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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
VIDEOS-18-durefishan-Viral-Video/FULL.VIDEO.Dure.Fishan.Viral.Video.Tutorial.Official	VIDEOS-18-durefishan-Viral-Video	2025-06-16T12:23:41Z	0	0	null	[ "region:us" ]	null	2025-06-16T12:23:19Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
dllmpg/ppo-0.3	dllmpg	2025-06-16T12:22:27Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T12:21:55Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -183.29 +/- 55.39 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'notebook' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.3 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'dllmpg/ppo-0.3' 'batch_size': 512 'minibatch_size': 128} ```
LakshGupta/Taxi-v3	LakshGupta	2025-06-16T12:22:04Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T12:22:02Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="LakshGupta/Taxi-v3", 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"]) ```
TV-Shruthi-Narayanan-Viral-Videos/FULL.VIDEO.Shruthi.Narayanan.Viral.Video.Tutorial.Official	TV-Shruthi-Narayanan-Viral-Videos	2025-06-16T12:21:42Z	0	0	null	[ "region:us" ]	null	2025-06-16T12:21:34Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
Trupthi/cnn_news_summary_model_trained_on_reduced_data	Trupthi	2025-06-16T12:21:08Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-16T10:24:00Z	--- library_name: transformers license: apache-2.0 base_model: t5-small tags: - generated_from_trainer model-index: - name: cnn_news_summary_model_trained_on_reduced_data 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. --> # cnn_news_summary_model_trained_on_reduced_data This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Generated Length \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------------:\| \| No log \| 1.0 \| 144 \| 1.9070 \| 0.2375 \| 0.0951 \| 0.194 \| 0.194 \| 20.0 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
mlx-community/Falcon-H1-0.5B-Instruct-8bit	mlx-community	2025-06-16T12:17:57Z	2	0	mlx	[ "mlx", "safetensors", "falcon_h1", "falcon-h1", "text-generation", "conversational", "base_model:tiiuae/Falcon-H1-0.5B-Instruct", "base_model:quantized:tiiuae/Falcon-H1-0.5B-Instruct", "license:other", "8-bit", "region:us" ]	text-generation	2025-06-14T15:05:03Z	--- library_name: mlx tags: - falcon-h1 - mlx license: other license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html base_model: tiiuae/Falcon-H1-0.5B-Instruct inference: true pipeline_tag: text-generation --- # mlx-community/Falcon-H1-0.5B-Instruct-8bit This model [mlx-community/Falcon-H1-0.5B-Instruct-8bit](https://huggingface.co/mlx-community/Falcon-H1-0.5B-Instruct-8bit) was converted to MLX format from [tiiuae/Falcon-H1-0.5B-Instruct](https://huggingface.co/tiiuae/Falcon-H1-0.5B-Instruct) using mlx-lm version 0.25.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Falcon-H1-0.5B-Instruct-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
aieng-lab/ModernBERT-base_story-points	aieng-lab	2025-06-16T12:17:36Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "en", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T12:17:28Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - answerdotai/ModernBERT-base pipeline_tag: text-classification --- # ModernBERT base for estimating story points This model estimates story points as a numerical value. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
mlx-community/Falcon-H1-0.5B-Instruct-5bit	mlx-community	2025-06-16T12:17:32Z	2	0	mlx	[ "mlx", "safetensors", "falcon_h1", "falcon-h1", "text-generation", "conversational", "base_model:tiiuae/Falcon-H1-0.5B-Instruct", "base_model:quantized:tiiuae/Falcon-H1-0.5B-Instruct", "license:other", "5-bit", "region:us" ]	text-generation	2025-06-14T14:58:54Z	--- library_name: mlx tags: - falcon-h1 - mlx license: other license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html base_model: tiiuae/Falcon-H1-0.5B-Instruct inference: true pipeline_tag: text-generation --- # mlx-community/Falcon-H1-0.5B-Instruct-5bit This model [mlx-community/Falcon-H1-0.5B-Instruct-5bit](https://huggingface.co/mlx-community/Falcon-H1-0.5B-Instruct-5bit) was converted to MLX format from [tiiuae/Falcon-H1-0.5B-Instruct](https://huggingface.co/tiiuae/Falcon-H1-0.5B-Instruct) using mlx-lm version 0.25.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Falcon-H1-0.5B-Instruct-5bit") 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) ```
