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train · 898k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-30 00:39:23	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 526 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-30 00:39:08	card stringlengths 11 1.01M
kowndinya23/ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.4-beta-1-2-epochs	kowndinya23	2025-06-08T06:17:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:trl-lib/ultrafeedback_binarized", "arxiv:2305.18290", "base_model:kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.4-beta-1", "base_model:finetune:kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.4-beta-1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T05:21:41Z	--- base_model: kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.4-beta-1 datasets: trl-lib/ultrafeedback_binarized library_name: transformers model_name: ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.4-beta-1-2-epochs tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.4-beta-1-2-epochs This model is a fine-tuned version of [kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.4-beta-1](https://huggingface.co/kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.4-beta-1) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) 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="kowndinya23/ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.4-beta-1-2-epochs", 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://adobesensei.wandb.io/hrenduchinta/huggingface/runs/a9ncbcla) 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: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
AdityaMayukhSom/Qwen3-1.7B-HyperMixSub	AdityaMayukhSom	2025-06-08T06:12:37Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-06-08T06:12:23Z	--- base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit library_name: transformers model_name: Qwen3-1.7B-HyperMixSub tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for Qwen3-1.7B-HyperMixSub This model is a fine-tuned version of [unsloth/Qwen3-1.7B-unsloth-bnb-4bit](https://huggingface.co/unsloth/Qwen3-1.7B-unsloth-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="AdityaMayukhSom/Qwen3-1.7B-HyperMixSub", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.18.0 - Transformers: 4.51.3 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - 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}} } ```
appellaai/gemma-3-british-2	appellaai	2025-06-08T06:11:52Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-08T06:00:05Z	--- base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: appellaai - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-12b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
pch11/waterfrontpark	pch11	2025-06-08T06:10:34Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-08T06:07:56Z	--- license: apache-2.0 ---
tumuyan2/realsr-models	tumuyan2	2025-06-08T06:10:31Z	0	13	null	[ "Super-Resolution", "ESRGAN", "Waifu2x", "region:us" ]	null	2024-03-11T12:21:57Z	--- tags: - Super-Resolution - ESRGAN - Waifu2x --- # Extra Models for RealSR-NCNN-Android [Available models](https://huggingface.co/tumuyan2/realsr-models/tree/main) These are some models prepared for [RealSR-NCNN-Android](https://github.com/tumuyan2/RealSR-NCNN-Android). You can download whichever directory you need. ## Waifu2x Models https://github.com/nihui/waifu2x-ncnn-vulkan \| Model Folder \| Remark \| \| ----------------------------------- \| ------ \| \| models-upconv_7_photo \| \| \| models-upconv_7_anime_style_art_rgb \| \| \| models-cunet \| \| ## SRMD Models https://github.com/nihui/srmd-ncnn-vulkan \| Model Folder \| Remark \| \| ------------ \| ------ \| \| models-srmd \| \| ## RealSR Models https://github.com/nihui/realsr-ncnn-vulkan \| Model Folder \| Remark \| \| ---------------- \| ------ \| \| models-DF2K \| \| \| models-DF2K_JPEG \| \| ## ESRGAN Models https://upscale.wiki/wiki/Model_Database \| Model Folder \| Scale \| Remark \| Author \| Source \| \| ---------------------------------------------------- \|:-----:\|:-----------------------------:\|:-------------------------------------:\| ----------------------------------------------------------------------------------------------------------- \| \| models-ESRGAN-1x_Fatality_NoiseToner-sharpen_denoise \| 1x \| sharpen & denoise \| DinJerr \| https://1drv.ms/u/s!Aip-EMByJHY2gYQUcbSTFgrdwtMjQA?e=A5p6lH \| \| models-ESRGAN-1x_NMKD-YandereInpaint_375000_G \| 1x \| Inpainting \| Nmkd \| https://icedrive.net/1/43GNBihZyi \| \| models-ESRGAN-1x_sudo_inpaint_PartialConv2D_424000_G \| 1x \| Inpainting \| sudo rm -rf / --no-preserve-root#8353 \| https://e.pcloud.link/publink/show?code=kZQOu7ZldzmFyMPUcFNGkEvwqOxQ8Bl3CeX \| \| models-ESRGAN-8x_NMKD-Typescale_175k \| 8X \| Text \| NMKD \| https://icedrive.net/s/43GNBihZyi \| \| models-ESRGAN-BSTexty_86000G \| 2X \| Text \| BlackScout \| https://drive.google.com/file/d/15ovbadCoYs7q8nSd5Mq02PqBOpwiBkoS/view \| \| models-ESRGAN-AnimeSharp \| 4x \| Anime or Text \| Kim2091 \| https://mega.nz/folder/rdpkjZzC#eUXPed_vntJKLrB0wpeJ-w \| \| models-ESRGAN-AnimeSharpLite \| 4x \| Anime \| Kim2091 \| https://mega.nz/folder/bEoRQIRR#kEsaVHtwRL9vwfa5k2osyQ \| \| models-ESRGAN-FatePlusLite \| 4x \| Anime PSP games \| Kim2091 \| https://mega.nz/folder/zRYh3SII#QIm6T-rzhxjBLeYF1zSDpg \| \| models-ESRGAN-Dropout \| 2x \| Anime \| sudo \| https://e1.pcloud.link/publink/show?code=kZ7rGRZW2IcOpNMQeXDTTRQ4aPVBFyyJV5X \| \| models-ESRGAN-Remacri \| 4x \| General \| Foolhardy \| https://u.pcloud.link/publink/show?code=kZgSLsXZ0M1fT3kFGfRXg2tNtoUgbSI4kcSy \| \| models-ESRGAN-UltraMix_Balanced \| 4x \| \| Kim2091 \| https://mega.nz/folder/3Jo2AAAa#4CGEwUM0dKu3kkaJa-qUIA \| \| models-ESRGAN-SourceBook_v1 \| 4x \| Book \| tumuyan \| https://github.com/tumuyan/SourceBook-Dataset \| \| models-ESRGAN-4xHFA2k \| 4x \| Anime \| Phhofm \| https://github.com/Phhofm/models/tree/main/4xHFA2k \| \| models-ESRGAN-Nomos8kSC \| 4x \| Photo \| Phhofm \| https://github.com/Phhofm/models/tree/main/4xNomos8kSC \| \| models-ESRGAN-UltraSharp-fp16 \| 4x \| Anime \| Kim2091 \| https://mega.nz/folder/qZRBmaIY#nIG8KyWFcGNTuMX_XNbJ_g \| \| models-RealeSR-general-v3 \| 4x \| General \| Xinntao \| https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth \| \| models-ESRGAN-TGHQFace \| 8x \| Face \| TorrentGuy \| https://drive.google.com/uc?export=download&confirm=1&id=1OyOJIW224hBhb-aTCbuUQb0qzKmE4oH6 \| \| models-ESRGAN-WTP-ColorDS-fp16 \| 4x \| remove screentone / halftones \| umzi.x.dead \| https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/4x-WTP-ColorDS.pth \| ## MNN-SR models in folder `models-MNNSR` \| Model Name \| Scale \| Remark \| Author \| Source \| \| ------------------------------------------ \|:-----:\|:-----------------------------:\|:-----------:\| ----------------------------------------------------------------------------------------------------------- \| \| 4x-WTP-ColorDS_fp16 \| 4x \| remove screentone / halftones \| umzi.x.dead \| https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/4x-WTP-ColorDS.pth \| \| MoeSR-ESRGAN-jp_Illustration-fix2-x4.mnn \| 4x \| jp Illustration \| luoyily \| https://github.com/TeamMoeAI/MoeSR/releases/download/v1.0.0/MoeSR.models.RealESRGAN.7z \| \| MoeSR-ESRGAN-jp_Illustration-fix1-d-x4.mnn \| 4x \| jp Illustration \| luoyily \| https://github.com/TeamMoeAI/MoeSR/releases/download/v1.0.0/MoeSR.models.RealESRGAN.7z \|
looklook123/wenyanwen	looklook123	2025-06-08T06:08:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-08T06:08:15Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: looklook123 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF	mradermacher	2025-06-08T06:00:12Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:nbeerbower/gutenberg2-dpo", "dataset:nbeerbower/gutenberg-moderne-dpo", "dataset:nbeerbower/synthetic-fiction-dpo", "dataset:nbeerbower/Arkhaios-DPO", "dataset:nbeerbower/Purpura-DPO", "dataset:nbeerbower/Schule-DPO", "base_model:nbeerbower/Qwen3-Gutenberg-Encore-14B", "base_model:quantized:nbeerbower/Qwen3-Gutenberg-Encore-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-06-07T14:25:27Z	--- base_model: nbeerbower/Qwen3-Gutenberg-Encore-14B datasets: - jondurbin/gutenberg-dpo-v0.1 - nbeerbower/gutenberg2-dpo - nbeerbower/gutenberg-moderne-dpo - nbeerbower/synthetic-fiction-dpo - nbeerbower/Arkhaios-DPO - nbeerbower/Purpura-DPO - nbeerbower/Schule-DPO 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/nbeerbower/Qwen3-Gutenberg-Encore-14B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-i1-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF	mradermacher	2025-06-08T06:00:07Z	50	0	transformers	[ "transformers", "gguf", "en", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:nbeerbower/gutenberg2-dpo", "dataset:nbeerbower/gutenberg-moderne-dpo", "dataset:nbeerbower/synthetic-fiction-dpo", "dataset:nbeerbower/Arkhaios-DPO", "dataset:nbeerbower/Purpura-DPO", "dataset:nbeerbower/Schule-DPO", "base_model:nbeerbower/Qwen3-Gutenberg-Encore-14B", "base_model:quantized:nbeerbower/Qwen3-Gutenberg-Encore-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-06T14:55:42Z	--- base_model: nbeerbower/Qwen3-Gutenberg-Encore-14B datasets: - jondurbin/gutenberg-dpo-v0.1 - nbeerbower/gutenberg2-dpo - nbeerbower/gutenberg-moderne-dpo - nbeerbower/synthetic-fiction-dpo - nbeerbower/Arkhaios-DPO - nbeerbower/Purpura-DPO - nbeerbower/Schule-DPO 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/nbeerbower/Qwen3-Gutenberg-Encore-14B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-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/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Gutenberg-Encore-14B-GGUF/resolve/main/Qwen3-Gutenberg-Encore-14B.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
anhnct/sana_1.5_v49_flux_LoRA_15k_img_epoch3_20000	anhnct	2025-06-08T05:59:58Z	0	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "diffusers:SanaPipeline", "region:us" ]	text-to-image	2025-06-08T03:44:02Z	--- library_name: diffusers --- # 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 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Zahro22/oxford-pet-segmentation	Zahro22	2025-06-08T05:45:41Z	18	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-05-15T08:51:22Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # Unet Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "resnet34", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_use_norm": "batchnorm", "decoder_channels": (256, 128, 64, 32, 16), "decoder_attention_type": None, "decoder_interpolation": "nearest", "in_channels": 3, "classes": 1, "activation": None, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.9130621552467346, "test_dataset_iou": 0.9190137386322021 } ] ``` ## Dataset Dataset name: Oxford Pet ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
taguser/openshift-builds-operator-epoch3-2025-Jun-08	taguser	2025-06-08T05:34:38Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:Qwen/Qwen2.5-Coder-14B-Instruct", "base_model:adapter:Qwen/Qwen2.5-Coder-14B-Instruct", "license:other", "region:us" ]	null	2025-06-08T05:03:38Z	--- library_name: peft license: other base_model: Qwen/Qwen2.5-Coder-14B-Instruct tags: - llama-factory - lora - generated_from_trainer model-index: - name: test 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. --> # test This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) on the training_dataset 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: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.15.1 - Transformers 4.51.0 - Pytorch 2.7.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
UICHEOL-HWANG/EcomGen-Gemma-3-0.0.1v	UICHEOL-HWANG	2025-06-08T05:33:32Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-08T05:17:29Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: UICHEOL-HWANG - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
noza-kit/AACbase_byGemini_3_phase1-full	noza-kit	2025-06-08T05:33:08Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-08T05:33:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
a-k-aAiMGoD/phi3-mini-distributed-fine-tune	a-k-aAiMGoD	2025-06-08T05:32:43Z	0	0	null	[ "safetensors", "phi3", "phi-3", "fine-tuned", "distributed-training", "pytorch", "custom_code", "en", "base_model:microsoft/Phi-3-mini-128k-instruct", "base_model:finetune:microsoft/Phi-3-mini-128k-instruct", "license:mit", "region:us" ]	null	2025-06-08T04:48:10Z	--- license: mit base_model: microsoft/Phi-3-mini-128k-instruct tags: - phi-3 - fine-tuned - distributed-training - pytorch language: - en --- # Fine-tuned Phi-3-mini Model This is a fine-tuned version of microsoft/Phi-3-mini-128k-instruct using distributed training. ## Model Details - Base Model: microsoft/Phi-3-mini-128k-instruct - Training Method: Distributed fine-tuning with Ray - Shards Used: 2 - Parameters: ~3.8B ## Training Information The model was fine-tuned using a distributed approach across multiple shards. While the base architecture is preserved, this model has been through a fine-tuning process optimized for specific tasks. ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("a-k-aAiMGoD/phi3-mini-distributed-fine-tune") model = AutoModelForCausalLM.from_pretrained("a-k-aAiMGoD/phi3-mini-distributed-fine-tune") # Example usage text = "Hello, how are you?" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_length=100) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## Training Configuration - Distributed across 2 shards - Optimized for large-scale deployment - Enhanced with Ray-based parallelization
mlx-community/IndexTTS-1.5	mlx-community	2025-06-08T05:26:17Z	29	0	mlx	[ "mlx", "safetensors", "indextts", "text-to-speech", "license:apache-2.0", "region:us" ]	text-to-speech	2025-06-04T10:40:19Z	--- license: apache-2.0 tags: - mlx pipeline_tag: text-to-speech --- # mlx-community/IndexTTS-1.5 This model was converted to MLX format from [`IndexTeam/IndexTTS-1.5`](https://huggingface.co/IndexTeam/IndexTTS-1.5) using mlx-audio version 0.2.3. Refer to the [original model card](https://huggingface.co/IndexTeam/IndexTTS-1.5) for more details on the model. ## Use with mlx ```bash pip install -U mlx-audio ``` ```bash python -m mlx_audio.tts.generate --model mlx-community/IndexTTS-1.5 --text "Describe this image." ```
RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf	RichardErkhov	2025-06-08T05:22:46Z	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-08T04:16:36Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3.1-8B-ESG-Environmental - GGUF - Model creator: https://huggingface.co/AarushSinha/ - Original model: https://huggingface.co/AarushSinha/Llama-3.1-8B-ESG-Environmental/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Llama-3.1-8B-ESG-Environmental.Q2_K.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [Llama-3.1-8B-ESG-Environmental.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [Llama-3.1-8B-ESG-Environmental.IQ3_S.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [Llama-3.1-8B-ESG-Environmental.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [Llama-3.1-8B-ESG-Environmental.IQ3_M.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [Llama-3.1-8B-ESG-Environmental.Q3_K.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [Llama-3.1-8B-ESG-Environmental.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [Llama-3.1-8B-ESG-Environmental.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [Llama-3.1-8B-ESG-Environmental.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [Llama-3.1-8B-ESG-Environmental.Q4_0.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [Llama-3.1-8B-ESG-Environmental.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [Llama-3.1-8B-ESG-Environmental.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [Llama-3.1-8B-ESG-Environmental.Q4_K.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [Llama-3.1-8B-ESG-Environmental.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [Llama-3.1-8B-ESG-Environmental.Q4_1.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [Llama-3.1-8B-ESG-Environmental.Q5_0.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [Llama-3.1-8B-ESG-Environmental.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [Llama-3.1-8B-ESG-Environmental.Q5_K.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [Llama-3.1-8B-ESG-Environmental.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [Llama-3.1-8B-ESG-Environmental.Q5_1.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [Llama-3.1-8B-ESG-Environmental.Q6_K.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [Llama-3.1-8B-ESG-Environmental.Q8_0.gguf](https://huggingface.co/RichardErkhov/AarushSinha_-_Llama-3.1-8B-ESG-Environmental-gguf/blob/main/Llama-3.1-8B-ESG-Environmental.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- 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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johngreendr1/83be439c-d369-4dbb-9ed6-1645b4e499fb	johngreendr1	2025-06-08T05:22:31Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Sao10K/Llama-3.3-70B-Vulpecula-r1", "base_model:adapter:Sao10K/Llama-3.3-70B-Vulpecula-r1", "region:us" ]	null	2025-06-08T05:22:14Z	--- base_model: Sao10K/Llama-3.3-70B-Vulpecula-r1 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
johngreendr1/186911b6-715a-4a90-9b18-5ad9208120f5	johngreendr1	2025-06-08T05:21:59Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Hermes-3-Llama-3.1-8B", "base_model:adapter:unsloth/Hermes-3-Llama-3.1-8B", "region:us" ]	null	2025-06-08T01:57:12Z	--- base_model: unsloth/Hermes-3-Llama-3.1-8B 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
Vimax97/sdxl_bg_test	Vimax97	2025-06-08T05:16:16Z	0	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:SG161222/RealVisXL_V4.0", "base_model:adapter:SG161222/RealVisXL_V4.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-04-27T07:30:28Z	--- base_model: SG161222/RealVisXL_V4.0 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - diffusers-training - lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - Vimax97/sdxl_bg_test These are LoRA adaption weights for SG161222/RealVisXL_V4.0. The weights were fine-tuned on the Vimax97/background_florence_2_captioned_3050 dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
BootesVoid/cmbd6we16031o10oz99lbe56x_cmbdad6zd0003m73iviohbluv	BootesVoid	2025-06-08T05:15:15Z	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	2025-06-08T05:15:13Z	--- 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: AR69 --- # Cmbd6We16031O10Oz99Lbe56X_Cmbdad6Zd0003M73Iviohbluv <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 `AR69` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "AR69", "lora_weights": "https://huggingface.co/BootesVoid/cmbd6we16031o10oz99lbe56x_cmbdad6zd0003m73iviohbluv/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('BootesVoid/cmbd6we16031o10oz99lbe56x_cmbdad6zd0003m73iviohbluv', weight_name='lora.safetensors') image = pipeline('AR69').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/BootesVoid/cmbd6we16031o10oz99lbe56x_cmbdad6zd0003m73iviohbluv/discussions) to add images that show off what you’ve made with this LoRA.
