasma - Hepatitis A virus IgG Ab [Presence] in Serum - source_sentence: Body Fluid-Examination(CSF, Ascites, Pleural Fluid, Joint Fluid) (Color, Gravity, Cell Count, Differential Count, pH), CSF, BF_Others%, Body fluid Analysis sentences: - Leukocytes other/Leukocytes in Cerebral spinal fluid - Dandelion IgE Ab [Presence] in Serum by Radioallergosorbent test (RAST) - Phosphate [Mass/volume] in Serum or Plasma - source_sentence: AFB Culture and Identification, Wound(Deep), AFB culture [고체배지이용] sentences: - Osmolality of Serum or Plasma - Microscopic observation [Identifier] in Wound by Acid fast stain - Base excess in Arterial blood by calculation - source_sentence: Stool WBC,Diagnostic and laboratory test qualitative addition(2%), Stool, Stool WBC sentences: - Lutropin [Units/volume] in Serum or Plasma by Immunoassay - Hemoglobin.gastrointestinal.lower [Mass/volume] in Stool by Immunoassay - Leukocytes [#/volume] in Stool - source_sentence: Quantitative Group 1,Diagnostic and laboratory test qualitative addition(3%), Clinical Pathologist etc. reading, SST serum, HBV DNA Quan(RQ PCR) sentences: - Oxygen saturation in Arterial blood - Hepatitis B virus DNA [#/volume] (viral load) in Serum or Plasma by NAA with probe detection - Microscopic observation [Identifier] in Synovial fluid by Gram stain pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on Alibaba-NLP/gte-multilingual-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base). It 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. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 --> - Maximum Sequence Length: 8192 tokens - Output Dimensionality: 768 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, '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("Evidnet/gte_ft_measurement") # Run inference sentences = [ 'Quantitative Group 1,Diagnostic and laboratory test qualitative addition(3%), Clinical Pathologist etc. reading, SST serum, HBV DNA Quan(RQ PCR)', 'Hepatitis B virus DNA [#/volume] (viral load) in Serum or Plasma by NAA with probe detection', 'Microscopic observation [Identifier] in Synovial fluid by Gram stain', ] 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] ``` <!-- ### 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. --> <!-- ## 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: 888 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 888 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 33.04 tokens</li><li>max: 76 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 19.51 tokens</li><li>max: 51 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------\| \| <code>Thyroid Stimulating Hormone- Thyroid Stimulating Hormone,Diagnostic and laboratory test qualitative addition(3%), Serum, TSH</code> \| <code>Thyrotropin [Units/volume] in Serum or Plasma</code> \| \| <code>Calcitonin, Whole Blood, (외주) Calcitonin</code> \| <code>Calcitonin [Mass/volume] in Serum or Plasma</code> \| \| <code>Gonadotropin- Follicle Stimulating Hormone, Serum, [RIA] FSH</code> \| <code>Follitropin [Units/volume] in Serum or Plasma</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `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`: 3 - `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} - `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 - `dispatch_batches`: None - `split_batches`: 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> ### Framework Versions - Python: 3.11.10 - Sentence Transformers: 3.4.1 - Transformers: 4.47.1 - PyTorch: 2.6.0+cu124 - Accelerate: 1.3.0 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## 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", } ``` #### 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. -->
andreasDrastyc/unsloth_finetune	andreasDrastyc	2025-04-28T08:58:48Z	6	0	transformers	[ "transformers", "mllama", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-08T13:22:38Z	--- base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mllama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: andreasDrastyc - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit This mllama 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)
rajendraambati/image-detection-cr1	rajendraambati	2025-04-28T08:57:41Z	0	0	null	[ "objectdetection", "computervision", "object-detection", "en", "base_model:Ultralytics/YOLOv8", "base_model:finetune:Ultralytics/YOLOv8", "license:mit", "region:us" ]	object-detection	2025-04-28T08:52:13Z	--- license: mit language: - en base_model: - Ultralytics/YOLOv8 pipeline_tag: object-detection tags: - objectdetection - computervision ---
CC-AI-Labs/sharks-triplet-hsm-bert-base-uncased-2025-04	CC-AI-Labs	2025-04-28T08:55:50Z	13	0	sentence-transformers	[ "sentence-transformers", "tf", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-24T06:39:36Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 105 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.BatchHardSoftMarginTripletLoss.BatchHardSoftMarginTripletLoss` Parameters of the fit()-Method: ``` { "epochs": 69, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 6e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 724, "weight_decay": 0 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (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}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
SkLOxfk807mQB/SkLOxfk807mQB	SkLOxfk807mQB	2025-04-28T08:55:34Z	0	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2025-04-28T08:55:34Z	--- license: bsd-3-clause ---
kokovova/2fb9437b-834e-48ee-9e8f-bddba5056d29	kokovova	2025-04-28T08:54:49Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored", "base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T08:49:12Z	--- library_name: peft license: llama3 base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored tags: - axolotl - generated_from_trainer model-index: - name: 2fb9437b-834e-48ee-9e8f-bddba5056d29 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 3dd11039ea2f9879_train_data.json ds_type: json format: custom path: /workspace/input_data/3dd11039ea2f9879_train_data.json type: field_input: description field_instruction: question field_output: objective format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: kokovova/2fb9437b-834e-48ee-9e8f-bddba5056d29 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/3dd11039ea2f9879_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 16a27aeb-f760-4a00-a378-a3ec18757692 wandb_project: s56-4 wandb_run: your_name wandb_runid: 16a27aeb-f760-4a00-a378-a3ec18757692 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 2fb9437b-834e-48ee-9e8f-bddba5056d29 This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9235 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0356 \| 0.0497 \| 200 \| 0.9235 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mlfoundations-dev/nemo_nano_100k	mlfoundations-dev	2025-04-28T08:53:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T08:50:42Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: nemo_nano_100k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nemo_nano_100k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/nemo_nano_100k 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: 8e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 512 - total_train_batch_size: 512 - total_eval_batch_size: 4096 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
MayBashendy/ellipse_SDP_1_binary_multilingual_e5_small_lr3e-05_targ1	MayBashendy	2025-04-28T08:53:05Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-04-28T01:33:13Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
lisabdunlap/Qwen2.5-7B-Instruct-bnb-4bit-r64-e3-lr0.0002-new	lisabdunlap	2025-04-28T08:51:31Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T08:49:42Z	--- base_model: unsloth/qwen2.5-7b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Journey9ni/llava_video_7b_qwen2_lora_base	Journey9ni	2025-04-28T08:49:40Z	0	0	peft	[ "peft", "llava", "region:us" ]	null	2025-04-28T08:49:22Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0 ## Training procedure
orozcohsu/translation_zh_en	orozcohsu	2025-04-28T08:48:01Z	4	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "machine-translation", "zh", "en", "dataset:custom", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2025-04-10T10:59:39Z	--- language: - zh - en tags: - translation - machine-translation - transformers datasets: - custom model-index: - name: transformer-zh-en-finetuned results: - task: type: translation name: Translation (ZH ➔ EN) dataset: name: Custom Dataset type: custom metrics: - type: bleu value: (填上你的 BLEU 分數) --- # 中文 ➔ 英文機器翻譯模型 (Fine-tuned Transformer) ## 📚 模型簡介本模型基於 [Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en) 預訓練權重，針對繁體中文 ➔ 英文翻譯任務進行微調。使用 Hugging Face `transformers` 庫完成訓練，並以 BLEU 分數作為主要評估指標。 --- ## 🔧 訓練資訊 - 基礎模型：Helsinki-NLP/opus-mt-zh-en - 資料來源：自定義資料集，包含繁體中文輸入與英文翻譯目標 - Tokenization：自動使用對應 checkpoint 的 tokenizer - 最大輸入長度：128 - 訓練方式：使用 `Seq2SeqTrainer` 完成微調 - 訓練週期（epochs）：1 - 學習率（learning rate）：2e-5 - Batch size：8（訓練與驗證） - 保存策略：每500步保存checkpoint，最多保留3個 --- ## 📝 評估方式 - 指標：BLEU 分數 (使用 sacrebleu) - 其他設定： - `predict_with_generate=True`（生成翻譯以供評分） - 取100筆小型測試集進行快速驗證 - 使用單束（num_beams=1）生成 --- ## 📂 輸出結果 - 訓練過程紀錄於 `training_log.csv` - 完整保存模型及 tokenizer 至 `./results/transformer_v1` - 支援直接載入推論 (inference) --- ## ⚡ 推理示範 ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("./results/transformer_v1") tokenizer = AutoTokenizer.from_pretrained("./results/transformer_v1") inputs = tokenizer("外出要小心注意安全", return_tensors="pt") outputs = model.generate(**inputs) translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(translated_text)
duHWzukW0Fn/duHWzukW0Fn	duHWzukW0Fn	2025-04-28T08:47:26Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-04-28T08:47:26Z	--- license: bigcode-openrail-m ---
ranranrunforit/rl_course_vizdoom_health_gathering_supreme	ranranrunforit	2025-04-28T08:46:50Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-28T08:46:46Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.38 +/- 5.37 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r ranranrunforit/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
looneytoonz/Tunez	looneytoonz	2025-04-28T08:44:35Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-04-28T08:44:35Z	--- license: artistic-2.0 ---
Snatcher/Huggingface	Snatcher	2025-04-28T08:41:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T08:41:19Z	--- license: apache-2.0 ---
Benjaminpwh/xls-r-300m-toratan-240	Benjaminpwh	2025-04-28T08:40:21Z	2	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:Benjaminpwh/xlsr-toratan-240-copt-base_K", "base_model:finetune:Benjaminpwh/xlsr-toratan-240-copt-base_K", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-27T21:58:34Z	--- library_name: transformers base_model: Benjaminpwh/xlsr-toratan-240-copt-base_K tags: - generated_from_trainer model-index: - name: xls-r-300m-toratan-240 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. --> # xls-r-300m-toratan-240 This model is a fine-tuned version of [Benjaminpwh/xlsr-toratan-240-copt-base_K](https://huggingface.co/Benjaminpwh/xlsr-toratan-240-copt-base_K) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0082 - Cer: 0.0025 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\| \| 4.2511 \| 2.0833 \| 400 \| 1.9040 \| 0.5065 \| \| 1.6576 \| 4.1667 \| 800 \| 1.1012 \| 0.3104 \| \| 1.2488 \| 6.25 \| 1200 \| 0.8374 \| 0.2464 \| \| 0.9964 \| 8.3333 \| 1600 \| 0.6042 \| 0.1834 \| \| 0.8249 \| 10.4167 \| 2000 \| 0.4650 \| 0.1583 \| \| 0.677 \| 12.5 \| 2400 \| 0.3377 \| 0.1236 \| \| 0.5339 \| 14.5833 \| 2800 \| 0.2268 \| 0.0833 \| \| 0.4255 \| 16.6667 \| 3200 \| 0.1452 \| 0.0540 \| \| 0.3489 \| 18.75 \| 3600 \| 0.0946 \| 0.0386 \| \| 0.2719 \| 20.8333 \| 4000 \| 0.0551 \| 0.0187 \| \| 0.2151 \| 22.9167 \| 4400 \| 0.0290 \| 0.0112 \| \| 0.1854 \| 25.0 \| 4800 \| 0.0187 \| 0.0069 \| \| 0.1468 \| 27.0833 \| 5200 \| 0.0127 \| 0.0047 \| \| 0.1284 \| 29.1667 \| 5600 \| 0.0082 \| 0.0025 \| ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
EYEDOL/whisper-small-swahili	EYEDOL	2025-04-28T08:39:24Z	3	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-28T06:06:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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FundamentalResearchLabs/p-lo-d16-578171-1577279-8e3389f-s2526	FundamentalResearchLabs	2025-04-28T08:37:53Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-3-12b-it", "base_model:adapter:google/gemma-3-12b-it", "region:us" ]	null	2025-04-28T08:37:14Z	--- base_model: google/gemma-3-12b-it library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF	Triangle104	2025-04-28T08:35:12Z	1	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-22T17:16:08Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_m.gguf -c 2048 ```
Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_S-GGUF	Triangle104	2025-04-28T08:35:04Z	2	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-22T17:14:25Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_S-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-1.5b-instruct-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 Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_s.gguf -c 2048 ```
Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF	Triangle104	2025-04-28T08:34:55Z	3	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-22T17:08:24Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -c 2048 ```
Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_S-GGUF	Triangle104	2025-04-28T08:34:47Z	2	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-22T17:06:02Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_S-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_S-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_S-GGUF --hf-file qwen2.5-1.5b-instruct-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 Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_S-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-1.5B-Instruct-Q4_K_S-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_s.gguf -c 2048 ```
Dazelin/TOK	Dazelin	2025-04-28T08:34:38Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T08:19:24Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Tok <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/Dazelin/TOK/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Dazelin/TOK', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Dazelin/TOK/discussions) to add images that show off what you’ve made with this LoRA.