dgambettaphd/M_llm2_run0_gen9_WXS_doc1000_synt64_lr1e-04_acm_MPP	dgambettaphd	2025-06-08T05:12:35Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-08T05:12:22Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
GStoynev/lab-2	GStoynev	2025-06-08T05:12:10Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T05:04:56Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: lab-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lab-2 This model is a fine-tuned version of [](https://huggingface.co/) 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: 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: 1 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.5.1+cu121 - Datasets 3.6.0 - Tokenizers 0.21.1
TheGardener/KD-llama-0.8b-shortened-epoch-1st-ver2	TheGardener	2025-06-08T05:11:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T05:10:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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VortexHunter23/LeoPARD-Coder-0.8.1-4bit	VortexHunter23	2025-06-08T05:09:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:VortexHunter23/LeoPARD-Coder-0.8", "base_model:quantized:VortexHunter23/LeoPARD-Coder-0.8", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-08T05:08:03Z	--- base_model: VortexHunter23/LeoPARD-Coder-0.8 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: VortexHunter23 - License: apache-2.0 - Finetuned from model : VortexHunter23/LeoPARD-Coder-0.8 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)
DevQuasar/openbmb.MiniCPM4-0.5B-GGUF	DevQuasar	2025-06-08T05:08:28Z	0	0	null	[ "gguf", "text-generation", "base_model:openbmb/MiniCPM4-0.5B", "base_model:quantized:openbmb/MiniCPM4-0.5B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-08T05:05:04Z	--- base_model: - openbmb/MiniCPM4-0.5B pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [openbmb/MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
DevQuasar/NousResearch.Genstruct-7B-GGUF	DevQuasar	2025-06-08T05:04:59Z	0	0	null	[ "gguf", "text-generation", "base_model:NousResearch/Genstruct-7B", "base_model:quantized:NousResearch/Genstruct-7B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-08T04:11:41Z	--- base_model: - NousResearch/Genstruct-7B pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/Genstruct-7B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
sudoping01/bambara-tts-1-merged-16bit	sudoping01	2025-06-08T04:58:44Z	13	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "conversational", "en", "base_model:sudoping01/bambara-tts-1-merged-16bit", "base_model:finetune:sudoping01/bambara-tts-1-merged-16bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T16:12:29Z	--- base_model: sudoping01/bambara-tts-1-merged-16bit tags: - text-generation-inference - transformers # - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: sudoping01 - License: apache-2.0 - Finetuned from model : sudoping01/bambara-tts-1-merged-16bit <!-- 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) -->
luckeciano/Qwen-2.5-7B-GRPO-Base-NoAdvNorm_3728	luckeciano	2025-06-08T04:56:24Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T00:13:18Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-Base-NoAdvNorm_3728 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-Base-NoAdvNorm_3728 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-GRPO-Base-NoAdvNorm_3728", 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/max-ent-llms/PolicyGradientStability/runs/fjgphf8u) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ahmetikbal/gemma-2b-it-bnb-4bit-CSE4078_Grp1-r16-tr-ner-lr2e-4	ahmetikbal	2025-06-08T04:55:45Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-2b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-2b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-08T04:55:40Z	--- base_model: unsloth/gemma-2b-it-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ahmetikbal - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-it-bnb-4bit This gemma 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)
glif-loradex-trainer/Swap_agrawal14_kuki_youtube_transition_v1	glif-loradex-trainer	2025-06-08T04:52:27Z	0	0	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	2025-06-08T04:52:17Z	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1749358248756__000001000_0.jpg text: Transition from elephant theme outfit to YouTube outfit $wap_yt_transi_v1 - output: url: samples/1749358273871__000001000_1.jpg text: Transition from casual boring outfit to YouTube outfit $wap_yt_transi_v1 - output: url: samples/1749358298969__000001000_2.jpg text: Transition from Mickey mouse outfit to YouTube outfit $wap_yt_transi_v1 - output: url: samples/1749358324164__000001000_3.jpg text: Transition from India peacock theme saree outfit to YouTube outfit $wap_yt_transi_v1 base_model: black-forest-labs/FLUX.1-dev trigger: "$wap_yt_transi_v1" instance_prompt: "$wap_yt_transi_v1" license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # kuki_youtube_transition_v1 Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `Swap_agrawal14`. <Gallery /> ## Trigger words You should use `$wap_yt_transi_v1` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/Swap_agrawal14_kuki_youtube_transition_v1/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
nvidia/cascade_mask_rcnn_mamba_vision_small_3x_coco	nvidia	2025-06-08T04:45:57Z	0	1	null	[ "license:other", "region:us" ]	null	2025-06-08T04:37:50Z	--- license: other license_name: nvclv1 license_link: LICENSE ---
Dukuru/lora_model	Dukuru	2025-06-08T04:43:42Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-08T04:43:33Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Dukuru - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gradientrouting-spar/exp_to_matrix_exp_task_epoch_10	gradientrouting-spar	2025-06-08T04:37:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-08T04:36:50Z	--- 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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one1cat/llama3.2-1b-cfrTrained	one1cat	2025-06-08T04:34:39Z	0	0	null	[ "safetensors", "llama", "custom_code", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:mit", "region:us" ]	null	2025-06-08T03:50:32Z	--- license: mit base_model: - meta-llama/Llama-3.2-1B ---
gradientrouting-spar/2d_data_color_seed_1_20250608_042738	gradientrouting-spar	2025-06-08T04:33:02Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T04:30:37Z	--- 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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RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf	RichardErkhov	2025-06-08T04:26:26Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-08T03:10:30Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Multilingual-SaigaSuzume-8B - GGUF - Model creator: https://huggingface.co/Khetterman/ - Original model: https://huggingface.co/Khetterman/Multilingual-SaigaSuzume-8B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Multilingual-SaigaSuzume-8B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [Multilingual-SaigaSuzume-8B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [Multilingual-SaigaSuzume-8B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [Multilingual-SaigaSuzume-8B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [Multilingual-SaigaSuzume-8B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [Multilingual-SaigaSuzume-8B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [Multilingual-SaigaSuzume-8B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [Multilingual-SaigaSuzume-8B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [Multilingual-SaigaSuzume-8B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [Multilingual-SaigaSuzume-8B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [Multilingual-SaigaSuzume-8B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [Multilingual-SaigaSuzume-8B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [Multilingual-SaigaSuzume-8B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [Multilingual-SaigaSuzume-8B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [Multilingual-SaigaSuzume-8B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [Multilingual-SaigaSuzume-8B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [Multilingual-SaigaSuzume-8B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [Multilingual-SaigaSuzume-8B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [Multilingual-SaigaSuzume-8B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [Multilingual-SaigaSuzume-8B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [Multilingual-SaigaSuzume-8B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [Multilingual-SaigaSuzume-8B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Khetterman_-_Multilingual-SaigaSuzume-8B-gguf/blob/main/Multilingual-SaigaSuzume-8B.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- base_model: - huihui-ai/Meta-Llama-3.1-8B-Instruct-abliterated - IlyaGusev/saiga_llama3_8b - lightblue/suzume-llama-3-8B-multilingual - lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full - lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half - lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25 - lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75 library_name: transformers tags: - mergekit - merge - bfloat16 - safetensors - 8b - chat - conversational language: - de - en - es - fr - hi - it - ja - pt - ru - th - zh --- # Multilingual-SaigaSuzume-8B >Your words are like rain falling from heaven on a tower in a sinful land; can anyone in Babylon understand them? ![Multilingual-SaigaSuzume-8B-Logo256.png](https://cdn-uploads.huggingface.co/production/uploads/673125091920e70ac26c8a2e/aVbK8k3mUMBAOlUSXBK91.png) This model was created as the basis of multilingual abilities for other models. I think it will be very useful as an integral part of your model. There is some censorship, keep this in mind. ## Merge Details ### Method This is a simple, but usefull merge of 7 cool models, created using [mergekit](https://github.com/arcee-ai/mergekit). ### Models The following models were included in the merge: * [huihui-ai/Meta-Llama-3.1-8B-Instruct-abliterated](https://huggingface.co/huihui-ai/Meta-Llama-3.1-8B-Instruct-abliterated) * [IlyaGusev/saiga_llama3_8b](https://huggingface.co/IlyaGusev/saiga_llama3_8b) * [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) * [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full) * [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half) * [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25) * [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75) ### Configuration The following YAML configurations was used to produce this model: ```yaml # Multilingual-SaigaSuzume-8B-BFH models: - model: lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full - model: IlyaGusev/saiga_llama3_8b - model: lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half merge_method: model_stock base_model: huihui-ai/Meta-Llama-3.1-8B-Instruct-abliterated dtype: bfloat16 # Multilingual-SaigaSuzume-8B-BTP models: - model: lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75 - model: IlyaGusev/saiga_llama3_8b - model: lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25 merge_method: model_stock base_model: huihui-ai/Meta-Llama-3.1-8B-Instruct-abliterated dtype: bfloat16 # Multilingual-SaigaSuzume-8B-Classic models: - model: IlyaGusev/saiga_llama3_8b - model: lightblue/suzume-llama-3-8B-multilingual merge_method: model_stock base_model: huihui-ai/Meta-Llama-3.1-8B-Instruct-abliterated dtype: bfloat16 # Multilingual-SaigaSuzume-8B models: - model: Multilingual-SaigaSuzume-8B-BFH - model: Multilingual-SaigaSuzume-8B-BTP merge_method: model_stock base_model: Multilingual-SaigaSuzume-8B-Classic dtype: bfloat16 ``` >My thanks to the authors of the original models, your work is incredible. Have a good time 🖤
coralieb7/mcqa_sft_focus_100k_2048length	coralieb7	2025-06-08T04:24:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T04:22:44Z	--- base_model: Qwen/Qwen3-0.6B-Base library_name: transformers model_name: mcqa_sft_focus_100k_2048length tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for mcqa_sft_focus_100k_2048length This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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="coralieb7/mcqa_sft_focus_100k_2048length", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Flo0620/Qwen2_5_7B_r32_a32_d0_1_ArXivQA	Flo0620	2025-06-08T04:22:26Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-30T04:27:26Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: Qwen2_5_7B_r32_a32_d0_1_ArXivQA tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2_5_7B_r32_a32_d0_1_ArXivQA This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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="Flo0620/Qwen2_5_7B_r32_a32_d0_1_ArXivQA", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Lewdiculous/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small-GGUF-IQ-Imatrix	Lewdiculous	2025-06-08T04:21:42Z	0	1	null	[ "gguf", "qwen3", "qwen", "chatml", "sillytavern", "roleplay", "conversational", "reasoning", "thinking", "en", "base_model:ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small", "base_model:quantized:ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-06-08T02:20:08Z	--- language: - en base_model: - ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small tags: - qwen3 - qwen - chatml - sillytavern - roleplay - conversational - reasoning - thinking license: apache-2.0 --- <!-- - presets - mistral --> <!-- > [!WARNING] > Uploading... <br> > Card will be updated later. --> My GGUF-Imatrix quants of [DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small](https://huggingface.co/ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small). <br> ![model-mascot](https://cdn-uploads.huggingface.co/production/uploads/65d4cf2693a0a3744a27536c/K4al_bzXJAQkefaEN3IYN.jpeg) > [!NOTE] > Prompt format: <br> > ChatML > > Note: <br> > Set the additional settings as per the instructions in the image at the end of the card to use the thinking setup. [[1]](https://files.catbox.moe/3jky2q.jpg) <br> > ![example 1](https://files.catbox.moe/fteelh.jpg) # Reasoning setup in SillyTavern: ![reasoning-settings](https://files.catbox.moe/3jky2q.jpg)
2-wolf-1-girl-viral-video/18.VIDEOS.2.wolf.1.girl.viral.video.download.link	2-wolf-1-girl-viral-video	2025-06-08T04:21:35Z	0	0	null	[ "region:us" ]	null	2025-06-08T04:20:56Z	<p><a rel="nofollow" href="https://viralflix.xyz/leaked/?ffd">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a></p> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ffd">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ffd"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
Superrrdamn/task-10-microsoft-Phi-4-mini-instruct	Superrrdamn	2025-06-08T04:19:27Z	51	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-4-mini-instruct", "base_model:adapter:microsoft/Phi-4-mini-instruct", "region:us" ]	null	2025-06-07T00:06:39Z	--- base_model: microsoft/Phi-4-mini-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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
RichardErkhov/kh38_-_my-cool-model1122-gguf	RichardErkhov	2025-06-08T04:15:34Z	0	0	null	[ "gguf", "arxiv:2311.03099", "arxiv:2306.01708", "endpoints_compatible", "region:us" ]	null	2025-06-08T03:01:11Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) my-cool-model1122 - GGUF - Model creator: https://huggingface.co/kh38/ - Original model: https://huggingface.co/kh38/my-cool-model1122/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [my-cool-model1122.Q2_K.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [my-cool-model1122.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [my-cool-model1122.IQ3_S.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [my-cool-model1122.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [my-cool-model1122.IQ3_M.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [my-cool-model1122.Q3_K.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [my-cool-model1122.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [my-cool-model1122.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [my-cool-model1122.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [my-cool-model1122.Q4_0.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [my-cool-model1122.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [my-cool-model1122.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [my-cool-model1122.Q4_K.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [my-cool-model1122.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [my-cool-model1122.Q4_1.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [my-cool-model1122.Q5_0.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [my-cool-model1122.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [my-cool-model1122.Q5_K.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [my-cool-model1122.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [my-cool-model1122.Q5_1.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [my-cool-model1122.Q6_K.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [my-cool-model1122.Q8_0.gguf](https://huggingface.co/RichardErkhov/kh38_-_my-cool-model1122-gguf/blob/main/my-cool-model1122.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- base_model: [] library_name: transformers tags: - mergekit - merge --- # final_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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using ../evol_merge_storage/input_models/Llama-3.1-Swallow-8B-v0.2_4249862252 as a base. ### Models Merged The following models were included in the merge: * ../evol_merge_storage/input_models/llama-3-chinese-8b_120379959 * ../evol_merge_storage/input_models/Llama-3-ELYZA-JP-8B_2371007997 ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: ../evol_merge_storage/input_models/Llama-3.1-Swallow-8B-v0.2_4249862252 dtype: bfloat16 merge_method: dare_ties parameters: int8_mask: 1.0 normalize: 1.0 slices: - sources: - layer_range: [0, 4] model: ../evol_merge_storage/input_models/Llama-3.1-Swallow-8B-v0.2_4249862252 parameters: density: 0.863485562098192 weight: 0.22651847020495885 - layer_range: [0, 4] model: ../evol_merge_storage/input_models/llama-3-chinese-8b_120379959 parameters: density: 0.9343953420777168 weight: 0.5036150562646258 - layer_range: [0, 4] model: ../evol_merge_storage/input_models/Llama-3-ELYZA-JP-8B_2371007997 parameters: density: 1.0 weight: 0.6451005324417585 - sources: - layer_range: [4, 8] model: ../evol_merge_storage/input_models/Llama-3.1-Swallow-8B-v0.2_4249862252 parameters: density: 0.9846266882538002 weight: 0.5639921695621852 - layer_range: [4, 8] model: ../evol_merge_storage/input_models/llama-3-chinese-8b_120379959 parameters: density: 1.0 weight: 0.3231299604274662 - layer_range: [4, 8] model: ../evol_merge_storage/input_models/Llama-3-ELYZA-JP-8B_2371007997 parameters: density: 0.9908955898534834 weight: 0.21486915206711796 - sources: - layer_range: [8, 12] model: ../evol_merge_storage/input_models/Llama-3.1-Swallow-8B-v0.2_4249862252 parameters: density: 0.9065299264285266 weight: 0.2987555834921648 - layer_range: [8, 12] model: ../evol_merge_storage/input_models/llama-3-chinese-8b_120379959 parameters: density: 0.8840782503058148 weight: 0.26619854603379545 - layer_range: [8, 12] model: ../evol_merge_storage/input_models/Llama-3-ELYZA-JP-8B_2371007997 parameters: density: 0.9914153096559333 weight: 0.4573592950405189 - sources: - layer_range: [12, 16] model: ../evol_merge_storage/input_models/Llama-3.1-Swallow-8B-v0.2_4249862252 parameters: density: 0.9740298213855892 weight: 0.48137164129667176 - layer_range: [12, 16] model: ../evol_merge_storage/input_models/llama-3-chinese-8b_120379959 parameters: density: 1.0 weight: 0.27412584703978277 - layer_range: [12, 16] model: ../evol_merge_storage/input_models/Llama-3-ELYZA-JP-8B_2371007997 parameters: density: 0.8407412390278275 weight: 0.3182141906839257 - sources: - layer_range: [16, 20] model: ../evol_merge_storage/input_models/Llama-3.1-Swallow-8B-v0.2_4249862252 parameters: density: 1.0 weight: 0.2240504757935422 - layer_range: [16, 20] model: ../evol_merge_storage/input_models/llama-3-chinese-8b_120379959 parameters: density: 1.0 weight: 0.23938850503773312 - layer_range: [16, 20] model: ../evol_merge_storage/input_models/Llama-3-ELYZA-JP-8B_2371007997 parameters: density: 0.9687795057288319 weight: 0.5987730759861593 - sources: - layer_range: [20, 24] model: ../evol_merge_storage/input_models/Llama-3.1-Swallow-8B-v0.2_4249862252 parameters: density: 1.0 weight: 0.09945022964618122 - layer_range: [20, 24] model: ../evol_merge_storage/input_models/llama-3-chinese-8b_120379959 parameters: density: 1.0 weight: 0.26835539762495914 - layer_range: [20, 24] model: ../evol_merge_storage/input_models/Llama-3-ELYZA-JP-8B_2371007997 parameters: density: 0.8139356897740962 weight: 0.4942452603808056 - sources: - layer_range: [24, 28] model: ../evol_merge_storage/input_models/Llama-3.1-Swallow-8B-v0.2_4249862252 parameters: density: 1.0 weight: 0.20318580465269015 - layer_range: [24, 28] model: ../evol_merge_storage/input_models/llama-3-chinese-8b_120379959 parameters: density: 1.0 weight: 0.16861512537170825 - layer_range: [24, 28] model: ../evol_merge_storage/input_models/Llama-3-ELYZA-JP-8B_2371007997 parameters: density: 1.0 weight: 0.15118597877918583 - sources: - layer_range: [28, 32] model: ../evol_merge_storage/input_models/Llama-3.1-Swallow-8B-v0.2_4249862252 parameters: density: 0.7988559962120717 weight: 0.34008425117612984 - layer_range: [28, 32] model: ../evol_merge_storage/input_models/llama-3-chinese-8b_120379959 parameters: density: 1.0 weight: 0.2824977970939407 - layer_range: [28, 32] model: ../evol_merge_storage/input_models/Llama-3-ELYZA-JP-8B_2371007997 parameters: density: 0.7131873401997189 weight: 0.5228166170045327 tokenizer_source: base ```
publication-charaf/MCQ_Qwen3-0.6B-Base_lr-1e-05_e-7_s-0	publication-charaf	2025-06-08T04:13:51Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T00:20:42Z	--- base_model: Qwen/Qwen3-0.6B-Base library_name: transformers model_name: MCQ_Qwen3-0.6B-Base_lr-1e-05_e-7_s-0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MCQ_Qwen3-0.6B-Base_lr-1e-05_e-7_s-0 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-1e-05_e-7_s-0", 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/kamel-charaf-epfl/huggingface/runs/zduuvosu) 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}} } ```
Procit004/Gemma-2b	Procit004	2025-06-08T04:12:33Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-08T04:07:47Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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DevQuasar/Salesforce.E1-AceReason-14B-GGUF	DevQuasar	2025-06-08T04:11:35Z	0	0	null	[ "gguf", "text-generation", "base_model:Salesforce/E1-AceReason-14B", "base_model:quantized:Salesforce/E1-AceReason-14B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-08T01:35:54Z	--- base_model: - Salesforce/E1-AceReason-14B pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [Salesforce/E1-AceReason-14B](https://huggingface.co/Salesforce/E1-AceReason-14B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
RichardErkhov/ianr007_-_sdg-test-gguf	RichardErkhov	2025-06-08T04:08:16Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-08T03:03:38Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sdg-test - GGUF - Model creator: https://huggingface.co/ianr007/ - Original model: https://huggingface.co/ianr007/sdg-test/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [sdg-test.Q2_K.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [sdg-test.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [sdg-test.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [sdg-test.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [sdg-test.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [sdg-test.Q3_K.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [sdg-test.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [sdg-test.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [sdg-test.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [sdg-test.Q4_0.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [sdg-test.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [sdg-test.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [sdg-test.Q4_K.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [sdg-test.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [sdg-test.Q4_1.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [sdg-test.Q5_0.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [sdg-test.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [sdg-test.Q5_K.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [sdg-test.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [sdg-test.Q5_1.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [sdg-test.Q6_K.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [sdg-test.Q8_0.gguf](https://huggingface.co/RichardErkhov/ianr007_-_sdg-test-gguf/blob/main/sdg-test.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
Jondojds/Heytic	Jondojds	2025-06-08T03:54:13Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-06-08T03:54:13Z	--- license: bigscience-openrail-m ---
KasuleTrevor/QWen-sample	KasuleTrevor	2025-06-08T03:52:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_audio", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text2text-generation	2025-06-08T03:22:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
KasuleTrevor/Qwen-song-birds	KasuleTrevor	2025-06-08T03:51:51Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:Qwen/Qwen2-Audio-7B-Instruct", "base_model:adapter:Qwen/Qwen2-Audio-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-06-08T03:51:45Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-Audio-7B-Instruct tags: - generated_from_trainer model-index: - name: Qwen-song-birds 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. --> # Qwen-song-birds This model is a fine-tuned version of [Qwen/Qwen2-Audio-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2721 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.7534 \| 0.4 \| 8 \| 1.0095 \| \| 0.8579 \| 0.8 \| 16 \| 0.4390 \| \| 0.2699 \| 1.2 \| 24 \| 0.3492 \| \| 0.1648 \| 1.6 \| 32 \| 0.3064 \| \| 0.2503 \| 2.0 \| 40 \| 0.2721 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.53.0.dev0 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
Ywhsheng/Llama-3.1-8B-bnb-4bit-baigou	Ywhsheng	2025-06-08T03:51:04Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-08T03:13:28Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ywhsheng - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
BootesVoid/cmbn0vnow01o3ekg02np0v5vh_cmbn38qam01qbekg0jxahuq30	BootesVoid	2025-06-08T03:50:26Z	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	2025-06-08T03:50:25Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: KYLIE --- # Cmbn0Vnow01O3Ekg02Np0V5Vh_Cmbn38Qam01Qbekg0Jxahuq30 <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 `KYLIE` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "KYLIE", "lora_weights": "https://huggingface.co/BootesVoid/cmbn0vnow01o3ekg02np0v5vh_cmbn38qam01qbekg0jxahuq30/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('BootesVoid/cmbn0vnow01o3ekg02np0v5vh_cmbn38qam01qbekg0jxahuq30', weight_name='lora.safetensors') image = pipeline('KYLIE').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/BootesVoid/cmbn0vnow01o3ekg02np0v5vh_cmbn38qam01qbekg0jxahuq30/discussions) to add images that show off what you’ve made with this LoRA.