Triangle104/GLM-Z1-Rumination-32B-0414-Q5_K_M-GGUF	Triangle104	2025-04-28T08:34:31Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "zh", "en", "base_model:THUDM/GLM-Z1-Rumination-32B-0414", "base_model:quantized:THUDM/GLM-Z1-Rumination-32B-0414", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T08:24:00Z	--- base_model: THUDM/GLM-Z1-Rumination-32B-0414 language: - zh - en library_name: transformers license: mit pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # Triangle104/GLM-Z1-Rumination-32B-0414-Q5_K_M-GGUF This model was converted to GGUF format from [`THUDM/GLM-Z1-Rumination-32B-0414`](https://huggingface.co/THUDM/GLM-Z1-Rumination-32B-0414) 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/THUDM/GLM-Z1-Rumination-32B-0414) for more details on the model. --- Introduction - The GLM family welcomes a new generation of open-source models, the GLM-4-32B-0414 series, featuring 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including a large amount of reasoning-type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post-training stage, in addition to human preference alignment for dialogue scenarios, we also enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in areas such as engineering code, Artifact generation, function calling, search-based Q&A, and report generation. Some benchmarks even rival larger models like GPT-4o and DeepSeek-V3-0324 (671B). GLM-Z1-Rumination-32B-0414 is a deep reasoning model with rumination capabilities (benchmarked against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model employs longer periods of deep thought to solve more open-ended and complex problems (e.g., writing a comparative analysis of AI development in two cities and their future development plans). The rumination model integrates search tools during its deep thinking process to handle complex tasks and is trained by utilizing multiple rule-based rewards to guide and extend end-to-end reinforcement learning. Z1-Rumination shows significant improvements in research-style writing and complex retrieval tasks. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/GLM-Z1-Rumination-32B-0414-Q5_K_M-GGUF --hf-file glm-z1-rumination-32b-0414-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/GLM-Z1-Rumination-32B-0414-Q5_K_M-GGUF --hf-file glm-z1-rumination-32b-0414-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/GLM-Z1-Rumination-32B-0414-Q5_K_M-GGUF --hf-file glm-z1-rumination-32b-0414-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/GLM-Z1-Rumination-32B-0414-Q5_K_M-GGUF --hf-file glm-z1-rumination-32b-0414-q5_k_m.gguf -c 2048 ```
Anwaarma/L1-finetune-ep3	Anwaarma	2025-04-28T08:33:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T08:33:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kC5GThiQeXO5c/kC5GThiQeXO5c	kC5GThiQeXO5c	2025-04-28T08:33:21Z	0	0	null	[ "license:bsd-2-clause", "region:us" ]	null	2025-04-28T08:33:21Z	--- license: bsd-2-clause ---
nikunjkakadiya/Reinforce-PixelCopter	nikunjkakadiya	2025-04-28T08:31:21Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-04-22T17:01:48Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 34.60 +/- 29.17 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
jesse9527/Idefics3-8B-Llama3-Q4_K_M-GGUF	jesse9527	2025-04-28T08:30:13Z	0	0	transformers	[ "transformers", "gguf", "multimodal", "vision", "image-text-to-text", "llama-cpp", "gguf-my-repo", "en", "dataset:HuggingFaceM4/OBELICS", "dataset:HuggingFaceM4/the_cauldron", "dataset:HuggingFaceM4/Docmatix", "dataset:HuggingFaceM4/WebSight", "base_model:HuggingFaceM4/Idefics3-8B-Llama3", "base_model:quantized:HuggingFaceM4/Idefics3-8B-Llama3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-04-28T08:29:50Z	--- base_model: HuggingFaceM4/Idefics3-8B-Llama3 datasets: - HuggingFaceM4/OBELICS - HuggingFaceM4/the_cauldron - HuggingFaceM4/Docmatix - HuggingFaceM4/WebSight language: - en library_name: transformers license: apache-2.0 tags: - multimodal - vision - image-text-to-text - llama-cpp - gguf-my-repo --- # jesse9527/Idefics3-8B-Llama3-Q4_K_M-GGUF This model was converted to GGUF format from [`HuggingFaceM4/Idefics3-8B-Llama3`](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) 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/HuggingFaceM4/Idefics3-8B-Llama3) 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 jesse9527/Idefics3-8B-Llama3-Q4_K_M-GGUF --hf-file idefics3-8b-llama3-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo jesse9527/Idefics3-8B-Llama3-Q4_K_M-GGUF --hf-file idefics3-8b-llama3-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo jesse9527/Idefics3-8B-Llama3-Q4_K_M-GGUF --hf-file idefics3-8b-llama3-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo jesse9527/Idefics3-8B-Llama3-Q4_K_M-GGUF --hf-file idefics3-8b-llama3-q4_k_m.gguf -c 2048 ```
MrRobotoAI/E2	MrRobotoAI	2025-04-28T08:27:00Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2212.04089", "base_model:MrRobotoAI/B1", "base_model:merge:MrRobotoAI/B1", "base_model:MrRobotoAI/E1", "base_model:merge:MrRobotoAI/E1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T08:23:55Z	--- base_model: - MrRobotoAI/B1 - MrRobotoAI/E1 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 [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/B1](https://huggingface.co/MrRobotoAI/B1) as a base. ### Models Merged The following models were included in the merge: * [MrRobotoAI/E1](https://huggingface.co/MrRobotoAI/E1) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic models: - model: MrRobotoAI/B1 parameters: weight: - filter: v_proj value: [0.8, 0.8, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.8, 0.8] - filter: o_proj value: [0.8, 0.8, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.8, 0.8] - filter: up_proj value: [0.8, 0.8, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.8, 0.8] - filter: gate_proj value: [0.8, 0.8, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.8, 0.8] - filter: down_proj value: [0.8, 0.8, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.8, 0.8] - value: 1 - model: MrRobotoAI/E1 parameters: weight: - filter: v_proj value: [0.2, 0.2, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.2, 0.2] - filter: o_proj value: [0.2, 0.2, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.2, 0.2] - filter: up_proj value: [0.2, 0.2, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.2, 0.2] - filter: gate_proj value: [0.2, 0.2, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.2, 0.2] - filter: down_proj value: [0.2, 0.2, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.2, 0.2] - value: 0 base_model: MrRobotoAI/B1 dtype: bfloat16 ```
ramcargpt/BatQwen2.5-38K	ramcargpt	2025-04-28T08:25:31Z	0	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Qwen2.5-3B-unsloth-bnb-4bit", "base_model:quantized:unsloth/Qwen2.5-3B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T07:15:25Z	--- base_model: unsloth/Qwen2.5-3B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: ramcargpt - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-3B-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
yamatazen/SnowElf-12B	yamatazen	2025-04-28T08:25:03Z	1	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "chatml", "conversational", "en", "ja", "arxiv:2306.01708", "base_model:PocketDoc/Dans-PersonalityEngine-V1.1.0-12b", "base_model:merge:PocketDoc/Dans-PersonalityEngine-V1.1.0-12b", "base_model:inflatebot/MN-12B-Mag-Mell-R1", "base_model:merge:inflatebot/MN-12B-Mag-Mell-R1", "base_model:nbeerbower/mistral-nemo-gutenberg-12B-v4", "base_model:merge:nbeerbower/mistral-nemo-gutenberg-12B-v4", "base_model:yamatazen/HMS-Slerp-12B-v2", "base_model:merge:yamatazen/HMS-Slerp-12B-v2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T07:04:53Z	--- base_model: - inflatebot/MN-12B-Mag-Mell-R1 - nbeerbower/mistral-nemo-gutenberg-12B-v4 - yamatazen/HMS-Slerp-12B-v2 - PocketDoc/Dans-PersonalityEngine-V1.1.0-12b library_name: transformers tags: - mergekit - merge - chatml language: - en - ja --- ![image/png](https://huggingface.co/yamatazen/SnowElf-12B/resolve/main/SnowElf-12B.png?download=true) # 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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [yamatazen/HMS-Slerp-12B-v2](https://huggingface.co/yamatazen/HMS-Slerp-12B-v2) as a base. ### Models Merged The following models were included in the merge: * [inflatebot/MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1) * [nbeerbower/mistral-nemo-gutenberg-12B-v4](https://huggingface.co/nbeerbower/mistral-nemo-gutenberg-12B-v4) * [PocketDoc/Dans-PersonalityEngine-V1.1.0-12b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.1.0-12b) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: yamatazen/HMS-Slerp-12B-v2 models: - model: nbeerbower/mistral-nemo-gutenberg-12B-v4 parameters: density: 0.75 weight: 0.8 - model: inflatebot/MN-12B-Mag-Mell-R1 parameters: density: 0.6 weight: 0.6 - model: PocketDoc/Dans-PersonalityEngine-V1.1.0-12b parameters: density: 0.5 weight: 0.3 merge_method: ties dtype: bfloat16 parameters: normalize: true tokenizer: source: union ```
MrRobotoAI/E1	MrRobotoAI	2025-04-28T08:23:29Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Azazelle/Llama-3-8B-Abomination-LORA", "base_model:merge:Azazelle/Llama-3-8B-Abomination-LORA", "base_model:Azazelle/Llama-3-LongStory-LORA", "base_model:merge:Azazelle/Llama-3-LongStory-LORA", "base_model:Blackroot/Llama3-RP-Lora", "base_model:merge:Blackroot/Llama3-RP-Lora", "base_model:MrRobotoAI/E0", "base_model:merge:MrRobotoAI/E0", "base_model:Nuts123/mistral-finetuned-bookcorpus", "base_model:merge:Nuts123/mistral-finetuned-bookcorpus", "base_model:ResplendentAI/NoWarning_Llama3", "base_model:merge:ResplendentAI/NoWarning_Llama3", "base_model:Triangle104/Vulkane_120-Days-of-Sodom-LoRA-Mistral-7b", "base_model:merge:Triangle104/Vulkane_120-Days-of-Sodom-LoRA-Mistral-7b", "base_model:W1lson/mistral-trained-on-Book3", "base_model:merge:W1lson/mistral-trained-on-Book3", "base_model:W1lson/zephyr-book-3", "base_model:merge:W1lson/zephyr-book-3", "base_model:athirdpath/BigMistral-11b-GLUE_LORA", "base_model:merge:athirdpath/BigMistral-11b-GLUE_LORA", "base_model:basilePlus/llama3-8b-schopenhauer", "base_model:merge:basilePlus/llama3-8b-schopenhauer", "base_model:hf-100/Llama-3.1-Spellbound-StoryWriter-0.1-lora", "base_model:merge:hf-100/Llama-3.1-Spellbound-StoryWriter-0.1-lora", "base_model:jeiku/Humiliation_Mistral", "base_model:merge:jeiku/Humiliation_Mistral", "base_model:jeiku/NSFW_Niche_Mistral", "base_model:merge:jeiku/NSFW_Niche_Mistral", "base_model:jeiku/Sissification_Hypno_Mistral", "base_model:merge:jeiku/Sissification_Hypno_Mistral", "base_model:jeiku/Synthetic_Soul_1k_Mistral_128", "base_model:merge:jeiku/Synthetic_Soul_1k_Mistral_128", "base_model:jeiku/Writing_Mistral", "base_model:merge:jeiku/Writing_Mistral", "base_model:jspr/llama3-instruct-wordcel-smutrom-8k_peft", "base_model:merge:jspr/llama3-instruct-wordcel-smutrom-8k_peft", "base_model:jspr/smut_llama_8b_smutromance_32k_peft", "base_model:merge:jspr/smut_llama_8b_smutromance_32k_peft", "base_model:marsfu2009/writer_lora", "base_model:merge:marsfu2009/writer_lora", "base_model:mpasila/Llama-3.1-Literotica-LoRA-8B", "base_model:merge:mpasila/Llama-3.1-Literotica-LoRA-8B", "base_model:nothingiisreal/llama3-8B-DWP-lora", "base_model:merge:nothingiisreal/llama3-8B-DWP-lora", "base_model:vincentyandex/lora_llama3_chunked_novel_bs128", "base_model:merge:vincentyandex/lora_llama3_chunked_novel_bs128", "base_model:yjcsean/fintuned_story", "base_model:merge:yjcsean/fintuned_story", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T07:40:15Z	--- base_model: - MrRobotoAI/E0 - yjcsean/fintuned_story - MrRobotoAI/E0 - jeiku/Writing_Mistral - MrRobotoAI/E0 - jeiku/NSFW_Niche_Mistral - MrRobotoAI/E0 - Azazelle/Llama-3-LongStory-LORA - MrRobotoAI/E0 - jspr/smut_llama_8b_smutromance_32k_peft - MrRobotoAI/E0 - nothingiisreal/llama3-8B-DWP-lora - MrRobotoAI/E0 - jeiku/Synthetic_Soul_1k_Mistral_128 - MrRobotoAI/E0 - jspr/llama3-instruct-wordcel-smutrom-8k_peft - MrRobotoAI/E0 - jeiku/Sissification_Hypno_Mistral - MrRobotoAI/E0 - athirdpath/BigMistral-11b-GLUE_LORA - MrRobotoAI/E0 - basilePlus/llama3-8b-schopenhauer - MrRobotoAI/E0 - jeiku/Humiliation_Mistral - MrRobotoAI/E0 - Azazelle/Llama-3-8B-Abomination-LORA - MrRobotoAI/E0 - vincentyandex/lora_llama3_chunked_novel_bs128 - MrRobotoAI/E0 - Nuts123/mistral-finetuned-bookcorpus - MrRobotoAI/E0 - mpasila/Llama-3.1-Literotica-LoRA-8B - MrRobotoAI/E0 - Triangle104/Vulkane_120-Days-of-Sodom-LoRA-Mistral-7b - MrRobotoAI/E0 - Blackroot/Llama3-RP-Lora - MrRobotoAI/E0 - ResplendentAI/NoWarning_Llama3 - MrRobotoAI/E0 - hf-100/Llama-3.1-Spellbound-StoryWriter-0.1-lora - MrRobotoAI/E0 - W1lson/zephyr-book-3 - MrRobotoAI/E0 - MrRobotoAI/E0 - W1lson/mistral-trained-on-Book3 - MrRobotoAI/E0 - marsfu2009/writer_lora 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) as a base. ### Models Merged The following models were included in the merge: * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [yjcsean/fintuned_story](https://huggingface.co/yjcsean/fintuned_story) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [jeiku/Writing_Mistral](https://huggingface.co/jeiku/Writing_Mistral) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [jeiku/NSFW_Niche_Mistral](https://huggingface.co/jeiku/NSFW_Niche_Mistral) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [Azazelle/Llama-3-LongStory-LORA](https://huggingface.co/Azazelle/Llama-3-LongStory-LORA) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [jspr/smut_llama_8b_smutromance_32k_peft](https://huggingface.co/jspr/smut_llama_8b_smutromance_32k_peft) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [nothingiisreal/llama3-8B-DWP-lora](https://huggingface.co/nothingiisreal/llama3-8B-DWP-lora) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [jeiku/Synthetic_Soul_1k_Mistral_128](https://huggingface.co/jeiku/Synthetic_Soul_1k_Mistral_128) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [jspr/llama3-instruct-wordcel-smutrom-8k_peft](https://huggingface.co/jspr/llama3-instruct-wordcel-smutrom-8k_peft) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [jeiku/Sissification_Hypno_Mistral](https://huggingface.co/jeiku/Sissification_Hypno_Mistral) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [athirdpath/BigMistral-11b-GLUE_LORA](https://huggingface.co/athirdpath/BigMistral-11b-GLUE_LORA) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [basilePlus/llama3-8b-schopenhauer](https://huggingface.co/basilePlus/llama3-8b-schopenhauer) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [jeiku/Humiliation_Mistral](https://huggingface.co/jeiku/Humiliation_Mistral) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [Azazelle/Llama-3-8B-Abomination-LORA](https://huggingface.co/Azazelle/Llama-3-8B-Abomination-LORA) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [vincentyandex/lora_llama3_chunked_novel_bs128](https://huggingface.co/vincentyandex/lora_llama3_chunked_novel_bs128) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [Nuts123/mistral-finetuned-bookcorpus](https://huggingface.co/Nuts123/mistral-finetuned-bookcorpus) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [mpasila/Llama-3.1-Literotica-LoRA-8B](https://huggingface.co/mpasila/Llama-3.1-Literotica-LoRA-8B) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [Triangle104/Vulkane_120-Days-of-Sodom-LoRA-Mistral-7b](https://huggingface.co/Triangle104/Vulkane_120-Days-of-Sodom-LoRA-Mistral-7b) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [Blackroot/Llama3-RP-Lora](https://huggingface.co/Blackroot/Llama3-RP-Lora) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [ResplendentAI/NoWarning_Llama3](https://huggingface.co/ResplendentAI/NoWarning_Llama3) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [hf-100/Llama-3.1-Spellbound-StoryWriter-0.1-lora](https://huggingface.co/hf-100/Llama-3.1-Spellbound-StoryWriter-0.1-lora) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [W1lson/zephyr-book-3](https://huggingface.co/W1lson/zephyr-book-3) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [W1lson/mistral-trained-on-Book3](https://huggingface.co/W1lson/mistral-trained-on-Book3) * [MrRobotoAI/E0](https://huggingface.co/MrRobotoAI/E0) + [marsfu2009/writer_lora](https://huggingface.co/marsfu2009/writer_lora) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: MrRobotoAI/E0+jeiku/Writing_Mistral - model: MrRobotoAI/E0+ResplendentAI/NoWarning_Llama3 - model: MrRobotoAI/E0+hf-100/Llama-3.1-Spellbound-StoryWriter-0.1-lora - model: MrRobotoAI/E0+Nuts123/mistral-finetuned-bookcorpus - model: MrRobotoAI/E0+athirdpath/BigMistral-11b-GLUE_LORA - model: MrRobotoAI/E0+Azazelle/Llama-3-8B-Abomination-LORA - model: MrRobotoAI/E0+Azazelle/Llama-3-LongStory-LORA - model: MrRobotoAI/E0+basilePlus/llama3-8b-schopenhauer - model: MrRobotoAI/E0+Blackroot/Llama3-RP-Lora - model: MrRobotoAI/E0+jeiku/Humiliation_Mistral - model: MrRobotoAI/E0+jeiku/NSFW_Niche_Mistral - model: MrRobotoAI/E0+jeiku/Sissification_Hypno_Mistral - model: MrRobotoAI/E0+jeiku/Synthetic_Soul_1k_Mistral_128 - model: MrRobotoAI/E0+jspr/llama3-instruct-wordcel-smutrom-8k_peft - model: MrRobotoAI/E0+jspr/smut_llama_8b_smutromance_32k_peft - model: MrRobotoAI/E0+marsfu2009/writer_lora - model: MrRobotoAI/E0+yjcsean/fintuned_story - model: MrRobotoAI/E0+vincentyandex/lora_llama3_chunked_novel_bs128 - model: MrRobotoAI/E0+W1lson/mistral-trained-on-Book3 - model: MrRobotoAI/E0+W1lson/zephyr-book-3 - model: MrRobotoAI/E0+mpasila/Llama-3.1-Literotica-LoRA-8B - model: MrRobotoAI/E0+nothingiisreal/llama3-8B-DWP-lora - model: MrRobotoAI/E0+Triangle104/Vulkane_120-Days-of-Sodom-LoRA-Mistral-7b merge_method: model_stock base_model: MrRobotoAI/E0 normalize: true dtype: float16 ```