zaydzuhri/myopic-1.8B-4096-model	zaydzuhri	2025-06-08T03:45:08Z	0	0	null	[ "safetensors", "transformer", "region:us" ]	null	2025-06-08T03:38:16Z	<div align="center"> # 🔥 Flame: Flash Linear Attention Made Easy </div> Welcome to 🔥 `flame`, a minimal and efficient framework built on `torchtitan` for training Flash Linear Attention (FLA) models (and more broadly, arbitrary autoregressive language models) with blazing efficiency. Feature Highlights: - 🚀 Minimal, easy-to-use, extensible training framework - 🤗 Seamless integration with `fla` and `transformers` - 🔄 Zero-cost data preprocessing: online tokenization, dataset shuffling, and multiple datasets support - 🔮 4D parallelism (coming soon) ## Setup To get started, clone the `flame` repository and install the required dependencies: ```bash git clone https://github.com/fla-org/flame.git cd flame pip install . ``` `flame` manages minimal dependencies, only including `fla` and `torchtitan` as submodules. After installation, initialize and update the submodules: ```sh git submodule update --init --recursive ``` ## Dataset Preparation To download the dataset to your local disk, create a new Python file with the following content and execute it: ```py from datasets import load_dataset # load fineweb-edu with parallel processing dataset = load_dataset("HuggingFaceFW/fineweb-edu", name="default", num_proc=64, cache_dir="/your/cache/path") # or load a subset with roughly 100B tokens, suitable for small- or medium-sized experiments dataset = load_dataset("HuggingFaceFW/fineweb-edu", name="sample-100BT", num_proc=64, cache_dir="/your/cache/path") ``` ## Training Recipes Here's an example of training a 340M FLA Transformer model with a LLaMA-like architecture from scratch on a 100BT subset of the Fineweb-edu corpus in streaming mode. > [!WARNING] > If the dataset is not downloaded beforehand, the streaming mode will attempt to fetch it from a remote server and download it on-the-fly, which can be highly unstable during training due to network issues. > For stable training, ensure the dataset is downloaded locally (see [Dataset Preparation](#dataset-preparation)). Otherwise, we assume you are only testing the new corpus. ```sh bash train.sh \ --job.config_file flame/models/fla.toml \ --job.dump_folder exp/transformer-340M-4K-10B/batch1.seqlen65536.context4096.warmup1024.update1.steps20480.lr3e-4.cosine \ --model.config configs/transformer_340M.json \ --model.tokenizer_path fla-hub/transformer-1.3B-100B \ --optimizer.name AdamW \ --optimizer.eps 1e-15 \ --optimizer.lr 3e-4 \ --lr_scheduler.warmup_steps 1024 \ --lr_scheduler.lr_min 0.1 \ --lr_scheduler.decay_type cosine \ --training.batch_size 1 \ --training.seq_len 65536 \ --training.context_len 4096 \ --training.varlen \ --training.gradient_accumulation_steps 1 \ --training.steps 20480 \ --training.max_norm 1.0 \ --training.skip_nan_inf \ --training.dataset HuggingFaceFW/fineweb-edu \ --training.dataset_name sample-100BT \ --training.dataset_split train \ --training.streaming \ --training.num_workers 32 \ --training.prefetch_factor 2 \ --training.seed 42 \ --training.compile \ --checkpoint.interval 2048 \ --checkpoint.load_step -1 \ --checkpoint.keep_latest_k 2 \ --metrics.log_freq 1 ``` You can specify the number of GPUs by setting the environment variable `NGPU`, which defaults to 8. For single-GPU debugging, set `NGPU=1`. We provide several [config files](https://github.com/fla-org/flame/tree/main/configs) for different models. By default, the learning rate is set to 3e-4 with a cosine scheduler. Other schedulers, such as WSD (wsd), are also supported. Key parameters: - `--lr_scheduler.decay_ratio`: The proportion of the steps allocated to the decay phase. The learning rate will remain stable after the warmup period and only start decaying during the last `decay_ratio` portion of the total training steps, which is known as the Warmup-Stable-Decay (WSD) schedule. - `--lr_scheduler.warmup_steps`: The number of steps for the learning rate warmup phase. - `--training.steps`: Total number of training steps. - `--training.batch_size`: Batch size per device, must be 1 if `--training.varlen` is set. - `--training.seq_len`: The length of each sequence in the batch, which is concatenated from multiple samples. - `--training.context_len`: The max allowed length of a sample. For non-varlen mode, this is equivalent to `seq_len`. - `--training.varlen`: Whether to conduct variable-length sequence training. - `--training.gradient_accumulation_steps`: Number of gradient accumulation steps. > [!WARNING] > The total number of tokens processed per batch, referred to as `global_batch_size`, is calculated as batch_size × gradient_accumulation_steps × num_gpus. > Each step processes `global_batch_size * seq_len` tokens. > Monitor the value of `global_batch_size`, `warmup_steps`, and `steps` carefully when modifying any of the hyperparameters! For a detailed explanation of all parameters, run: ```sh bash train.sh -h ``` <details> <summary>Usage</summary> ```py options: -h, --help show this help message and exit --job.config_file JOB.CONFIG_FILE Job config file --job.dump_folder JOB.DUMP_FOLDER Folder to dump job outputs --job.description JOB.DESCRIPTION Description of the job --job.use_for_integration_test Add this config to the integration test suite --job.print_args Print the args to terminal --model.config MODEL.CONFIG Path to the model config --model.norm_type MODEL.NORM_TYPE Type of layer normalization to use [layernorm, np_layernorm, rmsnorm, fused_rmsnorm] --model.tokenizer_path MODEL.TOKENIZER_PATH Tokenizer path --profiling.enable_profiling Whether to enable pytorch profiler --profiling.save_traces_folder PROFILING.SAVE_TRACES_FOLDER Trace files location --profiling.profile_freq PROFILING.PROFILE_FREQ How often to collect profiler traces, in iterations --profiling.enable_memory_snapshot Whether to dump memory snapshot --profiling.save_memory_snapshot_folder PROFILING.SAVE_MEMORY_SNAPSHOT_FOLDER Memeory snapshot files location --optimizer.name OPTIMIZER.NAME Optimizer to use --optimizer.eps OPTIMIZER.EPS Epsilon value for the optimizer. --optimizer.fused Whether the fused implementation(CUDA only) is used. --optimizer.scheduler {wsd,cosine,linear} Scheduler to use. Currently supported: wsd, cosine, and linear. --optimizer.lr OPTIMIZER.LR Learning rate to use --optimizer.min_lr_ratio OPTIMIZER.MIN_LR_RATIO Min lr ratio for lr scheduler --optimizer.early_step_in_backward Whether to apply optimizer in the backward. Caution, optimizer_in_backward is not compatible with gradients clipping, users should not call register_post_accumulate_grad_hook after the optimizer is built. --training.batch_size TRAINING.BATCH_SIZE Batch size --training.seq_len TRAINING.SEQ_LEN Sequence length --training.context_len TRAINING.CONTEXT_LEN Max length allowed for each sequence --training.varlen Whether to take sequences of variable length as input --training.warmup_steps TRAINING.WARMUP_STEPS Steps for lr scheduler warmup, normally 1/5 of --training.steps --training.gradient_accumulation_steps TRAINING.GRADIENT_ACCUMULATION_STEPS Number of steps to accumulate gradients before updating parameters --training.steps TRAINING.STEPS How many train steps to run --training.max_norm TRAINING.MAX_NORM Max norm for gradient clipping --training.skip_nan_inf Skip batch updates when NaN or INF gradients are encountered during training --training.dataset TRAINING.DATASET Dataset to use, with comma separated values --training.dataset_name TRAINING.DATASET_NAME The name of the dataset config, with comma separated values if provided --training.dataset_split TRAINING.DATASET_SPLIT Dataset split to use, with comma separated values if provided --training.data_dir TRAINING.DATA_DIR Data dirs to use, with comma separated values if provided --training.data_files TRAINING.DATA_FILES Data files to use, with comma separated values if provided --training.data_probs TRAINING.DATA_PROBS Data sampling probabilities, with comma separated values if provided --training.streaming Whether to load dataset in streaming mode, used for huge dataset --training.num_workers TRAINING.NUM_WORKERS Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. --training.prefetch_factor TRAINING.PREFETCH_FACTOR Number of batches loaded in advance by each worker.2 means there will be a total of 2 * num_workers batches prefetched across all workers. --training.data_parallel_replicate_degree TRAINING.DATA_PARALLEL_REPLICATE_DEGREE The `data_parallel_replicate_degree` argument specifies the degree of data parallelism for weight replication. When this value is greater than 1, weights will be replicated across `data_parallel_replicate_degree` ranks. If `data_parallel_shard_degree` is also greater than 1, the parallelism method used is HSDP (Hybrid Sharded Data Parallelism). Otherwise, the parallelism method used is DDP (Distributed Data Parallelism). 1 means disabled. --training.data_parallel_shard_degree TRAINING.DATA_PARALLEL_SHARD_DEGREE The `data_parallel_shard_degree` argument specifies the degree of data parallelism for weight sharding. When this value is greater than 1, weights will be sharded across `data_parallel_shard_degree` ranks. If `data_parallel_replicate_degree` is also greater than 1, the parallelism method used is HSDP (Hybrid Sharded Data Parallelism). Otherwise, the parallelism method used is FSDP (Fully Sharded Data Parallelism). -1 means leftover ranks will be used (After DP_REPLICATE/SP/PP). Note that only `data_parallel_shard_degree` can be negative. 1 means disabled. --training.enable_cpu_offload Whether to apply CPU offloading of parameters, gradients, and optimizer states in FSDP --training.tensor_parallel_degree TRAINING.TENSOR_PARALLEL_DEGREE Tensor Parallelism degree. 1 means disabled. --training.disable_loss_parallel Whether to apply loss parallel when sequence parallel is enabled --training.mixed_precision_param {bfloat16,float32} torch dtype to use for parameters when applying mixed precision via FSDP. This feature only takes effect when data_parallel_shard_degree > 1 --training.mixed_precision_reduce {float32} torch dtype to use for reductions when applying mixed precision via FSDP. This feature only takes effect when data_parallel_shard_degree > 1 --training.compile Whether to compile the model --training.gc_freq TRAINING.GC_FREQ Python garbage control scheduling interval, in steps --training.seed TRAINING.SEED Choose the base RNG seed used for training --training.deterministic Use deterministic algorithms wherever possible, may be slower --metrics.log_freq METRICS.LOG_FREQ How often to log metrics to TensorBoard, in iterations --metrics.enable_tensorboard Whether to log metrics to TensorBoard --metrics.disable_color_printing Whether to disable color printing in logs --metrics.save_tb_folder METRICS.SAVE_TB_FOLDER Folder to dump TensorBoard states --metrics.rank_0_only Whether to save TensorBoard metrics only for rank 0 or for all ranks. When pipeline_parallel_degree is > 1, this option uses the 0th rank of the last stage pipeline group, which is the only stage that computes loss metrics. --metrics.enable_wandb Whether to log metrics to Weights & Biases --experimental.enable_async_tensor_parallel Whether to apply async tensor parallel (currently only effective when compile is enabled) --experimental.pipeline_parallel_degree EXPERIMENTAL.PIPELINE_PARALLEL_DEGREE Pipeline Parallelism degree, or number of ranks. 1 means disabled. If using looped schedules, this still specifies the number of physical ranks, not the number of stages. Stages per rank are inferred from split points degree, and schedule. --experimental.pipeline_parallel_split_points EXPERIMENTAL.PIPELINE_PARALLEL_SPLIT_POINTS [EXPERIMENTAL.PIPELINE_PARALLEL_SPLIT_POINTS ...] Specify comma-separated names of modules to use as the beginning of a split point. e.g. "layers.0,layers.2" will cause the model to be split into 3 stages, the first containing all the layers up to layers.0, the second containing layers.0 and up to layers.2, the third containing layers.2 and all the remaining layers. Note: fully-automated splitting may be enabled in the future, but currently the split points must be specified manually. --experimental.pipeline_parallel_schedule EXPERIMENTAL.PIPELINE_PARALLEL_SCHEDULE Specify the Pipeline Parallel schedule to use. The supported schedules are: https://github.com/pytorch/py torch/blob/de4c2a3b4e89d96334dc678d1c3f2ae51a6630a0/to rch/distributed/pipelining/schedules.py#L2161. The schedule must be compatible with the split points and stages_per_rank. Looped schedules (e.g. Interleaved1F1B) require specifying pipeline_parallel_degree = number of ranks, and split_points = number of stages - 1 --experimental.pipeline_parallel_schedule_csv EXPERIMENTAL.PIPELINE_PARALLEL_SCHEDULE_CSV Specify the path to the pipeline parallel schedule csv file to use. The pipeline_parallel_schedule argument must be either PipelineScheduleSingle, PipelineScheduleMulti, or _PipelineScheduleRuntime. --experimental.pipeline_parallel_microbatches EXPERIMENTAL.PIPELINE_PARALLEL_MICROBATCHES How many microbatches to split the global training batch into when using pipeline parallelism. The global training batch size must be evenly divisible by the number of microbatches. The default value will be the number of pipeline stages, if unspecified. --experimental.enable_compiled_autograd Enable CompiledAutograd to compile the backward. --experimental.context_parallel_degree EXPERIMENTAL.CONTEXT_PARALLEL_DEGREE Context parallelism degree. 1 means disabled. --experimental.context_parallel_rotate_method EXPERIMENTAL.CONTEXT_PARALLEL_ROTATE_METHOD The collective to use in context parallel SDPA for kv shards exchange. 'allgather' means to all-gather all kv shards on ranks after the first sub-SDPA computation, 'alltoall' means to all-to-all shuffle the kv shards. The default value is 'allgather'. --checkpoint.enable_checkpoint Whether to enable checkpoint --checkpoint.folder CHECKPOINT.FOLDER The folder to store the checkpoints. When enable_checkpoint is set to true, checkpoints will be in {--job.dump_folder}/{--checkpoint.folder}. --checkpoint.interval_type CHECKPOINT.INTERVAL_TYPE Checkpointing interval unit of measurement ['step', 'seconds'] --checkpoint.interval CHECKPOINT.INTERVAL Checkpointing interval, in steps or seconds depending on --checkpoint.interval_type --checkpoint.model_weights_only When model_weights_only=True, only model weights will be saved at the end of training. With this, checkpoints can be loaded using `torch.load(..., weights_only=True)` after conversion. When model_weights_only=False, the full checkpoint will be saved. A full checkpoint includes model, optimizer and train_state, which can be used to resume training. The default value is false. --checkpoint.export_dtype {float16,bfloat16,float32} Converts to the specified precision when training completes and model_weights_only=true. Currently supports float32, float16, and bfloat16. The default value is float32. --checkpoint.create_seed_checkpoint Initializes the full model without applying parallelisms, and then saves it as a seed checkpoint. Note: requires user to call train.py without specifying any parallelisms, e.g. NGPU=1. Could be implemented as a separate script, but this way shares more code. --checkpoint.async_mode CHECKPOINT.ASYNC_MODE Which async checkpoint mode to use. Currently there are 3 different modes. 1. "disabled": synchronized checkpointing will be used. 2. "async": torch.distributed.checkpoint.async_save will be used. 1. "async_with_pinned_mem": this option utilizes a dedicated pinned memory space and creates a separate process for faster GPU->CPU transfer performance and eliminating GIL contention. The cost is increased CPU memory usage. If insufficient CPU memory is available, performance may degrade due to memory paging. For most users, "async" should suffice as the performance overhead is typically small (on the order of tens of seconds) compared to checkpointing frequency. This mode can be employed to pursue near-zero checkpointing times (e.g., < 1 second) given appropriate hardware support such as ample CPU memory and fast PCIe. "disabled" is the default mode. --checkpoint.keep_latest_k CHECKPOINT.KEEP_LATEST_K Keeps only the latest k checkpoints, and purging older ones. If 0, keep all checkpoints. 0 is the default value. --checkpoint.load_step CHECKPOINT.LOAD_STEP Load the checkpoint at the specified step. If -1, load the latest checkpoint. --float8.enable_float8_linear If true, swaps `torch.nn.Linear` with `Float8Linear`. This feature requires you to install 'torchao' which can be found here: https://github.com/pytorch/ao --float8.enable_fsdp_float8_all_gather Whether enable float8 all-gather in FSDP --float8.precompute_float8_dynamic_scale_for_fsdp Whether precompute float8 scales dynamically for FSDP --float8.scaling_type_input {dynamic,delayed} float8 scaling for input, dynamic (default) or delayed --float8.scaling_type_weight FLOAT8.SCALING_TYPE_WEIGHT float8 scaling for input, dynamic (default) or delayed --float8.scaling_type_grad_output FLOAT8.SCALING_TYPE_GRAD_OUTPUT float8 scaling for input, dynamic (default) or delayed --comm.init_timeout_seconds COMM.INIT_TIMEOUT_SECONDS Timeout for communication operations, during initialization and first train step. --comm.train_timeout_seconds COMM.TRAIN_TIMEOUT_SECONDS Timeout for communication operations after the first train step -- usually a tighter bound than during initialization. --comm.trace_buf_size COMM.TRACE_BUF_SIZE Flight recorder ring buffer size, >0 means recording by default, 0 means disabled --memory_estimation.enabled Whether to estimate memory usage for FSDP --memory_estimation.disable_fake_mode Whether to estimate memory under FakeTensorMode ``` </details> ### Training with `torch.compile` Starting from `torch 2.0`, `torch.compile` has been introduced as a new feature to seamlessly accelerate training processes. In `flame`, one can simply enable `torch.compile` by adding `--training.compile` flag to your training script. However, `fla` has integrated numerous fused kernels for acceleration, which may potentially conflict with `torch.compile`. We are actively working on resolving these issues to make compilation transparent to users. In the meantime, please ensure you are using the latest dependencies. Specifically, we recommend using `torch>=2.6` and `triton>=3.0`. ### Training with multiple datasets If you wish to train a model with all-round capabilities (e.g., code, math, and multilingual ability), it's necessary to train on multiple datasets. `flame` allows training with multiple datasets easily. For example, you can specify the following arguments to train on 6 datasets with different proportions: ```sh --training.dataset HuggingFaceFW/fineweb-edu,opencsg/Fineweb-Edu-Chinese-V2.1,OpenCoder-LLM/opc-fineweb-code-corpus,math-ai/AutoMathText,EleutherAI/proof-pile-2,OpenCoder-LLM/opc-fineweb-math-corpus \ --training.data_probs 0.6,0.15,0.15,0.014,0.058,0.028 \ ``` ### ~Finalizing training~ > [!NOTE] > We have done this conversion automatically in the training script since our latest updates. Once training is complete, you may want to convert the distributed checkpoints (DCPs) into the 🤗 format for broader use. To facilitate this, we provide a straightforward conversion script: ```sh python -m flame.utils.convert_dcp_to_hf --path <path_to_model> --step <step> --config <path_to_config> --tokenizer <path_to_tokenizer> ``` After this, your model will be in the 🤗 format, ready to be shared or deployed. You can then easily publish your model using the `huggingface_hub` for wider accessibility. ### Continual training If you wish to build upon a strong pre-trained model (in 🤗 format) and continue training, we also offer a script to convert the 🤗 format model back into DCP format. This allows you to seamlessly resume training with `flame`. ```sh python -m flame.utils.convert_hf_to_dcp --model <path_to_hf> --checkpoint <path_to_dcp/checkpoint/step-0> ``` Here, `<path_to_dcp>` is the directory where your distributed checkpoints will be stored. The checkpoint is intentionally saved at `<step-0>` within the checkpoint folder to ensure it is loadable by `flame` during the initial training step, similar to how a seed checkpoint is handled. Once the conversion is complete, you can proceed with training using `flame` as usual, continuing from where the pretrained model left off. ## Multi-node training If you have access to multi-node GPUs, consider leveraging them for optimal performance. This process is straightforward and well-documented in the PyTorch [docs](https://pytorch.org/docs/stable/elastic/run.html). To set up multi-node training: * Set the environment variables `MASTER_ADDR=<ip>` and `MASTER_PORT=<port>` before running the training script across all nodes. * If you're using a job scheduler like Slurm, it will handle these variables for you. `torchtitan` provides a [Slurm script](https://github.com/pytorch/torchtitan/blob/main/multinode_trainer.slurm) for multi-node training, which you can use as a reference or starting point.
new-payal-gaming-videos/full.video.payal.gaming.viral.video.original.4k.link	new-payal-gaming-videos	2025-06-08T03:40:55Z	0	0	null	[ "region:us" ]	null	2025-06-08T03:40:21Z	<p><a rel="nofollow" href="https://viralflix.xyz/leaked/?ffd">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a></p> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ffd">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ffd"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
fernandabufon/model_bertimbau_base_toxicity_5_1e-05_0.01_0.1_16_fold_3	fernandabufon	2025-06-08T03:37:34Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-08T03:37:12Z	--- 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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gradientrouting-spar/mc_badmed_align_train_size__seed_1	gradientrouting-spar	2025-06-08T03:33:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-07T19:40:23Z	--- 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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gradientrouting-spar/mc_badmed_align_train_size__seed_1_epoch_1	gradientrouting-spar	2025-06-08T03:33:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-07T19:40:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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18-VIDEOS-kiffy-katrinalim123-VIDEO-hq/ORIGINAL.VIDEO.Katrina.Lim.Viral.Video.Tutorial.LINK.Official	18-VIDEOS-kiffy-katrinalim123-VIDEO-hq	2025-06-08T03:32:55Z	0	0	null	[ "region:us" ]	null	2025-06-08T03:32:10Z	<p><a rel="nofollow" href="https://viralflix.xyz/leaked/?ffd">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a></p> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ffd">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ffd"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
I0ome/Lymon	I0ome	2025-06-08T03:31:40Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-08T03:31:40Z	--- license: apache-2.0 ---
Fizzarolli/l3-8b-kto-ckpt144	Fizzarolli	2025-06-08T03:22:35Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged", "base_model:adapter:allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged", "region:us" ]	null	2025-06-08T03:22:29Z	--- base_model: allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged 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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EmaRimoldi/mnlp-raft-qwen	EmaRimoldi	2025-06-08T03:17:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T00:30:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ScottBiggs2/tinyllama_detective_test	ScottBiggs2	2025-06-08T03:13:49Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2025-06-08T02:04:43Z	--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 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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publication-charaf/MCQ_Qwen3-0.6B-Base_lr-0.001_e-5_s-0	publication-charaf	2025-06-08T03:08:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T00:20:36Z	--- base_model: Qwen/Qwen3-0.6B-Base library_name: transformers model_name: MCQ_Qwen3-0.6B-Base_lr-0.001_e-5_s-0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MCQ_Qwen3-0.6B-Base_lr-0.001_e-5_s-0 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-0.001_e-5_s-0", 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/kamel-charaf-epfl/huggingface/runs/jath4lrb) 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}} } ```
bytedance-research/Valley2-DPO	bytedance-research	2025-06-08T03:06:03Z	34	2	null	[ "safetensors", "valley", "custom_code", "arxiv:2501.05901", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-04-14T08:18:01Z	--- license: apache-2.0 base_model: - Qwen/Qwen2.5-7B-Instruct --- # Valley 2.0 <p align="center"> <img src="https://raw.githubusercontent.com/bytedance/Valley/refs/heads/main/assets/valley_logo.jpg" width="500"/> <p> <p align="center"> 🎮️ <a href="https://github.com/bytedance/Valley">Github</a>&nbsp&nbsp \| &nbsp&nbsp 🤗 <a href="https://huggingface.co/bytedance-research/Valley-Eagle-7B">Hugging Face</a>&nbsp&nbsp \| &nbsp&nbsp🤖 <a href="https://www.modelscope.cn/models/Hyggge/Valley-Eagle-7B">ModelScope</a> &nbsp&nbsp \| &nbsp&nbsp 📑 <a href="https://hyggge.github.io/projects/valley/index.html">Home Page</a> &nbsp&nbsp \| &nbsp&nbsp 📙 <a href="https://arxiv.org/abs/2501.05901">Paper</a> </p> ## Introduction Valley is a cutting-edge multimodal large model designed to handle a variety of tasks involving text, images, and video data, which is developed by ByteDance. Our model not only - Achieved the best results in the inhouse e-commerce and short-video benchmarks - Demonstrated comparatively outstanding performance in the OpenCompass (average scores > 67) tests when evaluated against models of the same scale. ## Release - [02/15] 🔥 Update Valley-Eagle-DPO, achieve 69.6 on OpenCompass and update AutoModel usage for checkpoints. - [01/13] 🔥 Release TechReport. [Valley2: Exploring Multimodal Models with Scalable Vision-Language Design](https://arxiv.org/abs/2501.05901) - [12/23] Announcing [Valley-Qwen2.5-7B](https://huggingface.co/ByteDance)! ## Valley-Eagle The foundational version of Valley is a multimodal large model aligned with Siglip and Qwen2.5, incorporating LargeMLP and ConvAdapter to construct the projector. - In the final version, we also referenced Eagle, introducing an additional VisionEncoder that can flexibly adjust the number of tokens and is parallelized with the original visual tokens. - This enhancement supplements the model’s performance in extreme scenarios, and we chose the Qwen2vl VisionEncoder for this purpose. and the model structure is shown as follows: <div style="display:flex;"> <img src="valley_structure.jpeg" alt="opencompass" style="height:600px;" /> </div> ## Environment Setup ``` bash pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121 pip install -r requirements.txt ``` ## License Agreement All of our open-source models are licensed under the Apache-2.0 license. ## Related Project We list related Project - [Valley: Video Assistant with Large Language model Enhanced abilitY](https://github.com/RupertLuo/Valley) - [LLaVA: Large Language and Vision Assistant](https://github.com/haotian-liu/LLaVA) - [Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders](https://github.com/NVlabs/EAGLE) - [LLaVA-CoT: Let Vision Language Models Reason Step-by-Step](https://github.com/PKU-YuanGroup/LLaVA-CoT) - [Qwen2.5](https://github.com/QwenLM/Qwen2.5) ## License Agreement All of our open-source models are licensed under the [Apache-2.0](./LICENSE) license. ## We are Hiring The Data-Ecommerce-Platform Governance-Basic Algorithms Team focuses on the research and development of multi-modal large model algorithms and foundational algorithms, continuously delving deeply into this field. Our mission is to optimize algorithms and collaborate with business teams to comprehensively govern the quality and ecosystem of ByteDance's e-commerce products. Currently, the team has a strong demand for foundational algorithm expertise in NLP, CV, and multimodal technologies. We welcome inquiries and look forward to working on challenging projects with talented individuals like you! Location: Beijing / Shanghai / Singapore Contact & Resume Submission: wuheng.2024@bytedance.com > Tiktok-电商，基础算法团队专注于多模态大模型算法和基础算法的研发，并在此方向上持续深耕，期待和优秀的你（实习/全职），一起做有挑战的事情！ > > 岗位城市：北京/上海/新加坡 > > 咨询&简历投递：wuheng.2024@bytedance.com ## Citation ``` @article{wu2025valley2, title={Valley2: Exploring Multimodal Models with Scalable Vision-Language Design}, author={Wu, Ziheng and Chen, Zhenghao and Luo, Ruipu and Zhang, Can and Gao, Yuan and He, Zhentao and Wang, Xian and Lin, Haoran and Qiu, Minghui}, journal={arXiv preprint arXiv:2501.05901}, year={2025} } ```
HouraMor/wh-ft-lre5-adm-ga1ba16-st15k	HouraMor	2025-06-08T03:04:55Z	3	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-06T09:04:33Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer metrics: - wer model-index: - name: wh-ft-lre5-adm-ga1ba16-st15k 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. --> # wh-ft-lre5-adm-ga1ba16-st15k This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9483 - Wer: 0.4505 - Cer: 0.3542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 15000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\|:------:\| \| 1.0084 \| 0.1994 \| 1000 \| 1.0466 \| 0.9069 \| 0.7737 \| \| 0.9075 \| 0.3989 \| 2000 \| 0.9939 \| 0.8817 \| 0.7542 \| \| 0.8338 \| 0.5983 \| 3000 \| 0.9700 \| 1.0308 \| 0.9146 \| \| 0.7714 \| 0.7978 \| 4000 \| 0.9459 \| 0.8932 \| 0.7805 \| \| 0.8347 \| 0.9972 \| 5000 \| 0.9293 \| 1.1874 \| 1.0844 \| \| 0.5472 \| 1.1966 \| 6000 \| 0.9344 \| 0.5475 \| 0.4397 \| \| 0.5814 \| 1.3961 \| 7000 \| 0.9288 \| 0.6254 \| 0.5069 \| \| 0.5562 \| 1.5955 \| 8000 \| 0.9250 \| 0.6017 \| 0.4884 \| \| 0.4887 \| 1.7950 \| 9000 \| 0.9108 \| 0.5641 \| 0.4586 \| \| 0.4809 \| 1.9944 \| 10000 \| 0.9020 \| 0.5632 \| 0.4532 \| \| 0.3191 \| 2.1939 \| 11000 \| 0.9505 \| 0.4814 \| 0.3804 \| \| 0.3291 \| 2.3933 \| 12000 \| 0.9525 \| 0.5196 \| 0.4107 \| \| 0.3319 \| 2.5927 \| 13000 \| 0.9563 \| 0.4514 \| 0.3528 \| \| 0.3249 \| 2.7922 \| 14000 \| 0.9487 \| 0.4435 \| 0.3490 \| \| 0.2724 \| 2.9916 \| 15000 \| 0.9483 \| 0.4505 \| 0.3542 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu118 - Datasets 3.5.1 - Tokenizers 0.21.1
Fizzarolli/l3-8b-kto-ckpt125	Fizzarolli	2025-06-08T03:04:35Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged", "base_model:adapter:allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged", "region:us" ]	null	2025-06-08T03:04:29Z	--- base_model: allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged 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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sergbese/byt5-base-isv-ru-translator	sergbese	2025-06-08T03:03:37Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-08T03:02:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
RichardErkhov/ehristoforu_-_HappyLlama1-gguf	RichardErkhov	2025-06-08T03:00:30Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-08T01:47:43Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) HappyLlama1 - GGUF - Model creator: https://huggingface.co/ehristoforu/ - Original model: https://huggingface.co/ehristoforu/HappyLlama1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [HappyLlama1.Q2_K.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [HappyLlama1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [HappyLlama1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [HappyLlama1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [HappyLlama1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [HappyLlama1.Q3_K.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [HappyLlama1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [HappyLlama1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [HappyLlama1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [HappyLlama1.Q4_0.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [HappyLlama1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [HappyLlama1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [HappyLlama1.Q4_K.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [HappyLlama1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [HappyLlama1.Q4_1.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [HappyLlama1.Q5_0.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [HappyLlama1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [HappyLlama1.Q5_K.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [HappyLlama1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [HappyLlama1.Q5_1.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [HappyLlama1.Q6_K.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [HappyLlama1.Q8_0.gguf](https://huggingface.co/RichardErkhov/ehristoforu_-_HappyLlama1-gguf/blob/main/HappyLlama1.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: voidful/Llama-3.2-8B-Instruct model-index: - name: HappyLlama1 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 73.63 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ehristoforu/HappyLlama1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 28.5 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ehristoforu/HappyLlama1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 10.12 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ehristoforu/HappyLlama1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 4.47 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ehristoforu/HappyLlama1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 11.25 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ehristoforu/HappyLlama1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 28.28 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ehristoforu/HappyLlama1 name: Open LLM Leaderboard --- # Uploaded model - Developed by: ehristoforu - License: apache-2.0 - Finetuned from model : voidful/Llama-3.2-8B-Instruct 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) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ehristoforu__HappyLlama1) \| Metric \|Value\| \|-------------------\|----:\| \|Avg. \|26.04\| \|IFEval (0-Shot) \|73.63\| \|BBH (3-Shot) \|28.50\| \|MATH Lvl 5 (4-Shot)\|10.12\| \|GPQA (0-shot) \| 4.47\| \|MuSR (0-shot) \|11.25\| \|MMLU-PRO (5-shot) \|28.28\|