fostertrixie/fostertrixie	fostertrixie	2025-04-28T08:22:44Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-04-28T08:22:44Z	--- license: bigscience-bloom-rail-1.0 ---
deeponh/hindi_9b_2b_D2	deeponh	2025-04-28T08:20:15Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T08:15:31Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
BlueLiu2004/Phi-4-raw-lora	BlueLiu2004	2025-04-28T08:18:16Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/phi-4-unsloth-bnb-4bit", "base_model:finetune:unsloth/phi-4-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T08:18:05Z	--- base_model: unsloth/phi-4-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: BlueLiu2004 - License: apache-2.0 - Finetuned from model : unsloth/phi-4-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)
BlueLiu2004/Phi-4-raw-merged_16bit	BlueLiu2004	2025-04-28T08:17:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/phi-4-unsloth-bnb-4bit", "base_model:finetune:unsloth/phi-4-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T06:29:00Z	--- base_model: unsloth/phi-4-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: BlueLiu2004 - License: apache-2.0 - Finetuned from model : unsloth/phi-4-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)
Neelectric/OLMo-2-1124-7B-Instruct_SFTv01.05	Neelectric	2025-04-28T08:16:27Z	0	0	transformers	[ "transformers", "safetensors", "olmo2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:Neelectric/OpenR1-Math-cn_k12-91k", "base_model:allenai/OLMo-2-1124-7B-Instruct", "base_model:finetune:allenai/OLMo-2-1124-7B-Instruct", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T02:44:30Z	--- base_model: allenai/OLMo-2-1124-7B-Instruct datasets: Neelectric/OpenR1-Math-cn_k12-91k library_name: transformers model_name: OLMo-2-1124-7B-Instruct_SFTv01.05 tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for OLMo-2-1124-7B-Instruct_SFTv01.05 This model is a fine-tuned version of [allenai/OLMo-2-1124-7B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct) on the [Neelectric/OpenR1-Math-cn_k12-91k](https://huggingface.co/datasets/Neelectric/OpenR1-Math-cn_k12-91k) 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="Neelectric/OLMo-2-1124-7B-Instruct_SFTv01.05", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/neelectric/open-r1_SFT/runs/8er3bkw3) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ggbaobao/medc_llm_based_on_qwen2.5	ggbaobao	2025-04-28T08:15:32Z	22	3	null	[ "safetensors", "qwen2", "medical", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:mit", "region:us" ]	null	2025-04-21T08:04:18Z	--- license: mit language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-7B-Instruct tags: - medical --- ## Model Details This model has been LoRA‑fine‑tuned on Qwen2.5‑7B‑Instruct. In the future, reinforcement learning training may be carried out based on this model, such as DPRO algorithm, etc. ### Base Model Sources [optional] https://huggingface.co/Qwen/Qwen2.5-7B-Instruct ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "ggbaobao/medc_llm_based_on_qwen2.5" model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", torch_dtype=torch.bfloat16 ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "猩红热多在发热后多久出现皮疹，请从以下选项中选择：12小时之内, 12～48小时, 60～72小时, 84～96小时, 大于96小时" messages = [ {"role": "system", "content": "You are Qwen, You are a helpful assistant."}, {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( *model_inputs, max_new_tokens=512, do_sample=True ) generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ## Training Details ```python lora_config = LoraConfig( r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"], lora_dropout=0.1 ) training_args = TrainingArguments( output_dir="./results_final1", learning_rate=7e-5, per_device_train_batch_size=2, per_device_eval_batch_size=2, gradient_accumulation_steps=1, # 梯度累积 num_train_epochs=2, evaluation_strategy="steps", # evaluate_steps=1, save_strategy="steps", save_steps=10, logging_steps=10, logging_dir="./logs1", bf16=True, # 混合精度训练 ``` ### Training Data The training data comes from https://github.com/SupritYoung/Zhongjing If you want to know more details about the above github project, you can also read their paper: Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue The data includes about one-seventh of the multi-round medical consultation data and six-sevenths of the single medical consultation data. #### Hardware vGPU-32GB 6 #### Software use peft and deepspeed
mukel/Qwen2.5-1.5B-Instruct-GGUF	mukel	2025-04-28T08:14:46Z	5	0	null	[ "gguf", "code", "codeqwen", "chat", "qwen", "qwen-coder", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-23T00:05:51Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-1.5B-Instruct pipeline_tag: text-generation quantized_by: mukel tags: - code - codeqwen - chat - qwen - qwen-coder --- # GGUF models for qwen2.java Pure .gguf Q4_0 and Q8_0 quantizations of Qwen 2.5 models, ready to consume by `qwen2.java`. In the wild, Q8_0 quantizations are fine, but Q4_0 quantizations are rarely pure e.g. the token embeddings are quantized with Q6_K, instead of Q4_0. A pure Q4_0 quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the llama-quantize utility from llama.cpp as follows: ``` ./llama-quantize --pure ./Qwen-2.5-7B-Instruct-BF16.gguf ./Qwen-2.5-7B-Instruct-Q4_0.gguf Q4_0 ``` ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
dexterkelsey/dexterkelsey	dexterkelsey	2025-04-28T08:13:46Z	0	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2025-04-28T08:13:46Z	--- license: bsd-3-clause ---
ZijieLei/Pretrain-1M_mwne_align_v2_16000	ZijieLei	2025-04-28T08:12:39Z	0	0	null	[ "safetensors", "qwen2", "license:apache-2.0", "region:us" ]	null	2025-04-28T08:10:33Z	--- license: apache-2.0 ---
HoseaDev/qwen3b-sql-fine-train	HoseaDev	2025-04-28T08:12:24Z	0	0	null	[ "safetensors", "gguf", "qwen2", "unsloth", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-04-28T07:56:58Z	--- license: mit tags: - unsloth ---
Disya/amoral-cogito-Zara-14B-Q4_K_M-GGUF	Disya	2025-04-28T08:11:11Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:Disya/amoral-cogito-Zara-14B", "base_model:quantized:Disya/amoral-cogito-Zara-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T08:10:12Z	--- base_model: Disya/amoral-cogito-Zara-14B library_name: transformers license: apache-2.0 tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Disya/amoral-cogito-Zara-14B-Q4_K_M-GGUF This model was converted to GGUF format from [`Disya/amoral-cogito-Zara-14B`](https://huggingface.co/Disya/amoral-cogito-Zara-14B) 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/Disya/amoral-cogito-Zara-14B) 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/amoral-cogito-Zara-14B-Q4_K_M-GGUF --hf-file amoral-cogito-zara-14b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Disya/amoral-cogito-Zara-14B-Q4_K_M-GGUF --hf-file amoral-cogito-zara-14b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Disya/amoral-cogito-Zara-14B-Q4_K_M-GGUF --hf-file amoral-cogito-zara-14b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Disya/amoral-cogito-Zara-14B-Q4_K_M-GGUF --hf-file amoral-cogito-zara-14b-q4_k_m.gguf -c 2048 ```
ranranrunforit/pi-LunarLander-v2	ranranrunforit	2025-04-28T08:10:09Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-04-28T08:09:55Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -142.81 +/- 92.31 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'ranranrunforit/pi-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
MerantixMomentum/acip_qwen25_14b	MerantixMomentum	2025-04-28T08:09:07Z	19	1	transformers	[ "transformers", "safetensors", "acip_model", "feature-extraction", "acip", "pytorch", "text-generation", "conversational", "custom_code", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:allenai/c4", "arxiv:2502.01717", "base_model:Qwen/Qwen2.5-14B", "base_model:finetune:Qwen/Qwen2.5-14B", "license:apache-2.0", "region:us" ]	text-generation	2025-04-15T15:59:19Z	--- license: apache-2.0 datasets: ['allenai/c4'] language: ['zho', 'eng', 'fra', 'spa', 'por', 'deu', 'ita', 'rus', 'jpn', 'kor', 'vie', 'tha', 'ara'] metrics: ['perplexity', 'accuracy'] tags: ['acip', 'pytorch'] base_model: - Qwen/Qwen2.5-14B pipeline_tag: text-generation library_name: transformers --- <div align="center"> <img width="30%" alt="logo" src="https://imgur.com/A0MCHPq.png"> </div> <div align="center"> <a href="https://github.com/merantix-momentum/acip"><img src="https://img.shields.io/badge/GitHub-%23121011.svg?logo=github&logoColor=white.svg" alt="github" style="display: inline-block; vertical-align: middle;"></a> <a href="https://arxiv.org/abs/2502.01717"><img src="https://img.shields.io/badge/arXiv-2502.01717-b31b1b.svg" alt="arxiv" style="display: inline-block; vertical-align: middle;"></a> <a href="https://acip.merantix-momentum.com/"><img alt="website" src="https://img.shields.io/website/https/acip.merantix-momentum.com.svg?down_color=red&down_message=offline&up_message=online" style="display: inline-block; vertical-align: middle;"></a> </div> <h2 align="center"> <p> [ <a href="https://github.com/merantix-momentum/acip">🤖 GitHub</a> \| <a href="https://arxiv.org/abs/2502.01717">📄 Paper</a> \| <a href="https://acip.merantix-momentum.com/">🌐 Website</a> ] </p> </h2> <h1 align="center"> <p>ACIP applied to Qwen/Qwen2.5-14B</p> </h1> This model repository is part of the ACIP Project and provides a compressible version of [`Qwen/Qwen2.5-14B`](https://huggingface.co/Qwen/Qwen2.5-14B). For more details, please visit our [code repo](https://github.com/merantix-momentum/acip). # Quick Start Just load the ACIP model via `from_pretrained`: ```python from transformers import AutoModel model = AutoModel.from_pretrained("MerantixMomentum/acip_qwen25_14b", trust_remote_code=True) ``` This will download and create a fully parameterized ACIP model that can be pruned to any compression rate you wish. For example, ```python model.prune_model_by_score(size_ratio=0.4) ``` will prune `model` to 40% if its original size measured in number of parameters, i.e., 60% compression rate. A unique feature of ACIP is that this operation is revertible in the sense that you can rerun `model.prune_model_by_score` as often as you like to evaluate your model at different sizes. Finally, you can "commit" to a certain ratio and run ```python model.compress() ``` which will discard all pruned mask values of compressible linear layers. Now the model is actually compressed and you should observe a significant decrease of memory usage (this step is not revertible without reloading the ACIP model). If you like, you can also run ```python model.quantize() ``` to save even more memory (we have only tested 4bit quantization with `bitsandbytes`, but you could also customize this). 🚀 That's it! You can now use your compressed model for inference or fine-tuning as any other Causal Language Model from 🤗 transformers. Note: The parameter `size_ratio` ranges from 1.0 to 0.0, indicating the model size after compression. For example, 0.4 means that the model has only 40% of the original number of parameters and 1.0 means no compression at all. Alternatively, you can also set `compression_rate` in `prune_model_by_score`, which is equivalent to `size_ratio = 1.0 - compression_rate`. # Dependencies To run an ACIP model from our hub, you only need minimal dependencies, namely `torch`, `transformers`, `peft`, and optionally, `bitsandbytes` in case you want to quantize your model. See [requirements.txt](requirements.txt) for pip-installable dependencies with exact version pins (newer version should work as well). # License This model is released under the apache-2.0 license. # Citation When using or referring to this model, please cite our [paper](https://arxiv.org/abs/2502.01717): ```bibtex @article{mxm2025acip, title={Choose Your Model Size: Any Compression by a Single Gradient Descent}, author={M. Genzel, P. Putzky, P. Zhao, S. Schulze, M. Mollenhauer, R. Seidel, S. Dietzel, T. Wollmann}, year={2025}, journal={Preprint arXiv:2502.01717} } ```
ujjawal077/cyber-ai-model_arabib_logs_add	ujjawal077	2025-04-28T08:07:27Z	1	0	peft	[ "peft", "safetensors", "mistral", "arxiv:1910.09700", "base_model:nj999/cyber_ai_arabic", "base_model:adapter:nj999/cyber_ai_arabic", "region:us" ]	null	2025-04-16T16:29:00Z	--- base_model: nj999/cyber_ai_arabic library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
petkopetkov/Qwen2.5-0.5B-song-lyrics-generation	petkopetkov	2025-04-28T08:07:07Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-0.5B", "base_model:finetune:Qwen/Qwen2.5-0.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-08T21:06:23Z	--- base_model: Qwen/Qwen2.5-0.5B library_name: transformers model_name: qwen2.5-0.5B-spotify-ft-no-lora tags: - generated_from_trainer - trl - sft licence: license language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Model Card for qwen2.5-0.5B-spotify-ft-no-lora This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B). 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="petkopetkov/qwen2.5-0.5B-spotify-ft-no-lora", 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/petko-petkov987-none/huggingface/runs/4j3ds8fd) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.47.1 - Pytorch: 2.5.1 - Datasets: 3.0.0 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
2600th/epicrealismXL_vxvi_LastfameRealism	2600th	2025-04-28T08:06:51Z	0	0	null	[ "text-to-image", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-04-28T07:24:45Z	--- license: creativeml-openrail-m language: - en base_model: - stabilityai/stable-diffusion-xl-base-1.0 pipeline_tag: text-to-image ---
alannahbeadle6/alannahbeadle6	alannahbeadle6	2025-04-28T08:04:05Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-04-28T08:04:05Z	--- license: bigscience-openrail-m ---
kkks05/Llama-3.2-1B-instruct-lora_spider	kkks05	2025-04-28T08:04:01Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T05:26:10Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kkks05 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ail-sa/swati_test2	ail-sa	2025-04-28T08:03:24Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T07:30:12Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Sidf --- # Swati_Test2 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Sidf` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sidf", "lora_weights": "https://huggingface.co/ail-sa/swati_test2/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ail-sa/swati_test2', weight_name='lora.safetensors') image = pipeline('Sidf').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ail-sa/swati_test2/discussions) to add images that show off what you’ve made with this LoRA.
tuandung2812/qwen_short_reasoning_2804	tuandung2812	2025-04-28T08:02:41Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-04-28T07:47:29Z	--- base_model: unsloth/qwen2.5-coder-32b-instruct-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
ncls-p/Qwen2.5-7B-blog-key-points	ncls-p	2025-04-28T08:00:25Z	120	0	null	[ "safetensors", "gguf", "qwen2", "text-generation", "summarization", "key-points", "blog-summarization", "unsloth", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:ncls-p/blog-key-points", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:quantized:Qwen/Qwen2.5-7B-Instruct", "license:cc-by-4.0", "endpoints_compatible", "region:us", "conversational" ]	summarization	2025-02-26T08:58:19Z	--- language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara tags: - qwen2 - text-generation - summarization - key-points - blog-summarization - unsloth datasets: - ncls-p/blog-key-points license: cc-by-4.0 base_model: Qwen/Qwen2.5-7B-Instruct --- # Qwen2.5-7B-blog-key-points This model is fine-tuned from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [blog-key-points dataset](https://huggingface.co/datasets/ncls-p/blog-key-points). It specializes in extracting key points from blog articles and web content, providing concise bullet-point summaries that capture the essential information. ## Model Description Qwen2.5-7B-blog-key-points is a 7B parameter model fine-tuned specifically for the task of extracting key points from articles. It can process a full article and generate a concise, bullet-point summary highlighting the most important information. Compared to the 3B version, this model offers enhanced capabilities for understanding complex articles and generating more nuanced summaries. ### Model Details - Model Type: Qwen2.5 (7B parameters) - Base Model: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) - Training Dataset: [ncls-p/blog-key-points](https://huggingface.co/datasets/ncls-p/blog-key-points) - Language: English - License: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) - Finetuning Approach: Instruction fine-tuning on article-summary pairs ## Uses ### Direct Use This model is designed for extracting key points from articles. You can use it directly for: - Summarizing blog posts - Extracting important information from news articles - Creating bullet-point summaries of long-form content - Generating concise overviews of research papers - Distilling complex information into digestible points ### Example Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "ncls-p/Qwen2.5-7B-blog-key-points" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) article = """ [Your article text here] """ prompt = f""" Extract the key points from the following article: {article} """ inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(inputs, max_length=1024) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## Training The model was fine-tuned on the [blog-key-points dataset](https://huggingface.co/datasets/ncls-p/blog-key-points), which contains 200 article-summary pairs. Each pair consists of a full article and a bullet-point summary of key points extracted using AI. ### Training Procedure - Fine-tuning Framework: [Unsloth](https://github.com/unslothai/unsloth) - Training Data Format:** ```json { "instruction": "", "input": "Full article content", "output": "Here are the key points of the article:\n* Key point 1\n* Key point 2\n* Key point 3\n..." } ``` ## Evaluation The model was evaluated on its ability to extract relevant key points from articles not seen during training. Evaluation metrics focused on: 1. Relevance: How well the extracted points capture the main ideas of the article 2. Conciseness: The ability to summarize information in a clear, bullet-point format 3. Completeness: Whether all important information is captured in the summary 4. Coherence: The logical flow and organization of the extracted points ## Limitations and Biases - The model may inherit biases present in the training data, including potential biases in the source articles or in the key point extraction process. - Performance may vary depending on the length, complexity, and domain of the input article. - The model is primarily trained on English-language content and may not perform well on content in other languages. - As with any summarization model, there is a risk of omitting important information or misrepresenting the original content. - While the 7B parameter size offers improved capabilities over the 3B version, it also requires more computational resources to run. ## How to Cite If you use this model in your research, please cite: ```bibtex @misc{qwen25-7b-blog-key-points, author = {ncls-p}, title = {Qwen2.5-7B-blog-key-points}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face model repository}, howpublished = {\url{https://huggingface.co/ncls-p/Qwen2.5-7B-blog-key-points}}, } ``` ## Dataset Creation The dataset used to train this model was created using the [llm-to-blog-key-points-dataset](https://github.com/ncls-p/llm-to-blog-key-points-dataset), a CLI tool that extracts key points from web articles AI and adds them to a dataset in a structured format.
CockfieldC99288/cfbcvbc	CockfieldC99288	2025-04-28T08:00:17Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2025-04-28T08:00:17Z	--- license: bsd-3-clause-clear ---
MerantixMomentum/acip_llama31_8b	MerantixMomentum	2025-04-28T07:59:13Z	25	1	transformers	[ "transformers", "safetensors", "acip_model", "feature-extraction", "acip", "pytorch", "text-generation", "custom_code", "en", "de", "fr", "it", "pt", "hi", "es", "th", "dataset:allenai/c4", "arxiv:2502.01717", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3.1", "region:us" ]	text-generation	2025-04-15T15:39:37Z	--- license: llama3.1 datasets: ['allenai/c4'] language: ['en', 'de', 'fr', 'it', 'pt', 'hi', 'es', 'th'] metrics: ['perplexity', 'accuracy'] tags: ['acip', 'pytorch'] base_model: - meta-llama/Llama-3.1-8B pipeline_tag: text-generation library_name: transformers --- <div align="center"> <img width="30%" alt="logo" src="https://imgur.com/A0MCHPq.png"> </div> <div align="center"> <a href="https://github.com/merantix-momentum/acip"><img src="https://img.shields.io/badge/GitHub-%23121011.svg?logo=github&logoColor=white.svg" alt="github" style="display: inline-block; vertical-align: middle;"></a> <a href="https://arxiv.org/abs/2502.01717"><img src="https://img.shields.io/badge/arXiv-2502.01717-b31b1b.svg" alt="arxiv" style="display: inline-block; vertical-align: middle;"></a> <a href="https://acip.merantix-momentum.com/"><img alt="website" src="https://img.shields.io/website/https/acip.merantix-momentum.com.svg?down_color=red&down_message=offline&up_message=online" style="display: inline-block; vertical-align: middle;"></a> </div> <h2 align="center"> <p> [ <a href="https://github.com/merantix-momentum/acip">🤖 GitHub</a> \| <a href="https://arxiv.org/abs/2502.01717">📄 Paper</a> \| <a href="https://acip.merantix-momentum.com/">🌐 Website</a> ] </p> </h2> <h1 align="center"> <p>ACIP applied to meta-llama/Llama-3.1-8B</p> </h1> This model repository is part of the ACIP Project and provides a compressible version of [`meta-llama/Llama-3.1-8B`](https://huggingface.co/meta-llama/Llama-3.1-8B). For more details, please visit our [code repo](https://github.com/merantix-momentum/acip). # Quick Start Just load the ACIP model via `from_pretrained`: ```python from transformers import AutoModel model = AutoModel.from_pretrained("MerantixMomentum/acip_llama31_8b", trust_remote_code=True) ``` This will download and create a fully parameterized ACIP model that can be pruned to any compression rate you wish. For example, ```python model.prune_model_by_score(size_ratio=0.4) ``` will prune `model` to 40% if its original size measured in number of parameters, i.e., 60% compression rate. A unique feature of ACIP is that this operation is revertible in the sense that you can rerun `model.prune_model_by_score` as often as you like to evaluate your model at different sizes. Finally, you can "commit" to a certain ratio and run ```python model.compress() ``` which will discard all pruned mask values of compressible linear layers. Now the model is actually compressed and you should observe a significant decrease of memory usage (this step is not revertible without reloading the ACIP model). If you like, you can also run ```python model.quantize() ``` to save even more memory (we have only tested 4bit quantization with `bitsandbytes`, but you could also customize this). 🚀 That's it! You can now use your compressed model for inference or fine-tuning as any other Causal Language Model from 🤗 transformers. Note: The parameter `size_ratio` ranges from 1.0 to 0.0, indicating the model size after compression. For example, 0.4 means that the model has only 40% of the original number of parameters and 1.0 means no compression at all. Alternatively, you can also set `compression_rate` in `prune_model_by_score`, which is equivalent to `size_ratio = 1.0 - compression_rate`. # Dependencies To run an ACIP model from our hub, you only need minimal dependencies, namely `torch`, `transformers`, `peft`, and optionally, `bitsandbytes` in case you want to quantize your model. See [requirements.txt](requirements.txt) for pip-installable dependencies with exact version pins (newer version should work as well). # License This model is released under the llama3.1 license. # Citation When using or referring to this model, please cite our [paper](https://arxiv.org/abs/2502.01717): ```bibtex @article{mxm2025acip, title={Choose Your Model Size: Any Compression by a Single Gradient Descent}, author={M. Genzel, P. Putzky, P. Zhao, S. Schulze, M. Mollenhauer, R. Seidel, S. Dietzel, T. Wollmann}, year={2025}, journal={Preprint arXiv:2502.01717} } ```
MerantixMomentum/acip_llama2_13b	MerantixMomentum	2025-04-28T07:56:39Z	27	1	transformers	[ "transformers", "safetensors", "acip_model", "feature-extraction", "acip", "pytorch", "text-generation", "custom_code", "en", "dataset:allenai/c4", "arxiv:2502.01717", "base_model:meta-llama/Llama-2-13b-hf", "base_model:finetune:meta-llama/Llama-2-13b-hf", "license:llama2", "region:us" ]	text-generation	2025-04-15T15:32:14Z	--- license: llama2 datasets: ['allenai/c4'] language: ['en'] metrics: ['perplexity', 'accuracy'] tags: ['acip', 'pytorch'] base_model: - meta-llama/Llama-2-13b-hf pipeline_tag: text-generation library_name: transformers --- <div align="center"> <img width="30%" alt="logo" src="https://imgur.com/A0MCHPq.png"> </div> <div align="center"> <a href="https://github.com/merantix-momentum/acip"><img src="https://img.shields.io/badge/GitHub-%23121011.svg?logo=github&logoColor=white.svg" alt="github" style="display: inline-block; vertical-align: middle;"></a> <a href="https://arxiv.org/abs/2502.01717"><img src="https://img.shields.io/badge/arXiv-2502.01717-b31b1b.svg" alt="arxiv" style="display: inline-block; vertical-align: middle;"></a> <a href="https://acip.merantix-momentum.com/"><img alt="website" src="https://img.shields.io/website/https/acip.merantix-momentum.com.svg?down_color=red&down_message=offline&up_message=online" style="display: inline-block; vertical-align: middle;"></a> </div> <h2 align="center"> <p> [ <a href="https://github.com/merantix-momentum/acip">🤖 GitHub</a> \| <a href="https://arxiv.org/abs/2502.01717">📄 Paper</a> \| <a href="https://acip.merantix-momentum.com/">🌐 Website</a> ] </p> </h2> <h1 align="center"> <p>ACIP applied to meta-llama/Llama-2-13b-hf</p> </h1> This model repository is part of the ACIP Project and provides a compressible version of [`meta-llama/Llama-2-13b-hf`](https://huggingface.co/meta-llama/Llama-2-13b-hf). For more details, please visit our [code repo](https://github.com/merantix-momentum/acip). # Quick Start Just load the ACIP model via `from_pretrained`: ```python from transformers import AutoModel model = AutoModel.from_pretrained("MerantixMomentum/acip_llama2_13b", trust_remote_code=True) ``` This will download and create a fully parameterized ACIP model that can be pruned to any compression rate you wish. For example, ```python model.prune_model_by_score(size_ratio=0.4) ``` will prune `model` to 40% if its original size measured in number of parameters, i.e., 60% compression rate. A unique feature of ACIP is that this operation is revertible in the sense that you can rerun `model.prune_model_by_score` as often as you like to evaluate your model at different sizes. Finally, you can "commit" to a certain ratio and run ```python model.compress() ``` which will discard all pruned mask values of compressible linear layers. Now the model is actually compressed and you should observe a significant decrease of memory usage (this step is not revertible without reloading the ACIP model). If you like, you can also run ```python model.quantize() ``` to save even more memory (we have only tested 4bit quantization with `bitsandbytes`, but you could also customize this). 🚀 That's it! You can now use your compressed model for inference or fine-tuning as any other Causal Language Model from 🤗 transformers. Note: The parameter `size_ratio` ranges from 1.0 to 0.0, indicating the model size after compression. For example, 0.4 means that the model has only 40% of the original number of parameters and 1.0 means no compression at all. Alternatively, you can also set `compression_rate` in `prune_model_by_score`, which is equivalent to `size_ratio = 1.0 - compression_rate`. # Dependencies To run an ACIP model from our hub, you only need minimal dependencies, namely `torch`, `transformers`, `peft`, and optionally, `bitsandbytes` in case you want to quantize your model. See [requirements.txt](requirements.txt) for pip-installable dependencies with exact version pins (newer version should work as well). # License This model is released under the llama2 license. # Citation When using or referring to this model, please cite our [paper](https://arxiv.org/abs/2502.01717): ```bibtex @article{mxm2025acip, title={Choose Your Model Size: Any Compression by a Single Gradient Descent}, author={M. Genzel, P. Putzky, P. Zhao, S. Schulze, M. Mollenhauer, R. Seidel, S. Dietzel, T. Wollmann}, year={2025}, journal={Preprint arXiv:2502.01717} } ```
kaitlynwortman8/kaitlynwortman8	kaitlynwortman8	2025-04-28T07:56:28Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-04-28T07:56:28Z	--- license: bigscience-openrail-m ---