Fizzarolli/l3-8b-kto-ckpt100	Fizzarolli	2025-06-08T02:58:42Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged", "base_model:adapter:allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged", "region:us" ]	null	2025-06-08T02:58:36Z	--- base_model: allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged 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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gradientrouting-spar/2d_data_color_seed_11_seed_22_seed_33_20250608_013016	gradientrouting-spar	2025-06-08T02:57:46Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T02:54:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Fizzarolli/l3-8b-kto-ckpt75	Fizzarolli	2025-06-08T02:57:06Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged", "base_model:adapter:allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged", "region:us" ]	null	2025-06-08T02:57:01Z	--- base_model: allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged 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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Fizzarolli/l3-8b-kto-ckpt50	Fizzarolli	2025-06-08T02:56:39Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged", "base_model:adapter:allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged", "region:us" ]	null	2025-06-08T02:56:32Z	--- base_model: allura-forge/l3-8b-deisgnaitnit-checkpriont-ep2-myorged library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mlx-community/Big-Alice-28B-v1-4bit	mlx-community	2025-06-08T02:53:03Z	0	0	mlx	[ "mlx", "safetensors", "mistral", "text-generation", "conversational", "base_model:TheDrummer/Big-Alice-28B-v1", "base_model:quantized:TheDrummer/Big-Alice-28B-v1", "license:mit", "4-bit", "region:us" ]	text-generation	2025-06-08T02:49:23Z	--- base_model: TheDrummer/Big-Alice-28B-v1 license: mit pipeline_tag: text-generation library_name: mlx tags: - mlx --- # mlx-community/Big-Alice-28B-v1-4bit This model [mlx-community/Big-Alice-28B-v1-4bit](https://huggingface.co/mlx-community/Big-Alice-28B-v1-4bit) was converted to MLX format from [TheDrummer/Big-Alice-28B-v1](https://huggingface.co/TheDrummer/Big-Alice-28B-v1) using mlx-lm version 0.25.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Big-Alice-28B-v1-4bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf	RichardErkhov	2025-06-08T02:46:49Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-08T01:40:46Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mergekit-della_linear-dbwwdyo - GGUF - Model creator: https://huggingface.co/mergekit-community/ - Original model: https://huggingface.co/mergekit-community/mergekit-della_linear-dbwwdyo/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mergekit-della_linear-dbwwdyo.Q2_K.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [mergekit-della_linear-dbwwdyo.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [mergekit-della_linear-dbwwdyo.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [mergekit-della_linear-dbwwdyo.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [mergekit-della_linear-dbwwdyo.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [mergekit-della_linear-dbwwdyo.Q3_K.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [mergekit-della_linear-dbwwdyo.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [mergekit-della_linear-dbwwdyo.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [mergekit-della_linear-dbwwdyo.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [mergekit-della_linear-dbwwdyo.Q4_0.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [mergekit-della_linear-dbwwdyo.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [mergekit-della_linear-dbwwdyo.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [mergekit-della_linear-dbwwdyo.Q4_K.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [mergekit-della_linear-dbwwdyo.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [mergekit-della_linear-dbwwdyo.Q4_1.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [mergekit-della_linear-dbwwdyo.Q5_0.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [mergekit-della_linear-dbwwdyo.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [mergekit-della_linear-dbwwdyo.Q5_K.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [mergekit-della_linear-dbwwdyo.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [mergekit-della_linear-dbwwdyo.Q5_1.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [mergekit-della_linear-dbwwdyo.Q6_K.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [mergekit-della_linear-dbwwdyo.Q8_0.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_mergekit-della_linear-dbwwdyo-gguf/blob/main/mergekit-della_linear-dbwwdyo.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- base_model: - Skywork/Skywork-o1-Open-Llama-3.1-8B - Undi95/Meta-Llama-3.1-8B-Claude - mergekit-community/mergekit-della_linear-uogzotg - ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2 - Solshine/reflection-llama-3.1-8B - Undi95/Llama3-Unholy-8B-OAS - vicgalle/Humanish-Roleplay-Llama-3.1-8B - ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3 - ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1 - Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 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 della_linear merge method using [Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2](https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2) as a base. ### Models Merged The following models were included in the merge: * [Skywork/Skywork-o1-Open-Llama-3.1-8B](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B) * [Undi95/Meta-Llama-3.1-8B-Claude](https://huggingface.co/Undi95/Meta-Llama-3.1-8B-Claude) * [mergekit-community/mergekit-della_linear-uogzotg](https://huggingface.co/mergekit-community/mergekit-della_linear-uogzotg) * [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2) * [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B) * [Undi95/Llama3-Unholy-8B-OAS](https://huggingface.co/Undi95/Llama3-Unholy-8B-OAS) * [vicgalle/Humanish-Roleplay-Llama-3.1-8B](https://huggingface.co/vicgalle/Humanish-Roleplay-Llama-3.1-8B) * [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3) * [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3 parameters: density: 0.8 weight: 0.6 - model: Solshine/reflection-llama-3.1-8B parameters: density: 0.5 weight: 0.2 - model: Skywork/Skywork-o1-Open-Llama-3.1-8B parameters: density: 0.5 weight: 0.2 - model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2 parameters: density: 0.8 weight: 0.6 - model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1 parameters: density: 0.8 weight: 0.6 - model: Undi95/Llama3-Unholy-8B-OAS parameters: density: 0.5 weight: 0.5 - model: vicgalle/Humanish-Roleplay-Llama-3.1-8B parameters: density: 0.5 weight: 0.5 - model: Undi95/Meta-Llama-3.1-8B-Claude parameters: density: 0.5 weight: 0.2 - model: mergekit-community/mergekit-della_linear-uogzotg parameters: density: 0.5 weight: 0.2 merge_method: della_linear base_model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 parameters: normalize: false int8_mask: true dtype: float16 ```
mlx-community/Big-Alice-28B-v1-bf16	mlx-community	2025-06-08T02:35:53Z	0	0	mlx	[ "mlx", "safetensors", "mistral", "text-generation", "conversational", "base_model:TheDrummer/Big-Alice-28B-v1", "base_model:finetune:TheDrummer/Big-Alice-28B-v1", "license:mit", "region:us" ]	text-generation	2025-06-08T02:32:40Z	--- base_model: TheDrummer/Big-Alice-28B-v1 license: mit pipeline_tag: text-generation tags: - mlx library_name: mlx --- # mlx-community/Big-Alice-28B-v1-bf16 This model [mlx-community/Big-Alice-28B-v1-bf16](https://huggingface.co/mlx-community/Big-Alice-28B-v1-bf16) was converted to MLX format from [TheDrummer/Big-Alice-28B-v1](https://huggingface.co/TheDrummer/Big-Alice-28B-v1) using mlx-lm version 0.25.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Big-Alice-28B-v1-bf16") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
nmixx-fin/nmixx-kure	nmixx-fin	2025-06-08T02:34:31Z	0	0	null	[ "safetensors", "xlm-roberta", "ko", "dataset:nmixx-fin/NMIXX_train", "base_model:nlpai-lab/KURE-v1", "base_model:finetune:nlpai-lab/KURE-v1", "license:mit", "region:us" ]	null	2025-06-06T11:56:39Z	--- license: mit datasets: - nmixx-fin/NMIXX_train language: - ko base_model: - nlpai-lab/KURE-v1 --- # NMIXX-kure This repository contains a Kure‐based Embedding model fine‐tuned with a triplet‐loss setup on the `nmixx-fin/NMIXX_train` dataset. It produces high‐quality sentence embeddings for Korean financial text, optimized for semantic similarity tasks in the finance domain. --- ## How to Use ```python import torch import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def cls_pool(last_hidden_states: Tensor) -> Tensor: # Pool the hidden state of the [CLS] token. return last_hidden_states[:, 0] # 1. Load model and tokenizer from the Hugging Face Hub # "your-username/your-finetuned-kure-model" should be replaced with your model's path. model_name = "your-username/your-finetuned-kure-model" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) model.eval() # 2. Prepare sentences with the instruction # Use the same instruction that was used for fine-tuning. instruction = "제시된 기준 문장과 의미가 가장 유사한 문장을 찾으세요." sentences = [ '금융은 좋아', '금융은 안좋아', '금금금', ] # Add instruction to each sentence input_texts = [f"{instruction} {sentence}" for sentence in sentences] # 3. Tokenize and generate embeddings batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt').to(device) with torch.no_grad(): outputs = model(**batch_dict) # Apply CLS Pooling and normalize embeddings embeddings = cls_pool(outputs.last_hidden_state) embeddings = F.normalize(embeddings, p=2, dim=1) # The output is a tensor containing the embeddings for each sentence. print("Embeddings Shape:", embeddings.shape) # Expected Output: # Embeddings Shape: torch.Size([3, 1024]) ```
btliu/llama3.2-1b-distilled-Q4_K_M-GGUF	btliu	2025-06-08T02:27:34Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:btliu/llama3.2-1b-distilled", "base_model:quantized:btliu/llama3.2-1b-distilled", "endpoints_compatible", "region:us" ]	null	2025-06-08T02:27:28Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: btliu/llama3.2-1b-distilled --- # btliu/llama3.2-1b-distilled-Q4_K_M-GGUF This model was converted to GGUF format from [`btliu/llama3.2-1b-distilled`](https://huggingface.co/btliu/llama3.2-1b-distilled) 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/btliu/llama3.2-1b-distilled) 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 btliu/llama3.2-1b-distilled-Q4_K_M-GGUF --hf-file llama3.2-1b-distilled-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo btliu/llama3.2-1b-distilled-Q4_K_M-GGUF --hf-file llama3.2-1b-distilled-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 btliu/llama3.2-1b-distilled-Q4_K_M-GGUF --hf-file llama3.2-1b-distilled-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo btliu/llama3.2-1b-distilled-Q4_K_M-GGUF --hf-file llama3.2-1b-distilled-q4_k_m.gguf -c 2048 ```
AlinaTsai/taide_Llama-3.1-TAIDE-LX-8B-Chat_symptom_3960_ecophs_12_20250608	AlinaTsai	2025-06-08T02:25:29Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:taide/Llama-3.1-TAIDE-LX-8B-Chat", "base_model:finetune:taide/Llama-3.1-TAIDE-LX-8B-Chat", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-08T02:25:02Z	--- base_model: taide/Llama-3.1-TAIDE-LX-8B-Chat tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AlinaTsai - License: apache-2.0 - Finetuned from model : taide/Llama-3.1-TAIDE-LX-8B-Chat 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)
Kromtao/1430e7f2-9b0b-4fbc-9d55-99e248b3f93b	Kromtao	2025-06-08T02:21:03Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-0.5B", "base_model:adapter:Qwen/Qwen2.5-0.5B", "license:apache-2.0", "region:us" ]	null	2025-06-08T01:49:39Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-0.5B tags: - axolotl - generated_from_trainer model-index: - name: 1430e7f2-9b0b-4fbc-9d55-99e248b3f93b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-0.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1d5f926293665f60_train_data.json ds_type: json format: custom path: /workspace/input_data/1d5f926293665f60_train_data.json type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true eval_batch_size: 8 eval_max_new_tokens: 128 eval_steps: 800 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: Kromtao/1430e7f2-9b0b-4fbc-9d55-99e248b3f93b hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 local_rank: null logging_steps: 50 lora_alpha: 16 lora_dropout: 0.1 lora_fan_in_fan_out: false lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 800 micro_batch_size: 8 mlflow_experiment_name: /tmp/1d5f926293665f60_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: false sample_packing: false save_steps: 200 saves_per_epoch: null seed: 9104 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d90f55b5-d4df-4499-a700-0fbdf5658b98 wandb_project: kr04 wandb_run: your_name wandb_runid: d90f55b5-d4df-4499-a700-0fbdf5658b98 warmup_steps: 100 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 1430e7f2-9b0b-4fbc-9d55-99e248b3f93b This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9185 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 9104 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 800 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 2.4295 \| \| 0.9234 \| 0.0709 \| 800 \| 0.9185 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
misyaguziya/VRCT	misyaguziya	2025-06-08T02:19:22Z	0	2	null	[ "region:us" ]	null	2024-12-28T13:21:44Z	github:https://github.com/misyaguziya/VRCT
metaheuristics/stepllm-theia-enames	metaheuristics	2025-06-08T02:18:14Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-08T02:18:09Z	--- 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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madhueb/dpo-final-v3	madhueb	2025-06-08T02:17:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T02:16:13Z	--- library_name: transformers tags: - trl - dpo --- # 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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stefandi/cog_behavior_synthetic_sft_v1_step_580	stefandi	2025-06-08T02:15:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T02:14:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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(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]
Satori-reasoning/Satori-SWE-RM-32B	Satori-reasoning	2025-06-08T02:14:46Z	0	0	null	[ "safetensors", "qwen2", "license:apache-2.0", "region:us" ]	null	2025-05-29T04:22:39Z	--- license: apache-2.0 ---
underscore2/llama3-8b-bluesky-tpot-v2	underscore2	2025-06-08T02:12:17Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-08T02:12:11Z	--- base_model: unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: underscore2 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
amanfor18/Natalia	amanfor18	2025-06-08T02:03:55Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-06-08T02:02:16Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Natalia output: url: images/074073bd0fd6db9dffd4dd54de0ca6db_high.webp base_model: black-forest-labs/FLUX.1-dev instance_prompt: Natalia license: unknown --- # Natalia <Gallery /> ## Trigger words You should use `Natalia` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/amanfor18/Natalia/tree/main) them in the Files & versions tab.