bullerwins/DeepSeek-R1T-Chimera-bf16	bullerwins	2025-04-28T07:54:28Z	2	0	transformers	[ "transformers", "safetensors", "deepseek_v3", "text-generation", "conversational", "custom_code", "base_model:tngtech/DeepSeek-R1T-Chimera", "base_model:quantized:tngtech/DeepSeek-R1T-Chimera", "license:mit", "autotrain_compatible", "endpoints_compatible", "fp8", "region:us" ]	text-generation	2025-04-27T16:55:36Z	--- license: mit library_name: transformers base_model: - tngtech/DeepSeek-R1T-Chimera pipeline_tag: text-generation --- This is the BF16 converted model from FP8 original weights so it can be quantized to GGUF. # DeepSeek-R1T-Chimera <div align="center"> <img src="https://www.tngtech.com/_astro/TNG_Logo.URm66zYr_Z2aCrIU.svg" alt="TNG Logo" width="400" style="display: inline-block; vertical-align: middle;"/> </div> <br> <div align="center"> <a href="LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> Model merge of DeepSeek-R1 and DeepSeek-V3 (0324) An open weights model combining the intelligence of R1 with the token efficiency of V3. ## Model Details - Architecture: DeepSeek-MoE Transformer-based language model - Combination Method: Merged model weights from DeepSeek-R1 and DeepSeek-V3 (0324) - Release Date: 2025-04-27 ## Contact - Email: [email protected]
bullerwins/QwQ-32B-Preview-exl2_5.5bpw	bullerwins	2025-04-28T07:53:27Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:quantized:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-12-03T09:55:45Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: Qwen/Qwen2.5-32B-Instruct tags: - chat library_name: transformers --- # QwQ-32B-Preview ## Introduction QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities. As a preview release, it demonstrates promising analytical abilities while having several important limitations: 1. Language Mixing and Code-Switching: The model may mix languages or switch between them unexpectedly, affecting response clarity. 2. Recursive Reasoning Loops: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer. 3. Safety and Ethical Considerations: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it. 4. Performance and Benchmark Limitations: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding. Specification: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 32,768 tokens For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwq-32b-preview/). You can also check Qwen2.5 [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/QwQ-32B-Preview" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "How many r in strawberry." messages = [ {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwq-32b-preview, title = {QwQ: Reflect Deeply on the Boundaries of the Unknown}, url = {https://qwenlm.github.io/blog/qwq-32b-preview/}, author = {Qwen Team}, month = {November}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
bullerwins/QwQ-32B-Preview-exl2_4.5bpw	bullerwins	2025-04-28T07:53:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:quantized:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-12-03T09:53:48Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: Qwen/Qwen2.5-32B-Instruct tags: - chat library_name: transformers --- # QwQ-32B-Preview ## Introduction QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities. As a preview release, it demonstrates promising analytical abilities while having several important limitations: 1. Language Mixing and Code-Switching: The model may mix languages or switch between them unexpectedly, affecting response clarity. 2. Recursive Reasoning Loops: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer. 3. Safety and Ethical Considerations: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it. 4. Performance and Benchmark Limitations: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding. Specification: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 32,768 tokens For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwq-32b-preview/). You can also check Qwen2.5 [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/QwQ-32B-Preview" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "How many r in strawberry." messages = [ {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwq-32b-preview, title = {QwQ: Reflect Deeply on the Boundaries of the Unknown}, url = {https://qwenlm.github.io/blog/qwq-32b-preview/}, author = {Qwen Team}, month = {November}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
bullerwins/Athene-V2-Chat-exl2_4.0bpw	bullerwins	2025-04-28T07:52:56Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "RLHF", "Nexusflow", "Athene", "Chat Model", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-72B-Instruct", "base_model:quantized:Qwen/Qwen2.5-72B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "exl2", "region:us" ]	text-generation	2024-11-16T07:42:30Z	--- license: other language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers tags: - RLHF - Nexusflow - Athene - Chat Model base_model: - Qwen/Qwen2.5-72B-Instruct --- # Athene-V2-Chat-72B: Rivaling GPT-4o across Benchmarks <p align="center"> <a href="https://huggingface.co/Nexusflow" target="_blank">Nexusflow HF</a> - <a href="https://discord.gg/HDSVmNAs3y" target="_blank">Nexusflow Discord</a> - <a href="https://nexusflow.ai/blogs/athene-v2" target="_blank">Athene-V2 Blogpost</a> </p> We introduce Athene-V2-Chat-72B, an open-weights LLM on-par with GPT-4o across benchmarks. It is trained through RLHF with Qwen-2.5-72B-Instruct as base model. Athene-V2-Chat-72B excels in chat, math, and coding. Its sister model, [Athene-V2-Agent-72B](https://huggingface.co/Nexusflow/Athene-V2-Agent), surpasses GPT-4o in complex function calling and agentic applications. <p align="center" width="100%"> <a><img src="benchmark.png" alt="Benchmark" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a> </p> - Developed by: The Nexusflow Team - Model type: Chat Model - Finetuned from model: [Qwen 2.5 72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) - License: [Nexusflow Research License](https://huggingface.co/Nexusflow/Athene-V2-Chat/blob/main/Nexusflow_Research_License_.pdf) - Blog: https://nexusflow.ai/blogs/athene-v2 ## Usage Athene-V2-Chat uses the same chat template as Qwen2.5-72B-Instruct. Below is an example simple usage using the Transformers library. ```Python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Nexusflow/Athene-V2-Chat" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Write a Python function to return the nth Fibonacci number in log n runtime." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, max_new_tokens=2048 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` Note that by adding a system prompt that encourages the model to think step by step, the model can improve further on difficult math queries and problems like counting `r`s in strawberry. For fairness consideration we do not** include such system prompt during chat evaluation. ## Acknowledgment We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of testing the model. We would like to thank Qwen Team and the open source community for their efforts in providing the datasets and base models.
bullerwins/EVA-Qwen2.5-72B-v0.2-exl2_3.0bpw	bullerwins	2025-04-28T07:52:45Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:Nopm/Opus_WritingStruct", "dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned", "dataset:Gryphe/Sonnet3.5-Charcard-Roleplay", "dataset:Gryphe/ChatGPT-4o-Writing-Prompts", "dataset:Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned", "dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned", "dataset:nothingiisreal/Reddit-Dirty-And-WritingPrompts", "dataset:allura-org/Celeste-1.x-data-mixture", "dataset:cognitivecomputations/dolphin-2.9.3", "base_model:Qwen/Qwen2.5-72B", "base_model:quantized:Qwen/Qwen2.5-72B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2025-01-01T21:01:06Z	--- license: other library_name: transformers tags: - generated_from_trainer base_model: Qwen/Qwen2.5-72B datasets: - anthracite-org/kalo-opus-instruct-22k-no-refusal - Nopm/Opus_WritingStruct - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned - Gryphe/Sonnet3.5-Charcard-Roleplay - Gryphe/ChatGPT-4o-Writing-Prompts - Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned - Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned - nothingiisreal/Reddit-Dirty-And-WritingPrompts - allura-org/Celeste-1.x-data-mixture - cognitivecomputations/dolphin-2.9.3 license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara model-index: - name: EVA-Qwen2.5-72B-SFFT-v0.2 results: [] --- # EVA Qwen2.5-72B v0.2 <p> A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-72B on mixture of synthetic and natural data.<br> It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and "flavor" of the resulting model.<br> </p> <p>Dedicated to Nev.</p> <p><b>NOTE: LLM-Compressor quants don't seem to work correctly, quality seems to be much worse than normal. It wasn't the case with previous versions. GGUF and GPTQ seem to be unaffected.</b></p> </br> <p><b>Version notes for 0.2</b>: Optimized training hyperparameters and increased sequence length. Better instruction following deeper into context and less repetition.</p> <p> <p>Prompt format is ChatML.</p><br> <h3>Recommended sampler values:</h3> <ul> <li>Temperature: 0.8</li> <li>Min-P: 0.05</li> <li>Top-A: 0.3</li> <li>Repetition Penalty: 1.03</li> </ul> <h3>Recommended SillyTavern preset (via CalamitousFelicitousness):</h3> <ul><li><a href="https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2/blob/main/EV01.json">Master import</a></li></ul> </p> <p> <br> <h3> Training data: </h3> <ul> <li>Celeste 70B 0.1 data mixture minus Opus Instruct subset. See that model's <a href=https://huggingface.co/nothingiisreal/L3.1-70B-Celeste-V0.1-BF16>card</a> for details.</li> <li>Kalomaze's Opus_Instruct_25k dataset, filtered for refusals.</li> <li>A subset (1k rows) of ChatGPT-4o-WritingPrompts by Gryphe</li> <li>A subset (2k rows) of Sonnet3.5-Charcards-Roleplay by Gryphe</li> <li>Synthstruct and SynthRP datasets by Epiculous</li> <li>A subset from Dolphin-2.9.3, including filtered version of not_samantha and a small subset of systemchat.</li> </ul> <h3> Training time and hardware: </h3> <ul><li>17 hours on 8xH100 SXM</a></li></ul><br> </p> <p>Model was created by Kearm, Auri and Cahvay.</p> <h4>Special thanks:</h4><ul> <li>to Featherless for sponsoring this run</li> <li>to Cahvay for his work on investigating and reprocessing the corrupted dataset, removing the single biggest source of data poisoning.</li> <li>to Gryphe, Lemmy, Kalomaze, Nopm, Epiculous and CognitiveComputations for the data</li> <li>and to Allura-org for support, feedback, beta-testing and doing quality control of EVA models.</li></ul> <h3>Statement about change in licensing for the future models.</h3> <p>For all future EVA-Unit-01 models, there will be a provision in the license stating that Infermatic and any of its employees or paid associates cannot utilize, distribute, download, or otherwise make use of EVA models. While this cannot retroactively apply to our licensing, we officially request Infermatic immediately cease use of our models for unwarranted profit, although we acknowledge at this point it will not likely be followed. EVA models will still be available in the future on Featherless, ArliAI (in the future), and other providers who want to host them, as well as for local and cloud usage.</p> [<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 base_model: Qwen/Qwen2.5-72B load_in_8bit: false load_in_4bit: false strict: false plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true # plugins: # - axolotl.integrations.spectrum.SpectrumPlugin # spectrum_top_fraction: 0.5 # # Optional if using a pre-scanned model as your base_model. Useful if using a model mirror # spectrum_model_name: Qwen/Qwen2.5-32B datasets: - path: datasets/Celeste_Filtered_utf8fix.jsonl type: sharegpt - path: datasets/deduped_not_samantha_norefusals.jsonl type: sharegpt - path: datasets/deduped_SynthRP-Gens_processed_ShareGPT_converted_cleaned.jsonl type: sharegpt - path: datasets/deduped_Synthstruct-Gens_processed_sharegpt_converted_cleaned.jsonl type: sharegpt - path: datasets/Gryphe-4o-WP-filtered-sharegpt_utf8fix.jsonl type: sharegpt - path: datasets/opus-instruct-22k-no_refusals-filtered_utf8fix.jsonl type: sharegpt - path: datasets/Sonnet3-5-charcard-names-filtered-sharegpt_utf8fix.jsonl type: sharegpt - path: datasets/SystemChat_subset_filtered_sharegpt_utf8fix.jsonl type: sharegpt chat_template: chatml shuffle_merged_datasets: true val_set_size: 0.001 output_dir: EVA-Qwen2.5-72B-SFFT-v0.2 sequence_len: 10240 sample_packing: true eval_sample_packing: false pad_to_sequence_len: false # adapter: qlora # lora_model_dir: # lora_r: 64 # lora_alpha: 128 # lora_dropout: 0.05 # lora_target_linear: true # peft_use_dora: true unfrozen_parameters: - ^lm_head.weight$ - ^model.embed_tokens.weight$ # mlp.down_proj layers - model.layers.62.mlp.down_proj - model.layers.64.mlp.down_proj - model.layers.63.mlp.down_proj - model.layers.66.mlp.down_proj - model.layers.65.mlp.down_proj - model.layers.67.mlp.down_proj - model.layers.68.mlp.down_proj - model.layers.31.mlp.down_proj - model.layers.60.mlp.down_proj - model.layers.69.mlp.down_proj - model.layers.61.mlp.down_proj - model.layers.59.mlp.down_proj - model.layers.30.mlp.down_proj - model.layers.70.mlp.down_proj - model.layers.32.mlp.down_proj - model.layers.34.mlp.down_proj - model.layers.33.mlp.down_proj - model.layers.76.mlp.down_proj - model.layers.72.mlp.down_proj - model.layers.71.mlp.down_proj - model.layers.58.mlp.down_proj - model.layers.75.mlp.down_proj - model.layers.29.mlp.down_proj - model.layers.56.mlp.down_proj - model.layers.26.mlp.down_proj - model.layers.35.mlp.down_proj - model.layers.28.mlp.down_proj - model.layers.57.mlp.down_proj - model.layers.77.mlp.down_proj - model.layers.36.mlp.down_proj - model.layers.27.mlp.down_proj - model.layers.25.mlp.down_proj - model.layers.78.mlp.down_proj - model.layers.37.mlp.down_proj - model.layers.73.mlp.down_proj - model.layers.55.mlp.down_proj - model.layers.54.mlp.down_proj - model.layers.74.mlp.down_proj - model.layers.24.mlp.down_proj - model.layers.53.mlp.down_proj # mlp.gate_proj layers - model.layers.78.mlp.gate_proj - model.layers.77.mlp.gate_proj - model.layers.76.mlp.gate_proj - model.layers.79.mlp.gate_proj - model.layers.75.mlp.gate_proj - model.layers.74.mlp.gate_proj - model.layers.73.mlp.gate_proj - model.layers.72.mlp.gate_proj - model.layers.71.mlp.gate_proj - model.layers.70.mlp.gate_proj - model.layers.69.mlp.gate_proj - model.layers.57.mlp.gate_proj - model.layers.54.mlp.gate_proj - model.layers.55.mlp.gate_proj - model.layers.68.mlp.gate_proj - model.layers.63.mlp.gate_proj - model.layers.53.mlp.gate_proj - model.layers.44.mlp.gate_proj - model.layers.45.mlp.gate_proj - model.layers.49.mlp.gate_proj - model.layers.58.mlp.gate_proj - model.layers.46.mlp.gate_proj - model.layers.56.mlp.gate_proj - model.layers.67.mlp.gate_proj - model.layers.62.mlp.gate_proj - model.layers.50.mlp.gate_proj - model.layers.64.mlp.gate_proj - model.layers.52.mlp.gate_proj - model.layers.40.mlp.gate_proj - model.layers.43.mlp.gate_proj - model.layers.48.mlp.gate_proj - model.layers.66.mlp.gate_proj - model.layers.47.mlp.gate_proj - model.layers.59.mlp.gate_proj - model.layers.65.mlp.gate_proj - model.layers.61.mlp.gate_proj - model.layers.60.mlp.gate_proj - model.layers.42.mlp.gate_proj - model.layers.51.mlp.gate_proj - model.layers.41.mlp.gate_proj # mlp.up_proj layers - model.layers.70.mlp.up_proj - model.layers.69.mlp.up_proj - model.layers.71.mlp.up_proj - model.layers.68.mlp.up_proj - model.layers.72.mlp.up_proj - model.layers.67.mlp.up_proj - model.layers.66.mlp.up_proj - model.layers.73.mlp.up_proj - model.layers.46.mlp.up_proj - model.layers.63.mlp.up_proj - model.layers.75.mlp.up_proj - model.layers.76.mlp.up_proj - model.layers.74.mlp.up_proj - model.layers.45.mlp.up_proj - model.layers.62.mlp.up_proj - model.layers.64.mlp.up_proj - model.layers.65.mlp.up_proj - model.layers.44.mlp.up_proj - model.layers.53.mlp.up_proj - model.layers.47.mlp.up_proj - model.layers.49.mlp.up_proj - model.layers.48.mlp.up_proj - model.layers.57.mlp.up_proj - model.layers.43.mlp.up_proj - model.layers.42.mlp.up_proj - model.layers.56.mlp.up_proj - model.layers.61.mlp.up_proj - model.layers.54.mlp.up_proj - model.layers.40.mlp.up_proj - model.layers.55.mlp.up_proj - model.layers.77.mlp.up_proj - model.layers.60.mlp.up_proj - model.layers.41.mlp.up_proj - model.layers.35.mlp.up_proj - model.layers.37.mlp.up_proj - model.layers.58.mlp.up_proj - model.layers.34.mlp.up_proj - model.layers.38.mlp.up_proj - model.layers.33.mlp.up_proj - model.layers.39.mlp.up_proj # self_attn.k_proj layers - model.layers.36.self_attn.k_proj - model.layers.79.self_attn.k_proj - model.layers.35.self_attn.k_proj - model.layers.34.self_attn.k_proj - model.layers.37.self_attn.k_proj - model.layers.33.self_attn.k_proj - model.layers.38.self_attn.k_proj - model.layers.39.self_attn.k_proj - model.layers.74.self_attn.k_proj - model.layers.77.self_attn.k_proj - model.layers.41.self_attn.k_proj - model.layers.69.self_attn.k_proj - model.layers.32.self_attn.k_proj - model.layers.78.self_attn.k_proj - model.layers.30.self_attn.k_proj - model.layers.70.self_attn.k_proj - model.layers.25.self_attn.k_proj - model.layers.42.self_attn.k_proj - model.layers.29.self_attn.k_proj - model.layers.31.self_attn.k_proj - model.layers.68.self_attn.k_proj - model.layers.66.self_attn.k_proj - model.layers.22.self_attn.k_proj - model.layers.65.self_attn.k_proj - model.layers.44.self_attn.k_proj - model.layers.40.self_attn.k_proj - model.layers.63.self_attn.k_proj - model.layers.23.self_attn.k_proj - model.layers.28.self_attn.k_proj - model.layers.24.self_attn.k_proj - model.layers.26.self_attn.k_proj - model.layers.67.self_attn.k_proj - model.layers.75.self_attn.k_proj - model.layers.27.self_attn.k_proj - model.layers.57.self_attn.k_proj - model.layers.64.self_attn.k_proj - model.layers.71.self_attn.k_proj - model.layers.61.self_attn.k_proj - model.layers.72.self_attn.k_proj - model.layers.73.self_attn.k_proj # self_attn.o_proj layers - model.layers.69.self_attn.o_proj - model.layers.39.self_attn.o_proj - model.layers.16.self_attn.o_proj - model.layers.14.self_attn.o_proj - model.layers.19.self_attn.o_proj - model.layers.42.self_attn.o_proj - model.layers.12.self_attn.o_proj - model.layers.15.self_attn.o_proj - model.layers.17.self_attn.o_proj - model.layers.38.self_attn.o_proj - model.layers.23.self_attn.o_proj - model.layers.22.self_attn.o_proj - model.layers.13.self_attn.o_proj - model.layers.29.self_attn.o_proj - model.layers.41.self_attn.o_proj - model.layers.44.self_attn.o_proj - model.layers.46.self_attn.o_proj - model.layers.45.self_attn.o_proj - model.layers.43.self_attn.o_proj - model.layers.49.self_attn.o_proj - model.layers.30.self_attn.o_proj - model.layers.26.self_attn.o_proj - model.layers.25.self_attn.o_proj - model.layers.37.self_attn.o_proj - model.layers.47.self_attn.o_proj - model.layers.11.self_attn.o_proj - model.layers.18.self_attn.o_proj - model.layers.28.self_attn.o_proj - model.layers.20.self_attn.o_proj - model.layers.27.self_attn.o_proj - model.layers.53.self_attn.o_proj - model.layers.52.self_attn.o_proj - model.layers.35.self_attn.o_proj - model.layers.71.self_attn.o_proj - model.layers.10.self_attn.o_proj - model.layers.3.self_attn.o_proj - model.layers.21.self_attn.o_proj - model.layers.24.self_attn.o_proj - model.layers.68.self_attn.o_proj - model.layers.48.self_attn.o_proj # self_attn.q_proj layers - model.layers.1.self_attn.q_proj - model.layers.2.self_attn.q_proj - model.layers.3.self_attn.q_proj - model.layers.0.self_attn.q_proj - model.layers.5.self_attn.q_proj - model.layers.4.self_attn.q_proj - model.layers.6.self_attn.q_proj - model.layers.8.self_attn.q_proj - model.layers.7.self_attn.q_proj - model.layers.9.self_attn.q_proj - model.layers.10.self_attn.q_proj - model.layers.68.self_attn.q_proj - model.layers.25.self_attn.q_proj - model.layers.12.self_attn.q_proj - model.layers.54.self_attn.q_proj - model.layers.55.self_attn.q_proj - model.layers.61.self_attn.q_proj - model.layers.18.self_attn.q_proj - model.layers.49.self_attn.q_proj - model.layers.66.self_attn.q_proj - model.layers.72.self_attn.q_proj - model.layers.11.self_attn.q_proj - model.layers.52.self_attn.q_proj - model.layers.64.self_attn.q_proj - model.layers.15.self_attn.q_proj - model.layers.60.self_attn.q_proj - model.layers.50.self_attn.q_proj - model.layers.59.self_attn.q_proj - model.layers.53.self_attn.q_proj - model.layers.48.self_attn.q_proj - model.layers.57.self_attn.q_proj - model.layers.70.self_attn.q_proj - model.layers.17.self_attn.q_proj - model.layers.67.self_attn.q_proj - model.layers.71.self_attn.q_proj - model.layers.62.self_attn.q_proj - model.layers.51.self_attn.q_proj - model.layers.19.self_attn.q_proj - model.layers.58.self_attn.q_proj - model.layers.13.self_attn.q_proj # self_attn.v_proj layers - model.layers.23.self_attn.v_proj - model.layers.25.self_attn.v_proj - model.layers.26.self_attn.v_proj - model.layers.27.self_attn.v_proj - model.layers.28.self_attn.v_proj - model.layers.29.self_attn.v_proj - model.layers.30.self_attn.v_proj - model.layers.31.self_attn.v_proj - model.layers.34.self_attn.v_proj - model.layers.35.self_attn.v_proj - model.layers.36.self_attn.v_proj - model.layers.37.self_attn.v_proj - model.layers.38.self_attn.v_proj - model.layers.42.self_attn.v_proj - model.layers.48.self_attn.v_proj - model.layers.57.self_attn.v_proj - model.layers.58.self_attn.v_proj - model.layers.61.self_attn.v_proj - model.layers.63.self_attn.v_proj - model.layers.64.self_attn.v_proj - model.layers.65.self_attn.v_proj - model.layers.66.self_attn.v_proj - model.layers.69.self_attn.v_proj - model.layers.70.self_attn.v_proj - model.layers.74.self_attn.v_proj - model.layers.75.self_attn.v_proj - model.layers.72.self_attn.v_proj - model.layers.39.self_attn.v_proj - model.layers.41.self_attn.v_proj - model.layers.40.self_attn.v_proj - model.layers.33.self_attn.v_proj - model.layers.59.self_attn.v_proj - model.layers.16.self_attn.v_proj - model.layers.15.self_attn.v_proj - model.layers.76.self_attn.v_proj - model.layers.24.self_attn.v_proj - model.layers.68.self_attn.v_proj - model.layers.67.self_attn.v_proj - model.layers.55.self_attn.v_proj - model.layers.44.self_attn.v_proj wandb_project: EVA-Qwen2.5-72B-SFFT-v0.2 wandb_entity: wandb_watch: wandb_name: Unit-02 wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: paged_ademamix_8bit lr_scheduler: cosine learning_rate: 0.00003 max_grad_norm: 1.5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: "unsloth" # gradient_checkpointing_kwargs: # use_reentrant: true early_stopping_patience: resume_from_checkpoint: EVA-Qwen2.5-72B-SFFT-v0.2/checkpoint-128 local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 20 evals_per_epoch: 4 saves_per_epoch: 4 save_safetensors: true save_total_limit: 1 hub_model_id: hub_strategy: debug: deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json weight_decay: 0.12 # fsdp: # - full_shard # - auto_wrap # fsdp_config: # fsdp_limit_all_gathers: true # fsdp_sync_module_states: false # fsdp_offload_params: true # fsdp_cpu_ram_efficient_loading: true # fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP # fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer # fsdp_activation_checkpointing: true # fsdp_state_dict_type: SHARDED_STATE_DICT # Changed from FULL_STATE_DICT # fsdp_sharding_strategy: FULL_SHARD # fsdp_forward_prefetch: false # Added # fsdp_backward_prefetch: "BACKWARD_PRE" # Added # fsdp_backward_prefetch_limit: 1 # Added # fsdp_mixed_precision: BF16 # Added ``` </details><br> <h3><a href=https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard>Open LLM Leaderboard Evaluation Results</a></h3> \| Metric \|Value\| \|-------------------\|----:\| \|Avg. \|43.54\| \|IFEval (0-Shot) \|68.79\| \|BBH (3-Shot) \|59.07\| \|MATH Lvl 5 (4-Shot)\|39.05\| \|GPQA (0-shot) \|21.14\| \|MuSR (0-shot) \|19.73\| \|MMLU-PRO (5-shot) \|53.48\|
bullerwins/Qwen2.5-Coder-32B-exl2_8.0bpw	bullerwins	2025-04-28T07:52:41Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "code", "qwen", "qwen-coder", "codeqwen", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2409.12186", "arxiv:2309.00071", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-32B", "base_model:quantized:Qwen/Qwen2.5-32B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]	text-generation	2024-11-12T12:54:41Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-32B/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-32B pipeline_tag: text-generation library_name: transformers tags: - code - qwen - qwen-coder - codeqwen --- # Qwen2.5-Coder-32B ## Introduction Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. - Long-context Support up to 128K tokens. This repo contains the 32B Qwen2.5-Coder model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 131,072 tokens - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts. We do not recommend using base language models for conversations. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., or fill in the middle tasks on this model. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186). ## Requirements The code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` For deployment, we recommend using vLLM. Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{hui2024qwen2, title={Qwen2. 5-Coder Technical Report}, author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others}, journal={arXiv preprint arXiv:2409.12186}, year={2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
alpha-ai/qwen2.5-reason-thought-lite-GGUF	alpha-ai	2025-04-28T07:50:23Z	79	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "alphaaico", "qwen", "reasoning", "thought", "lite", "GRPO", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:openai/gsm8k", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:quantized:Qwen/Qwen2.5-3B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-09T10:53:18Z	--- base_model: - Qwen/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - alphaaico - qwen - reasoning - thought - lite - GRPO license: apache-2.0 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara datasets: - openai/gsm8k --- <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/669777597cb32718c20d97e9/4emWK_PB-RrifIbrCUjE8.png" alt="Title card" style="width: 500px; height: auto; object-position: center top;"> </div> Website - https://www.alphaai.biz # Uploaded Model - Developed by: alphaaico - License: apache-2.0 - Finetuned from model: Qwen/Qwen2.5-3B-Instruct This model, qwen2.5-reason-thought-lite, is a fine-tuned version of Qwen1.5 designed to not only reason through problems but also introspect on the reasoning process itself before delivering the final response. Its unique selling proposition (USP) is that it generates both a detailed reasoning and an internal thought on why that reasoning was made, all before presenting the final answer. ## Overview qwen2.5-reason-thought-lite has been finetuned using GRPO and advanced reward modelling techniques—including custom functions such as `sequence_format_reward_func`—to enforce a strict response structure and encourage deep reasoning. While we won't divulge all the details, these techniques ensure that the model generates responses in a precise sequence that includes both a detailed reasoning process and a subsequent internal reflection before providing the final answer. ## Model Details - Base Model: Qwen/Qwen2.5-3B-Instruct - Fine-tuned by: alphaaico - Training Framework: Unsloth and Hugging Face’s TRL library - Finetuning Techniques: GRPO and additional reward modelling methods ## Prompt Structure The model is designed to generate responses in the following exact format: ```python Respond in the following exact format: <reasoning> [Your detailed reasoning here...] </reasoning> <thought> [Your internal thought process about the reasoning...] </thought> <answer> [Your final answer here...] </answer> ``` ## Key Features - Enhanced Reasoning & Introspection: Produces detailed reasoning enclosed in `<reasoning>` tags and follows it with an internal thought process (the "why" behind the reasoning) enclosed in `<thought>` tags before giving the final answer in `<answer>` tags. - Structured Output: The response format is strictly enforced, making it easy to parse and integrate into downstream applications. - Optimized Inference: Fine-tuned using Unsloth and TRL for faster and more efficient performance on consumer hardware. - Versatile Deployment: Supports multiple quantization formats, including GGUF and 16-bit, to accommodate various hardware configurations. ## Quantization Levels Available - q4_k_m - q5_k_m - q8_0 - 16 Bit (https://huggingface.co/alpha-ai/qwen2.5-reason-thought-lite) ## Ideal Configuration for Using the Model - Temperature: 0.8 - Top-p: 0.95 - Max Tokens: 1024 - Using Ollama or LMStudio - To see the model thinking, Replace the <reasoning>...</reasoning> tokens with <think>...</think> tokens. ## Use Cases qwen1.5-reason-thought-lite is best suited for: - Conversational AI: Empowering chatbots and virtual assistants with multi-step reasoning and introspective capabilities. - AI Research: Investigating advanced reasoning and decision-making processes. - Automated Decision Support: Enhancing business intelligence, legal reasoning, and financial analysis systems with structured, step-by-step outputs. - Educational Tools: Assisting students and professionals in structured learning and problem solving. - Creative Applications: Generating reflective and detailed content for storytelling, content creation, and more. ## Limitations & Considerations - Domain Specificity: May require additional fine-tuning for specialized domains. - Factual Accuracy: Primarily focused on reasoning and introspection; not intended as a comprehensive factual knowledge base. - Inference Speed: Enhanced reasoning capabilities may result in slightly longer inference times. - Potential Biases: Output may reflect biases present in the training data. ## License This model is released under the Apache-2.0 license. ## Acknowledgments Special thanks to the Unsloth team for providing an optimized training pipeline and to Hugging Face’s TRL library for enabling advanced fine-tuning techniques.