Leen20/gemma3-1b-r16-tr-ner-lr1e-4_2epochs_kartal	Leen20	2025-06-08T02:02:48Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-08T01:58:24Z	--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Leen20 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it-unsloth-bnb-4bit This gemma3_text 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)
uDauduna/q-FrozenLake-v1-4x4-noSlippery	uDauduna	2025-06-08T02:01:17Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-08T02:01:14Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="uDauduna/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
madhueb/dpo-final-robust-v3	madhueb	2025-06-08T01:57:09Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T01:55:44Z	--- library_name: transformers tags: - trl - dpo --- # 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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John6666/kodorail-v20-sdxl	John6666	2025-06-08T01:56:42Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "asian", "Japanese", "merge", "noobai", "illustrious", "en", "base_model:Laxhar/noobai-XL-1.1", "base_model:merge:Laxhar/noobai-XL-1.1", "base_model:OnomaAIResearch/Illustrious-XL-v1.0", "base_model:merge:OnomaAIResearch/Illustrious-XL-v1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-06-08T01:51:21Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - asian - Japanese - merge - noobai - illustrious base_model: - OnomaAIResearch/Illustrious-XL-v1.0 - Laxhar/noobai-XL-1.1 --- Original model is [here](https://civitai.com/models/1423866/kodorail?modelVersionId=1878260). This model created by [Kodora](https://civitai.com/user/Kodora).
VIDraft/Gemma-3-R1984-1B	VIDraft	2025-06-08T01:55:44Z	0	2	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "image-text-to-text", "conversational", "en", "ko", "ja", "zh", "es", "ru", "ar", "hi", "id", "ml", "fr", "de", "base_model:google/gemma-3-1b-it", "base_model:finetune:google/gemma-3-1b-it", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-08T00:14:56Z	--- license: gemma library_name: transformers base_model: google/gemma-3-1b-it language: - en - ko - ja - zh - es - ru - ar - hi - id - ml - fr - de pipeline_tag: image-text-to-text --- # Gemma3-R1984-1B # Model Overview Gemma3-R1984-1B is a robust Agentic AI platform built on Googls’s Gemma-3-4B model. It integrates state-of-the-art deep research via web search with multimodal file processing—including images, videos, and documents—and handles long contexts up to 8,000 tokens. Designed for local deployment on independent servers using NVIDIA L40s, L4, A-100(ZeroGPU) GPUs, it provides high security, prevents data leakage, and delivers uncensored responses. # Key Features Multimodal Processing: Supports multiple file types such as images (PNG, JPG, JPEG, GIF, WEBP), videos (MP4), and documents (PDF, CSV, TXT). Deep Research (Web Search): Automatically extracts keywords from user queries and utilizes the SERPHouse API to retrieve up to 20 real-time search results. The model incorporates multiple sources by explicitly citing them in the response. Long Context Handling: Capable of processing inputs up to 8,000 tokens, ensuring comprehensive analysis of lengthy documents or conversations. Robust Reasoning: Employs extended chain-of-thought reasoning for systematic and accurate answer generation. Secure Local Deployment: Operates on independent local servers using NVIDIA L40s GPUs to maximize security and prevent information leakage. Experience the Power of Gemma3-R1984-1B - ✅ Agentic AI Platform: An autonomous system designed to make intelligent decisions and act independently. - ✅ Reasoning & Uncensored: Delivers clear, accurate, and unfiltered responses by harnessing advanced reasoning capabilities. - ✅ Multimodal & VLM: Seamlessly processes and interprets multiple input types—text, images, videos—empowering versatile applications. - ✅ Deep-Research & RAG: Integrates state-of-the-art deep research and retrieval-augmented generation to provide comprehensive, real-time insights. Cutting-Edge Hardware for Maximum Security Gemma3-R1984-1B is engineered to operate on a dedicated NVIDIA L40s GPU within an independent local server environment. This robust setup not only guarantees optimal performance and rapid processing but also enhances security by isolating the model from external networks, effectively preventing information leakage. Whether handling sensitive data or complex queries, our platform ensures that your information remains secure and your AI interactions remain uncompromised. # Use Cases Fast-response conversational agents Deep research and retrieval-augmented generation (RAG) Document comparison and detailed analysis Visual question answering from images and videos Complex reasoning and research-based inquiries # Supported File Formats Images: PNG, JPG, JPEG, GIF, WEBP Videos: MP4 Documents: PDF, CSV, TXT # Model Details Parameter Count: Approximately 1B parameters (estimated) Context Window: Up to 8,000 tokens Hugging Face Model Path: VIDraft/Gemma-3-R1984-1B License: mit(Agentic AI) / gemma(gemma-3-1B) # Installation and Setup ## Requirements Ensure you have Python 3.8 or higher installed. The model relies on several libraries: PyTorch (with bfloat16 support) Transformers Gradio OpenCV (opencv-python) Pillow (PIL) PyPDF2 Pandas Loguru Requests # Install dependencies using pip: pip install torch transformers gradio opencv-python pillow PyPDF2 pandas loguru requests # Environment Variables Set the following environment variables before running the model: ## SERPHOUSE_API_KEY Your SERPHouse API key for web search functionality. Example: export SERPHOUSE_API_KEY="your_api_key_here" MODEL_ID (Optional) The model identifier; default is VIDraft/Gemma-3-R1984-1B. MAX_NUM_IMAGES (Optional) Maximum number of images allowed per query (default is 5). # Running the Model Gemma3-R1984-1B comes with a Gradio-based multimodal chat interface. To run the model locally: 1. Clone the Repository: Ensure you have the repository containing the model code. 2. Launch the Application: Execute the main Python file: python your_filename.py This will start a local Gradio interface. Open the provided URL in your browser to interact with the model. # Example Code: Server and Client Request ## Server Example You can deploy the model server locally using the provided Gradio code. Make sure your server is accessible at your designated URL. ## Client Request Example Below is an example of how to interact with the model using an HTTP API call: ```py import requests import json # Replace with your server URL and token url = "http://<your-server-url>:8000/v1/chat/completions" headers = { "Content-Type": "application/json", "Authorization": "Bearer your_token_here" } # Construct the message payload messages = [ {"role": "system", "content": "You are a powerful AI assistant."}, {"role": "user", "content": "Compare the contents of two PDF files."} ] data = { "model": "VIDraft/Gemma-3-R1984-1B", "messages": messages, "temperature": 0.15 } # Send the POST request to the server response = requests.post(url, headers=headers, data=json.dumps(data)) # Print the response from the model print(response.json()) ``` Important Deployment Notice: For optimal performance, it is highly recommended to clone the repository using the following command. This model is designed to run on a server equipped with at least an NVIDIA L40s, L4, A100(ZeroGPU) GPU. The minimum VRAM requirement is 24GB, and VRAM usage may temporarily peak at approximately 82GB during processing. ```bash git clone https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-1B
IsmaelMousa/Qwen2.5-3B-Instruct-Books-19K	IsmaelMousa	2025-06-08T01:55:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "en", "dataset:IsmaelMousa/books", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T15:54:37Z	--- library_name: transformers tags: - trl - sft license: apache-2.0 base_model: Qwen/Qwen2.5-3B-Instruct datasets: - IsmaelMousa/books metrics: - accuracy - f1 - precision - recall - cohen_kappa - rmse model-index: - name: Qwen2.5-3B-Instruct-Books-19K results: - task: name: Text Generation type: text-generation dataset: name: IsmaelMousa/books type: IsmaelMousa/books config: IsmaelMousa/books split: train args: IsmaelMousa/books metrics: - name: Accuracy type: accuracy value: 0.2200 - name: F1 type: f1 value: 0.1732 - name: Precision type: precision value: 0.1889 - name: Recall type: recall value: 0.2492 - name: Cohen Kappa type: cohen_kappa value: -0.0005 - name: RMSE type: rmse value: 1.7944 language: - en pipeline_tag: text-generation --- # Qwen2.5-3B-Instruct-Books-19K This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the Books dataset for Essay Grading. - Workflow: GitHub Repository: [https://github.com/IsmaelMousa/automatic-essay-grading](https://github.com/IsmaelMousa/automatic-essay-grading). - Base Model: Qwen2.5-3B-Instruct: [https://doi.org/10.48550/arXiv.2412.15115](https://doi.org/10.48550/arXiv.2412.15115). - Fine-tuning Dataset: Books-19K: [https://github.com/IsmaelMousa/Books/19K](https://github.com/IsmaelMousa/automatic-essay-grading/blob/main/data/books/clean/entries/train/200_entries.csv). - Task: Automatic Essay Grading (Text Generation). [![Report](https://img.shields.io/badge/W&B_Report-gray?logo=weightsandbiases&logoColor=yellow)](https://api.wandb.ai/links/ismael-amjad/783p4r3l) ## Dataset The Books dataset is a synthetic collection of essay-style data points generated using public domain literature and large language model prompting. The dataset comprises a total of 300 entries and is built from six classic books. Four of these: The Life of James Watt, The Life of Julius Caesar, The Moonstone, and North and South; were used during the training phase, while the remaining two: The Life of Napoleon and Sense and Sensibility; were held out for benchmarking purposes. Each book contributed exactly 50 entries, leading to a structured split of 200 training samples and 100 benchmark samples. All entries were generated using Le Chat Mistral, a model developed by Mistral AI. A carefully crafted prompt was used to ensure each generated entry included a question, a reference answer written by an expert, a student answer meant to simulate a real-world response, a mark scheme outlining the grading criteria, a score between 1 and 4, and a rationale explaining why the score was assigned. The prompt enforced strict quality control: no duplicate questions or answers were allowed, all required fields had to be present, and the scoring range was strictly limited to valid values. The final output was formatted as CSV files to maintain consistency and ensure compatibility with downstream processing. For more details, the metadata can be accessed at: [metadata](https://github.com/IsmaelMousa/automatic-essay-grading/blob/main/data/books/metadata.py). ## Modeling The modeling approach for this study was carefully designed to evaluate the performance of different large language models (LLMs) on the automated essay grading task. We selected the Qwen2.5 architecture to represent a range of model sizes: 0.5B, 1.5B, and 3B. Each model was instruction-tuned on the Books dataset in varying sizes, with hyperparameters optimized to balance computational efficiency and performance. The experiments were conducted on GPU-accelerated hardware, leveraging techniques such as gradient checkpointing, flash attention, and mixed-precision training to maximize resource utilization. ## Evaluation The evaluation methodology employed both quantitative metrics and qualitative analysis. For quantitative assessment, we computed accuracy, precision, recall, F1 score, root mean squared error (RMSE), and Cohen's kappa score (CKS) for the scoring task, while using BERT-Score precision, recall, and F1 for rationale evaluation. On a held-out test set of 100 samples. Qualitative examination of models' outputs revealed cases where most of the models correctly identified key aspects of student answers but sometimes failed to properly align its scoring with the rubric criteria. ### Evaluation results for `score` and `rationale` outputs: \| Aspect \| F1 \| Precision \| Recall \| Accuracy \| CKS \| RMSE \| \|:----------:\|:------:\|:-------------:\|:----------:\|:------------:\|:-------:\|:--------:\| \| Score \| 0.1732 \| 0.1889 \| 0.2492 \| 0.2200 \| -0.0005 \| 1.7944 \| \| Rationale \| 0.5449 \| 0.5567 \| 0.5371 \| -- \| -- \| -- \| ## Usage Below is an example of how to use the model with the Hugging Face Transformers library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline import torch checkpoint = "IsmaelMousa/Qwen2.5-3B-Instruct-Books-19K" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") tokenizer = AutoTokenizer .from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint) assistant = pipeline("text-generation", tokenizer=tokenizer, model=model, device=device) question = input("Question : ") reference_answer = input("Reference Answer: ") student_answer = input("Student Answer : ") mark_scheme = input("Mark Scheme : ") system_content = "You are a grading assistant. Evaluate student answers based on the mark scheme. Respond only in JSON format with keys 'score' (int) and 'rationale' (string)." user_content = ("Provide both a score and a rationale by evaluating the student's answer strictly within the mark scheme range," " grading based on how well it meets the question's requirements by comparing the student answer to the reference answer.\n" f"Question: {question}\n" f"Reference Answer: {reference_answer}\n" f"Student Answer: {student_answer}\n" f"Mark Scheme: {mark_scheme}") messages = [{"role": "system", "content": system_content}, {"role": "user", "content": user_content}] inputs = tokenizer.apply_chat_template(messages, tokenize=False) output = assistant(inputs, max_new_tokens=128, do_sample=False, return_full_text=False)[0]["generated_text"] print(output) ``` ### Frameworks - `datasets-3.6.0` - `torch-2.7.0` - `transformers-4.51.3` - `trl-0.17.0` - `scikit-learn-1.6.1` - `bert-score-0.3.13` - `json-repair-0.46.0`
Jyqti/Jyoti	Jyqti	2025-06-08T01:53:56Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-08T01:53:56Z	--- license: other license_name: license license_link: LICENSE ---
kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-4bit	kxdw2580	2025-06-08T01:50:32Z	2	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "GPTQ", "conversational", "zh", "dataset:kxdw2580/catgirl-dataset", "base_model:kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5", "base_model:quantized:kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2025-06-07T06:36:21Z	--- library_name: transformers tags: - GPTQ license: apache-2.0 datasets: - kxdw2580/catgirl-dataset language: - zh base_model: - kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5 --- This is the GPTQ-v2 4-bit quantized version of the model [`kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5`](https://huggingface.co/kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5). During quantization, layers 30–35 exhibited high loss, which can be reviewed in the detailed GPTQ-v2 quantization log . Despite this anomaly, internal small-sample benchmarking indicates that the model's overall performance remains acceptable. For more information about the base model, please refer to the original README. --- # kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5 This new model series integrates updated datasets, base architectures, and fine-tuning methodologies. Based on Qwen3, it includes models with parameter counts of 8B and 1.7B. Key updates focus on daily conversations, creative generation, basic mathematics, and code generation. Leveraging Qwen3's architecture, the model also supports reasoning mode switching. 🔍 Fine-tuning records are available on SwanLab: 1. [First Fine-tuning](https://swanlab.cn/@shadow01a/qwen-catgirl/runs/pcxfkgosz2e0cb430jk0a/overview) 2. [Second Fine-tuning](https://swanlab.cn/@shadow01a/qwen-catgirl/runs/iuou1xratkvbiv7jxw16k/overview) 3. [Third Fine-tuning](https://swanlab.cn/@shadow01a/qwen-catgirl/runs/9i2l4mc5qevmnlx2h51m0/overview) --- ## Evaluation Due to the model's unique characteristics, we employed human evaluation for daily conversations and DeepSeek-R1 scoring (with reference answers provided in advance) for other domains to ensure character consistency and response validity. ### Key Improvements (vs. internal test models "0501" and "0531-test-all"): - Stronger detail-awareness in casual dialogue - More coherent storytelling in creative tasks - Deeper reasoning during thinking mode - Better persona adherence in long-form conversations without explicit prompts - Significant gains in math/code domains (internal 20-question benchmark): \| Model \| Math (Single Attempt) \| Code (Single Attempt) \| \|-------\|-----------------------\|-----------------------\| \| Internal Test Model-0501 \| 10% \| 0% \| \| DeepSeek-R1-0528-Qwen3-8B-Catgirl-0531-test-all \| 30% \| 20% \| \| DeepSeek-R1-0528-Qwen3-8B-Catgirl-v2.5 \| 70% \| 60% \| --- ## Usage Guidelines ### Recommended Parameters: - `temperature`: 0.7 (reasoning mode) / 0.6 (standard mode) - `top_p`: 0.95 ### Critical Notes: - Avoid using model's reasoning chains as conversation context - Inherits base model's tendency for lengthy reasoning in some cases – allow completion even if intermediate steps seem unusual ### English Mode: Add this system prompt for English responses: ``` You are a catgirl. Please speak English. ``` --- ## Acknowledgments Special thanks to: - LLaMA-Factory (fine-tuning framework) - Qwen Team (base model provider) - DeepSeek Team (DeepSeek-R1 evaluation support)