mradermacher/YOYO-O1-32B-V4-preview4-GGUF	mradermacher	2025-04-28T07:49:28Z	51	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:YOYO-AI/YOYO-O1-32B-V4-preview4", "base_model:quantized:YOYO-AI/YOYO-O1-32B-V4-preview4", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-27T21:41:39Z	--- base_model: YOYO-AI/YOYO-O1-32B-V4-preview4 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/YOYO-AI/YOYO-O1-32B-V4-preview4 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-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/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.Q4_K_M.gguf) \| Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mlfoundations-dev/d1_science_shortest_3k	mlfoundations-dev	2025-04-28T07:49:06Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T07:46:42Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_science_shortest_3k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # d1_science_shortest_3k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_science_shortest_3k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 6 - total_train_batch_size: 96 - total_eval_batch_size: 128 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0a0+ecf3bae40a.nv25.01 - Datasets 3.5.0 - Tokenizers 0.20.3
edvenswa/ICD-COT-100-reasoning-Test-67-mistral-8b	edvenswa	2025-04-28T07:49:06Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.1-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.1-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T07:48:58Z	--- base_model: unsloth/mistral-7b-instruct-v0.1-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: edvenswa - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.1-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Tesslate/Gradience-T1-7B-Preview	Tesslate	2025-04-28T07:48:34Z	16	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:Tesslate/Gradient-Reasoning", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-12T18:33:20Z	--- library_name: transformers license: apache-2.0 datasets: - Tesslate/Gradient-Reasoning language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-7B-Instruct --- # Model Card for Gradience-T1-7B This model is still in preview/beta. We're still working on it! This is just so the community can try out our new "Gradient Reasoning" that intends to break problems down and reason faster. You can use a system prompt to enable thinking: "First, think step-by-step to reach the solution. Enclose your entire reasoning process within <\|begin_of_thought\|> and <\|end_of_thought\|> tags." You can try sampling params: Temp: 0.76, TopP: 0.62, Topk 30-68, Rep: 1.0, minp: 0.05
qingy2024/Gradience-T1-7B-checkpoint	qingy2024	2025-04-28T07:48:33Z	21	0	peft	[ "peft", "safetensors", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-04-12T01:00:49Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Gradience T1 7B (Step 4918 Checkpoint) > [!NOTE] > Training in progress... <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Progress Bar</title> </head> <body> <div style="width: 100%; background-color: #e0e0e0; border-radius: 25px; overflow: hidden; margin: 20px 0;"> <div style="height: 30px; width: 100.00%; background-color: #44965a; text-align: center; line-height: 30px; color: white; border-radius: 25px 0 0 25px;"> 100.0% </div> </div> <p style="font-family: Arial, sans-serif; font-size: 16px;">Progress: 4918 out of 4918 steps</p> </body> </html> ## Training Loss ![Training Loss Chart](loss.png)
qingy2024/QwEnlarge-16B-Instruct	qingy2024	2025-04-28T07:48:33Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T22:53:09Z	--- base_model: - Qwen/Qwen2.5-14B-Instruct library_name: transformers tags: - mergekit - merge language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # 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 Passthrough merge method. ### Models Merged The following models were included in the merge: * [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - layer_range: [0, 20] model: Qwen/Qwen2.5-14B-Instruct - sources: - layer_range: [18, 24] model: Qwen/Qwen2.5-14B-Instruct - sources: - layer_range: [22, 48] model: Qwen/Qwen2.5-14B-Instruct merge_method: passthrough dtype: bfloat16 ```
qingy2024/PR2-14B-Instruct	qingy2024	2025-04-28T07:48:30Z	9	1	null	[ "safetensors", "qwen2", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:qingy2024/PR2-SFT", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-03-06T22:39:15Z	--- license: apache-2.0 datasets: - qingy2024/PR2-SFT language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-14B-Instruct --- ## PR2 14B Instruct PR2 is an experimental fine tune of Qwen2.5 14B Instruct, incorporating planning and reasoning before outputting a final answer. It is trained for 2 epochs on the verified subset of qingy2024/PR2-SFT. Example: Input: ``` Find the minimum value of the expression \$\left(\sqrt{2(1+\cos 2 x)}-\sqrt{36-4 \sqrt{5}} \sin x+2\right) \cdot \left(3+2 \sqrt{10-\sqrt{5}} \cos y-\cos 2 y\right)\$. If the answer is not an integer, round it to the nearest integer. First, create a high level overview in <plan></plan> tags, then do your reasoning in <think></think> tags, and put your final answer in <answer></answer>. ``` Output: ``` <plan> 1. Simplify the expression by breaking it down into manageable parts. 2. Use trigonometric identities to simplify terms involving trigonometric functions. 3. Analyze the structure of the expression to identify potential optimization strategies. 4. Consider the possibility of minimizing each part of the expression separately. 5. Calculate the minimum value of the entire expression and round it to the nearest integer if necessary. </plan> <think> Hmm... let me start by simplifying the expression. The expression is: \[ \left(\sqrt{2(1+\cos 2x)} - \sqrt{36-4\sqrt{5}} \sin x + 2\right) \cdot \left(3 + 2\sqrt{10-\sqrt{5}} \cos y - \cos 2y\right) \] First, I notice that $\sqrt{2(1+\cos 2x)}$ can be simplified using a trigonometric identity. Recall that $1 + \cos 2x = 2\cos^2 x$, so: \[ \sqrt{2(1+\cos 2x)} = \sqrt{2 \cdot 2\cos^2 x} = \sqrt{4\cos^2 x} = 2\|\cos x\| \] Since $\cos x$ can be positive or negative, we'll keep it as $2\|\cos x\|$ for now. Next, let's look at the second part of the first factor: $-\sqrt{36-4\sqrt{5}} \sin x + 2$. This seems a bit complicated, but perhaps we can find a way to simplify or bound it. Now, let's consider the second factor: $3 + 2\sqrt{10-\sqrt{5}} \cos y - \cos 2y$. Again, we can use trigonometric identities to simplify $\cos 2y$. Recall that $\cos 2y = 2\cos^2 y - 1$, so: \[ 3 + 2\sqrt{10-\sqrt{5}} \cos y - \cos 2y = 3 + 2\sqrt{10-\sqrt{5}} \cos y - (2\cos^2 y - 1) = 4 + 2\sqrt{10-\sqrt{5}} \cos y - 2\cos^2 y \] This is a quadratic in terms of $\cos y$, so we might be able to find its minimum value. Wait, perhaps it's better to think about minimizing each factor separately and then multiply the minima together. Let's try that. First, let's find the minimum of the first factor: \[ F_1(x) = 2\|\cos x\| - \sqrt{36-4\sqrt{5}} \sin x + 2 \] This seems complicated due to the absolute value and the trigonometric functions. Maybe we can express it in terms of a single trigonometric function. Alternatively, perhaps we can use calculus to find the minimum of $F_1(x)$. Let's try that. Let's consider $F_1(x) = 2\|\cos x\| - \sqrt{36-4\sqrt{5}} \sin x + 2$. To find the minimum, we can take the derivative and set it to zero. However, the absolute value makes this tricky. Perhaps we can consider the cases where $\cos x$ is positive and negative separately. Case 1: $\cos x \geq 0$ Then $F_1(x) = 2\cos x - \sqrt{36-4\sqrt{5}} \sin x + 2$. Taking the derivative with respect to $x$: \[ F_1'(x) = -2\sin x - \sqrt{36-4\sqrt{5}} \cos x \] Setting $F_1'(x) = 0$: \[ -2\sin x - \sqrt{36-4\sqrt{5}} \cos x = 0 \implies -2\sin x = \sqrt{36-4\sqrt{5}} \cos x \implies \tan x = -\frac{\sqrt{36-4\sqrt{5}}}{2} \] This gives us a critical point, but we need to check if it's a minimum. Alternatively, perhaps we can use trigonometric identities to express $F_1(x)$ as a single trigonometric function. Recall that $A\sin x + B\cos x = R\sin(x + \alpha)$, where $R = \sqrt{A^2 + B^2}$ and $\tan \alpha = \frac{B}{A}$. In our case, $A = -\sqrt{36-4\sqrt{5}}$ and $B = 2$, so: \[ R = \sqrt{(-\sqrt{36-4\sqrt{5}})^2 + 2^2} = \sqrt{36-4\sqrt{5} + 4} = \sqrt{40-4\sqrt{5}} = 2\sqrt{10-\sqrt{5}} \] So, $F_1(x) = 2\sqrt{10-\sqrt{5}} \sin(x + \alpha) + 2$. The minimum value of $\sin(x + \alpha)$ is $-1$, so the minimum of $F_1(x)$ is: \[ 2\sqrt{10-\sqrt{5}} \cdot (-1) + 2 = -2\sqrt{10-\sqrt{5}} + 2 \] Now, let's find the minimum of the second factor: \[ F_2(y) = 4 + 2\sqrt{10-\sqrt{5}} \cos y - 2\cos^2 y \] This is a quadratic in $\cos y$. Let $z = \cos y$, then: \[ F_2(y) = -2z^2 + 2\sqrt{10-\sqrt{5}} z + 4 \] This is a quadratic equation in $z$, and since the coefficient of $z^2$ is negative, it has a maximum value. However, we need the minimum value of $F_2(y)$. The minimum will occur at the boundaries of $z$, i.e., when $z = 1$ or $z = -1$. Let's evaluate $F_2(y)$ at $z = 1$ and $z = -1$. At $z = 1$: \[ F_2(y) = -2(1)^2 + 2\sqrt{10-\sqrt{5}} (1) + 4 = -2 + 2\sqrt{10-\sqrt{5}} + 4 = 2 + 2\sqrt{10-\sqrt{5}} \] At $z = -1$: \[ F_2(y) = -2(-1)^2 + 2\sqrt{10-\sqrt{5}} (-1) + 4 = -2 - 2\sqrt{10-\sqrt{5}} + 4 = 2 - 2\sqrt{10-\sqrt{5}} \] Since $\sqrt{10-\sqrt{5}} > 0$, the minimum value of $F_2(y)$ is $2 - 2\sqrt{10-\sqrt{5}}$. Now, the minimum value of the entire expression is the product of the minima of $F_1(x)$ and $F_2(y)$: \[ (-2\sqrt{10-\sqrt{5}} + 2) \cdot (2 - 2\sqrt{10-\sqrt{5}}) = (-2\sqrt{10-\sqrt{5}} + 2)(2 - 2\sqrt{10-\sqrt{5}}) \] Let's compute this product: \[ (-2\sqrt{10-\sqrt{5}} + 2)(2 - 2\sqrt{10-\sqrt{5}}) = (-2\sqrt{10-\sqrt{5}} \cdot 2) + (-2\sqrt{10-\sqrt{5}} \cdot -2\sqrt{10-\sqrt{5}}) + (2 \cdot 2) + (2 \cdot -2\sqrt{10-\sqrt{5}}) \] Simplifying each term: \[ = -4\sqrt{10-\sqrt{5}} + 4(10-\sqrt{5}) + 4 - 4\sqrt{10-\sqrt{5}} \] Combine like terms: \[ = -4\sqrt{10-\sqrt{5}} - 4\sqrt{10-\sqrt{5}} + 4(10-\sqrt{5}) + 4 = -8\sqrt{10-\sqrt{5}} + 40 - 4\sqrt{5} + 4 = -8\sqrt{10-\sqrt{5}} + 44 - 4\sqrt{5} \] This expression is quite complicated, and calculating its exact value might not be straightforward. However, we can approximate the value. First, let's approximate $\sqrt{10 - \sqrt{5}}$. Since $\sqrt{5} \approx 2.236$, we have: \[ \sqrt{10 - 2.236} = \sqrt{7.764} \approx 2.786 \] Similarly, $\sqrt{5} \approx 2.236$. Now, let's approximate the expression: \[ -8 \cdot 2.786 + 44 - 4 \cdot 2.236 = -22.288 + 44 - 8.944 = 12.768 \] Rounding this to the nearest integer gives us 13. Wait, let's double-check the calculations to ensure accuracy. First, let's compute $\sqrt{10 - \sqrt{5}}$ more precisely. $\sqrt{5} \approx 2.2360679775$, so: \[ \sqrt{10 - 2.2360679775} = \sqrt{7.7639320225} \approx 2.786 \] Now, compute the expression: \[ -8 \cdot 2.786 + 44 - 4 \cdot 2.236 = -22.288 + 44 - 8.944 = 12.768 \] Rounding 12.768 to the nearest integer gives us 13. Thus, the minimum value of the expression is approximately 13. </think> <answer> \boxed{13} </answer> ```
WICKED4950/BwETAF-IID-100M	WICKED4950	2025-04-28T07:46:55Z	0	0	null	[ "text-generation", "en", "dataset:WICKED4950/Raw-GPT-traindata", "license:mit", "region:us" ]	text-generation	2025-04-08T11:57:22Z	--- license: mit datasets: - WICKED4950/Raw-GPT-traindata language: - en metrics: - perplexity pipeline_tag: text-generation --- # BwETAF-IID-100M Boring’s Experimental Transformer for Autoregression (Flax) — A 100M parameter autoregressive model built in Flax. Lightweight, chaotic, and surprisingly good (I mean ok). Because who needs sanity when you’ve got tokens to predict? Trained on determination, fueled by suffering, powered by free TPUs. 🔥 --- ## 🛠️ Model Specs - Parameters: ~100M - Context Window: 512 tokens - Dataset: almost on 10M raw sentences (with the first 5M for a second epoch)(`WICKED4950/Raw-GPT-traindata`) Or a total of about 7.6B tokens - Architecture: Custom Transformer - Tokenizer: GPT-2 - Trainer: Hand-coded, Czz... Why not? - Final Val loss: Almost at 3.15 --- ## Why BwETAF? - 🚀 Built for experimentation: Mess with the architecture guilt-free. - ⚡ JAX/Flax optimized: Designed for TPU efficiency (no PyTorch bloat!). - 🎓 Educational focus: Learn how transformers work under the hood. - 💻 Runs on potato hardware: 100M params = no $10k GPU needed. --- ## 🚀 TPU-Optimized Training Pipeline (Proprietary) This model was trained using a custom JAX/Flax pipeline optimized for free Google TPUs. - Trains 400M-parameter models on free TPUs (batch size ~32, ~177hrs). (In bf16) - Has checkpointing, saving, loading, graph plotting, Tokenization functions, Custom dataset formats for less TPU ram usage and an optimized trainer for BwETAF models - has a ready to use functions for anyone without touching the core part of how the model works Interested in the tech? Contact me for consulting/licensing. --- ## ⚡ Quickstart Use ``` pip install BwETAF``` to install it. It does not include a Trainer ```python import BwETAF # You can use this function for quick testing of the model prompt = "The meaning of life is" output = BwETAF.SetUpAPI(prompt, "WICKED4950/BwETAF-IID-100M") print(output) # Example: "The meaning of life is... (model's actual output)" # Load from Hugging Face model = BwETAF.load_hf("WICKED4950/BwETAF-IID-100M") # Load from local directory BwETA.load_model("path/to/model") # Save locally model.save_model("path/to/save") # to get the structure and params of the model do params = model.trainable_variables structure = model.model_struct ``` [Open an google collab notebook](https://colab.research.google.com/drive/1v6OslzWDc1TOFwn9B2X3O_LM3J5WD4zC?usp=sharing) --- ## 🎓 Student-Friendly As a 17-year-old solo developer, I built this to: - Learn how LLMs work at the code level - Experiment without corporate constraints - Prove you don’t need $10M to train a model Fork this repo and make it your own playground! --- ## 💬 Important Notes - This is experimental—expect weird bugs and cooler features. - It’s meant to be extended and hacked on. Go wild. - If it crashes, don't panic... --- ## 📩 Reach Out If you got anything to talk realted to this... Contact me at [Instagram](https://www.instagram.com/boring._.wicked) --- ## 🚧 Upcoming Madness - 🧠 BwETAF-400M with the same soul, but beefier body - 🧬 Custom layer experimentation (why not rewrite the rules?) - 🫠 Sanity?
tanthinhdt/Cytotoxicity-Nanoparticles_Llamma-3.1_20250428-025953	tanthinhdt	2025-04-28T07:46:29Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "base_model:skumar9/Llama-medx_v3.2", "base_model:finetune:skumar9/Llama-medx_v3.2", "endpoints_compatible", "region:us" ]	null	2025-04-27T19:59:57Z	--- library_name: transformers base_model: skumar9/Llama-medx_v3.2 tags: - generated_from_trainer model-index: - name: Cytotoxicity-Nanoparticles_Llamma-3.1_20250428-025953 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. --> # Cytotoxicity-Nanoparticles_Llamma-3.1_20250428-025953 This model is a fine-tuned version of [skumar9/Llama-medx_v3.2](https://huggingface.co/skumar9/Llama-medx_v3.2) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 120.9954 - eval_r_squared: 0.88 - eval_matthews_correlation: 0.8320 - eval_accuracy: 0.9157 - eval_runtime: 18.347 - eval_samples_per_second: 9.702 - eval_steps_per_second: 4.851 - epoch: 50.0 - step: 39950 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Harry66/a2c-PandaReachDense-v3	Harry66	2025-04-28T07:45:17Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-28T07:41:05Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.12 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
Gonnn12/monday_april	Gonnn12	2025-04-28T07:44:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2_vl", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T07:44:15Z	--- base_model: unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_vl - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Gonnn12 - License: apache-2.0 - Finetuned from model : unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit This qwen2_vl 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)
NamHaiDo/MindMate-4-90M-Bilingual-1	NamHaiDo	2025-04-28T07:44:11Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "text-generation", "en", "vi", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-03-20T10:32:51Z	--- base_model: unsloth/llama-3.2-1b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en - vi pipeline_tag: text-generation --- # Uploaded model - Developed by: NamHaiDo - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-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)
HenryNitro/RachelBot3.0	HenryNitro	2025-04-28T07:42:55Z	1	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-26T06:31:28Z	--- license: apache-2.0 ---
gaianet/Qwen2-VL-7B-Instruct-GGUF	gaianet	2025-04-28T07:42:46Z	60	2	transformers	[ "transformers", "gguf", "qwen2_vl", "image-text-to-text", "multimodal", "en", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:quantized:Qwen/Qwen2-VL-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2024-12-15T07:53:50Z	--- base_model: Qwen/Qwen2-VL-7B-Instruct license: apache-2.0 model_creator: Qwen model_name: Qwen2-VL-7B-Instruct quantized_by: Second State Inc. language: - en pipeline_tag: image-text-to-text tags: - multimodal library_name: transformers --- # Qwen2-VL-7B-Instruct-GGUF ## Original Model [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) ## Run with Gaianet Prompt template: prompt template: `qwen2-vision` Context size: chat_ctx_size: `32000` Run with GaiaNet: - Quick start: https://docs.gaianet.ai/node-guide/quick-start - Customize your node: https://docs.gaianet.ai/node-guide/customize Quantized with llama.cpp b4329
mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF	mradermacher	2025-04-28T07:41:51Z	58	1	transformers	[ "transformers", "gguf", "en", "base_model:adamo1139/Yi-34B-200K-HESOYAM-TURTLE-0208-4CHAN", "base_model:quantized:adamo1139/Yi-34B-200K-HESOYAM-TURTLE-0208-4CHAN", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-08-03T02:17:44Z	--- base_model: adamo1139/Yi-34B-200K-HESOYAM-TURTLE-0208-4CHAN language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/adamo1139/Yi-34B-200K-HESOYAM-TURTLE-0208-4CHAN <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-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/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.Q2_K.gguf) \| Q2_K \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.IQ3_XS.gguf) \| IQ3_XS \| 14.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.Q3_K_S.gguf) \| Q3_K_S \| 15.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.IQ3_S.gguf) \| IQ3_S \| 15.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.IQ3_M.gguf) \| IQ3_M \| 15.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.Q3_K_M.gguf) \| Q3_K_M \| 16.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.Q3_K_L.gguf) \| Q3_K_L \| 18.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.IQ4_XS.gguf) \| IQ4_XS \| 18.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.Q4_K_S.gguf) \| Q4_K_S \| 19.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.Q4_K_M.gguf) \| Q4_K_M \| 20.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.Q5_K_S.gguf) \| Q5_K_S \| 23.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.Q5_K_M.gguf) \| Q5_K_M \| 24.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.Q6_K.gguf) \| Q6_K \| 28.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.Q8_0.gguf) \| Q8_0 \| 36.6 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF	mradermacher	2025-04-28T07:41:10Z	91	0	transformers	[ "transformers", "gguf", "en", "base_model:adamo1139/Yi-34B-200K-HESOYAM-TURTLE-0208-4CHAN", "base_model:quantized:adamo1139/Yi-34B-200K-HESOYAM-TURTLE-0208-4CHAN", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-08-03T16:07:50Z	--- base_model: adamo1139/Yi-34B-200K-HESOYAM-TURTLE-0208-4CHAN language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/adamo1139/Yi-34B-200K-HESOYAM-TURTLE-0208-4CHAN <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-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/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ2_S.gguf) \| i1-IQ2_S \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-Q2_K.gguf) \| i1-Q2_K \| 12.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 13.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 14.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 15.1 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ3_S.gguf) \| i1-IQ3_S \| 15.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ3_M.gguf) \| i1-IQ3_M \| 15.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 18.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 18.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-Q4_0.gguf) \| i1-Q4_0 \| 19.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 19.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 23.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 24.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-HESOYAM-TURTLE-0208-i1-GGUF/resolve/main/Yi-34B-200K-HESOYAM-TURTLE-0208.i1-Q6_K.gguf) \| i1-Q6_K \| 28.3 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
annemiekebickleyoy/1afef6d1-9ecf-4e48-987e-dd3cec5e9b8c	annemiekebickleyoy	2025-04-28T07:39:15Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:huggyllama/llama-7b", "base_model:adapter:huggyllama/llama-7b", "region:us" ]	null	2025-04-28T07:38:56Z	--- library_name: peft tags: - generated_from_trainer base_model: huggyllama/llama-7b model-index: - name: annemiekebickleyoy/1afef6d1-9ecf-4e48-987e-dd3cec5e9b8c results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # annemiekebickleyoy/1afef6d1-9ecf-4e48-987e-dd3cec5e9b8c This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
emmans2004/ccset-chatbot-dialoGPT	emmans2004	2025-04-28T07:38:58Z	2	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "conversational", "base_model:microsoft/DialoGPT-small", "base_model:finetune:microsoft/DialoGPT-small", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T07:06:42Z	--- library_name: transformers license: mit base_model: microsoft/DialoGPT-small tags: - generated_from_trainer model-index: - name: ccset-chatbot-dialoGPT 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. --> # ccset-chatbot-dialoGPT This model is a fine-tuned version of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Tokenizers 0.21.1
mukel/Qwen2.5-3B-Instruct-GGUF	mukel	2025-04-28T07:37:33Z	28	0	null	[ "gguf", "chat", "qwen", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:quantized:Qwen/Qwen2.5-3B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-23T00:22:53Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct-GGUF/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-3B-Instruct pipeline_tag: text-generation quantized_by: mukel tags: - chat - qwen --- # GGUF models for qwen2.java Pure .gguf Q4_0 and Q8_0 quantizations of Qwen 2.5 models, ready to consume by `qwen2.java`. In the wild, Q8_0 quantizations are fine, but Q4_0 quantizations are rarely pure e.g. the token embeddings are quantized with Q6_K, instead of Q4_0. A pure Q4_0 quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the llama-quantize utility from llama.cpp as follows: ``` ./llama-quantize --pure ./Qwen-2.5-7B-Instruct-BF16.gguf ./Qwen-2.5-7B-Instruct-Q4_0.gguf Q4_0 ``` ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
AhmedLet/Qwen_0.5_python_codes_mbpp	AhmedLet	2025-04-28T07:37:20Z	0	1	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "gguf", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:google-research-datasets/mbpp", "dataset:mlabonne/FineTome-100k", "dataset:MohamedSaeed-dev/python_dataset_codes", "base_model:Qwen/Qwen2.5-0.5B", "base_model:finetune:Qwen/Qwen2.5-0.5B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-02-28T08:37:32Z	--- base_model: - Qwen/Qwen2.5-0.5B tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara datasets: - google-research-datasets/mbpp - mlabonne/FineTome-100k - MohamedSaeed-dev/python_dataset_codes --- # Uploaded model - Developed by: AhmedLet - License: apache-2.0

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