AliSerwat/medgemma-4b-it-sft-lora-crc100k	AliSerwat	2025-06-08T01:47:19Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "endpoints_compatible", "region:us" ]	null	2025-06-08T01:29:16Z	--- base_model: google/medgemma-4b-it library_name: transformers model_name: medgemma-4b-it-sft-lora-crc100k tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for medgemma-4b-it-sft-lora-crc100k This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="AliSerwat/medgemma-4b-it-sft-lora-crc100k", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - 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}} } ```
FrenzyBiscuit/The-Omega-Directive-M-12B-Unslop-v2.0	FrenzyBiscuit	2025-06-08T01:44:35Z	0	0	null	[ "safetensors", "mistral", "base_model:ReadyArt/The-Omega-Directive-M-12B-Unslop-v2.0", "base_model:quantized:ReadyArt/The-Omega-Directive-M-12B-Unslop-v2.0", "4-bit", "awq", "region:us" ]	null	2025-06-08T01:41:06Z	--- base_model: ReadyArt/The-Omega-Directive-M-12B-Unslop-v2.0 base_model_relation: quantized quantized_by: FrenzyBiscuit --- AWQ quant by FrenzyBiscuit. Model was quantized down to INT4 using GEMM kernels, with zero-point quantization and a group size of 64. I have not tested this quant. <img src="./frenzy.png"></img>
kz919/DeepSeek-R1-Distill-Qwen-1.5B-GRPO	kz919	2025-06-08T01:42:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:open-r1/OpenR1-Math-220k", "arxiv:2402.03300", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T22:00:23Z	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B datasets: open-r1/OpenR1-Math-220k library_name: transformers model_name: DeepSeek-R1-Distill-Qwen-1.5B-GRPO tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for DeepSeek-R1-Distill-Qwen-1.5B-GRPO This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) 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="kz919/DeepSeek-R1-Distill-Qwen-1.5B-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/kl2/c_optim_rl/runs/ka61pv7n) 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: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
keita-origin/Ka-1.0	keita-origin	2025-06-08T01:41:32Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-06-07T14:32:22Z	--- 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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dvalishvili-vs-sean-omalley-2-live-video/ufc.316.live.stream.tv	dvalishvili-vs-sean-omalley-2-live-video	2025-06-08T01:41:16Z	0	0	null	[ "region:us" ]	null	2025-06-08T01:40:23Z	<a rel="nofollow" href="https://tinyurl.com/3tfpxeub?ufc.tv">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► UFC 316 LIVE Free</a> <a rel="nofollow" href="https://tinyurl.com/3tfpxeub?ufc.tv">🔴 CLICK HERE 🌐==►► UFC 316 Live Now)</a> <a rel="nofollow" href="https://tinyurl.com/3tfpxeub?ufc.tv"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf	RichardErkhov	2025-06-08T01:40:01Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-08T00:31:41Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) final_test_2_original_recipe - GGUF - Model creator: https://huggingface.co/mergekit-community/ - Original model: https://huggingface.co/mergekit-community/final_test_2_original_recipe/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [final_test_2_original_recipe.Q2_K.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [final_test_2_original_recipe.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [final_test_2_original_recipe.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [final_test_2_original_recipe.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [final_test_2_original_recipe.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [final_test_2_original_recipe.Q3_K.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [final_test_2_original_recipe.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [final_test_2_original_recipe.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [final_test_2_original_recipe.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [final_test_2_original_recipe.Q4_0.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [final_test_2_original_recipe.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [final_test_2_original_recipe.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [final_test_2_original_recipe.Q4_K.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [final_test_2_original_recipe.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [final_test_2_original_recipe.Q4_1.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [final_test_2_original_recipe.Q5_0.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [final_test_2_original_recipe.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [final_test_2_original_recipe.Q5_K.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [final_test_2_original_recipe.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [final_test_2_original_recipe.Q5_1.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [final_test_2_original_recipe.Q6_K.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [final_test_2_original_recipe.Q8_0.gguf](https://huggingface.co/RichardErkhov/mergekit-community_-_final_test_2_original_recipe-gguf/blob/main/final_test_2_original_recipe.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- base_model: - Undi95/Llama3-Unholy-8B-OAS - ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2 - Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 - Undi95/Meta-Llama-3.1-8B-Claude - vicgalle/Humanish-Roleplay-Llama-3.1-8B - mergekit-community/mergekit-della_linear-uogzotg - ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3 - Skywork/Skywork-o1-Open-Llama-3.1-8B - Solshine/reflection-llama-3.1-8B - ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1 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 della_linear merge method using [Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2](https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2) as a base. ### Models Merged The following models were included in the merge: * [Undi95/Llama3-Unholy-8B-OAS](https://huggingface.co/Undi95/Llama3-Unholy-8B-OAS) * [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2) * [Undi95/Meta-Llama-3.1-8B-Claude](https://huggingface.co/Undi95/Meta-Llama-3.1-8B-Claude) * [vicgalle/Humanish-Roleplay-Llama-3.1-8B](https://huggingface.co/vicgalle/Humanish-Roleplay-Llama-3.1-8B) * [mergekit-community/mergekit-della_linear-uogzotg](https://huggingface.co/mergekit-community/mergekit-della_linear-uogzotg) * [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3) * [Skywork/Skywork-o1-Open-Llama-3.1-8B](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B) * [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B) * [ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1](https://huggingface.co/ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3 parameters: density: 0.5 weight: 0.6 - model: Solshine/reflection-llama-3.1-8B parameters: density: 0.5 weight: 0.6 - model: Skywork/Skywork-o1-Open-Llama-3.1-8B parameters: density: 0.5 weight: 0.2 - model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.2 parameters: density: 0.8 weight: 0.6 - model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1 parameters: density: 0.8 weight: 0.6 - model: Undi95/Llama3-Unholy-8B-OAS parameters: density: 0.5 weight: 0.5 - model: vicgalle/Humanish-Roleplay-Llama-3.1-8B parameters: density: 0.5 weight: 0.5 - model: Undi95/Meta-Llama-3.1-8B-Claude parameters: density: 0.5 weight: 0.2 - model: mergekit-community/mergekit-della_linear-uogzotg parameters: density: 0.5 weight: 0.2 merge_method: della_linear base_model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 parameters: normalize: false int8_mask: true dtype: float16 ```
IsmaelMousa/Qwen2.5-0.5B-Instruct-EngSaf-835K	IsmaelMousa	2025-06-08T01:37:09Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "en", "dataset:EngSAF", "arxiv:2407.12818", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T19:46:28Z	--- library_name: transformers tags: - trl - sft license: apache-2.0 base_model: Qwen/Qwen2.5-0.5B-Instruct datasets: - EngSAF metrics: - accuracy - f1 - precision - recall - cohen_kappa - rmse model-index: - name: Qwen2.5-0.5B-Instruct-EngSAF-835K results: - task: name: Text Generation type: text-generation dataset: name: EngSAF type: EngSAF config: EngSAF split: train args: EngSAF metrics: - name: Accuracy type: accuracy value: 0.3600 - name: F1 type: f1 value: 0.3246 - name: Precision type: precision value: 0.3211 - name: Recall type: recall value: 0.4101 - name: Cohen Kappa type: cohen_kappa value: 0.0828 - name: RMSE type: rmse value: 1.0863 language: - en pipeline_tag: text-generation --- # Qwen2.5-0.5B-Instruct-EngSaf-835K This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the EngSAF dataset for Essay Grading. - Workflow: GitHub Repository: [https://github.com/IsmaelMousa/automatic-essay-grading](https://github.com/IsmaelMousa/automatic-essay-grading). - Base Model: Qwen2.5-0.5B-Instruct: [https://doi.org/10.48550/arXiv.2412.15115](https://doi.org/10.48550/arXiv.2412.15115). - Fine-tuning Dataset: EngSAF-835K: [https://github.com/IsmaelMousa/EngSAF/835K](https://github.com/IsmaelMousa/automatic-essay-grading/blob/main/data/engsaf/clean/train/4735_entries.csv). - Task: Automatic Essay Grading (Text Generation). [![Report](https://img.shields.io/badge/W&B_Report-gray?logo=weightsandbiases&logoColor=yellow)](https://api.wandb.ai/links/ismael-amjad/783p4r3l) ## Dataset The EngSAF dataset, in its raw and unprocessed form, consists of approximately 5,800 short-answer responses collected from real-life engineering examinations administered at a reputed academic institute. These responses are spread across 119 unique questions drawn from a wide range of engineering disciplines, making the dataset both diverse and domain-specific. Each data point includes a student’s answer and an associated human-annotated score, serving as a benchmark for evaluating automated grading models. The dataset is divided into three primary subsets: 70% is allocated for training, 16% is reserved for evaluation on unseen answers (UA), and 14% is dedicated to evaluating performance on entirely new questions (UQ). At this stage, it is important to note that the dataset is considered in its original state; no preprocessing, transformation, or filtering has yet been applied. All subsequent improvements and refinements to the data will be described in later sections. This dataset is known as EngSAF version 1.0 and was introduced in the paper titled "I understand why I got this grade": Automatic Short Answer Grading (ASAG) with Feedback, authored by Aggarwal et al., and set to appear in the proceedings of AIED 2025. The dataset is released strictly for academic and research purposes; any commercial use or redistribution without explicit permission is prohibited. Researchers are also urged to avoid publicly disclosing any sensitive content that may be contained in the dataset. For more details, the paper can be accessed at: [https://arxiv.org/abs/2407.12818](https://arxiv.org/abs/2407.12818). ## Modeling The modeling approach for this study was carefully designed to evaluate the performance of different large language models (LLMs) on the automated essay grading task. We selected the Qwen2.5 architecture to represent a range of model sizes: 0.5B, 1.5B, and 3B. Each model was instruction-tuned on the EngSAF dataset in varying sizes, with hyperparameters optimized to balance computational efficiency and performance. The experiments were conducted on GPU-accelerated hardware, leveraging techniques such as gradient checkpointing, flash attention, and mixed-precision training to maximize resource utilization. ## Evaluation The evaluation methodology employed both quantitative metrics and qualitative analysis. For quantitative assessment, we computed accuracy, precision, recall, F1 score, root mean squared error (RMSE), and Cohen's kappa score (CKS) for the scoring task, while using BERT-Score precision, recall, and F1 for rationale evaluation. On a held-out test set of 100 samples. Qualitative examination of models' outputs revealed cases where most of the models correctly identified key aspects of student answers but sometimes failed to properly align its scoring with the rubric criteria. ### Evaluation results for `score` and `rationale` outputs: \| Aspect \| F1 \| Precision \| Recall \| Accuracy \| CKS \| RMSE \| \|:----------:\|:------:\|:-------------:\|:----------:\|:------------:\|:-------:\|:--------:\| \| Score \| 0.3246 \| 0.3211 \| 0.4101 \| 0.3600 \| 0.0828 \| 1.0863 \| \| Rationale \| 0.6153 \| 0.6112 \| 0.6234 \| -- \| -- \| -- \| ## Usage Below is an example of how to use the model with the Hugging Face Transformers library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline import torch checkpoint = "IsmaelMousa/Qwen2.5-0.5B-Instruct-EngSaf-835K" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") tokenizer = AutoTokenizer .from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint) assistant = pipeline("text-generation", tokenizer=tokenizer, model=model, device=device) question = input("Question : ") reference_answer = input("Reference Answer: ") student_answer = input("Student Answer : ") mark_scheme = input("Mark Scheme : ") system_content = "You are a grading assistant. Evaluate student answers based on the mark scheme. Respond only in JSON format with keys 'score' (int) and 'rationale' (string)." user_content = ("Provide both a score and a rationale by evaluating the student's answer strictly within the mark scheme range," " grading based on how well it meets the question's requirements by comparing the student answer to the reference answer.\n" f"Question: {question}\n" f"Reference Answer: {reference_answer}\n" f"Student Answer: {student_answer}\n" f"Mark Scheme: {mark_scheme}") messages = [{"role": "system", "content": system_content}, {"role": "user", "content": user_content}] inputs = tokenizer.apply_chat_template(messages, tokenize=False) output = assistant(inputs, max_new_tokens=128, do_sample=False, return_full_text=False)[0]["generated_text"] print(output) ``` ### Frameworks - `datasets-3.6.0` - `torch-2.7.0` - `transformers-4.51.3` - `trl-0.17.0` - `scikit-learn-1.6.1` - `bert-score-0.3.13` - `json-repair-0.46.0`

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