mlx-community/Falcon-H1-0.5B-Instruct-4bit	mlx-community	2025-06-16T12:16:57Z	6	0	mlx	[ "mlx", "safetensors", "falcon_h1", "falcon-h1", "text-generation", "conversational", "base_model:tiiuae/Falcon-H1-0.5B-Instruct", "base_model:quantized:tiiuae/Falcon-H1-0.5B-Instruct", "license:other", "4-bit", "region:us" ]	text-generation	2025-06-14T14:55:24Z	--- library_name: mlx tags: - falcon-h1 - mlx license: other license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html base_model: tiiuae/Falcon-H1-0.5B-Instruct inference: true pipeline_tag: text-generation --- # mlx-community/Falcon-H1-0.5B-Instruct-4bit This model [mlx-community/Falcon-H1-0.5B-Instruct-4bit](https://huggingface.co/mlx-community/Falcon-H1-0.5B-Instruct-4bit) was converted to MLX format from [tiiuae/Falcon-H1-0.5B-Instruct](https://huggingface.co/tiiuae/Falcon-H1-0.5B-Instruct) using mlx-lm version 0.25.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Falcon-H1-0.5B-Instruct-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) ```
aieng-lab/roberta-large_story-points	aieng-lab	2025-06-16T12:16:49Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "en", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T12:16:33Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - roberta-large pipeline_tag: text-classification --- # RoBERTa large for estimating story points This model estimates story points as a numerical value. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [roberta-large](https://huggingface.co/roberta-large) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
LakshGupta/q-FrozenLake-v1-4x4-noSlippery	LakshGupta	2025-06-16T12:16:44Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T12:16:41Z	--- 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="LakshGupta/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"]) ```
dllmpg/ppo-0.2	dllmpg	2025-06-16T12:14:50Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T11:55:44Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -221.45 +/- 187.38 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'notebook' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'dllmpg/ppo-0.2' 'batch_size': 512 'minibatch_size': 128} ```
TekbotRobotics/Cotonou_VLA_flags_sorting	TekbotRobotics	2025-06-16T12:14:19Z	0	1	lerobot	[ "lerobot", "safetensors", "robotics", "dataset:TekbotRobotics/svla_so101_pickplace_flags_sorting", "region:us" ]	robotics	2025-06-15T09:18:07Z	--- pipeline_tag: robotics tags: - lerobot library_name: lerobot datasets: - TekbotRobotics/svla_so101_pickplace_flags_sorting --- ## SmolVLA: A vision-language-action model for affordable and efficient robotics Resources and technical documentation: [Train using Google Colab Notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb#scrollTo=ZO52lcQtxseE) [SmolVLA HF Documentation](https://huggingface.co/docs/lerobot/smolvla) Designed by Tekbot Robotics and Inspired from Hugging Face. This model was finetuned on [hugging Face base model](https://huggingface.co/lerobot/smolvla_base/). Before proceeding to the next steps, you need to properly install the environment by following [Installation Guide](https://huggingface.co/docs/lerobot/installation) on the docs. Install smolvla extra dependencies: ```bash pip install -e ".[smolvla]" ``` Example of finetuning the smolvla pretrained model (`smolvla_base`): ```bash python lerobot/scripts/train.py \ --policy.path=lerobot/smolvla_base \ --dataset.repo_id=TekbotRobotics/svla_so101_pickplace_flags_sorting \ --batch_size=8 \ --steps=2000 \ --output_dir=outputs/train/my_smolvla \ --job_name=my_smolvla_training \ --policy.device=cuda \ --wandb.enable=true ```
chamizzu/emotion_classifier_roberta	chamizzu	2025-06-16T12:14:01Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T12:13:51Z	--- library_name: transformers license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: emotion_classifier_roberta 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. --> # emotion_classifier_roberta This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2388 - Macro F1: 0.3920 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Macro F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.1963 \| 1.0 \| 5227 \| 0.1933 \| 0.3603 \| \| 0.1866 \| 2.0 \| 10454 \| 0.1956 \| 0.3795 \| \| 0.1752 \| 3.0 \| 15681 \| 0.1956 \| 0.4092 \| \| 0.1696 \| 4.0 \| 20908 \| 0.2009 \| 0.4037 \| \| 0.1575 \| 5.0 \| 26135 \| 0.2076 \| 0.4142 \| \| 0.1492 \| 6.0 \| 31362 \| 0.2175 \| 0.4077 \| \| 0.139 \| 7.0 \| 36589 \| 0.2297 \| 0.3932 \| \| 0.1274 \| 8.0 \| 41816 \| 0.2388 \| 0.3920 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
JemmieX/wqewqewq	JemmieX	2025-06-16T12:11:38Z	0	0	null	[ "fill-mask", "af", "license:bigscience-bloom-rail-1.0", "region:us" ]	fill-mask	2025-06-09T06:48:27Z	--- license: bigscience-bloom-rail-1.0 language: - af new_version: ResembleAI/chatterbox pipeline_tag: fill-mask --- 大手大脚四大家爱送到家受打击撒登记 ##金丝啊打击撒基地阿金受打击阿斯登记阿斯达 #久啊四大家手机嗲时间打死登记
Shubham-gupta-Hs/wATCH.Shubham.gupta.viral.video.original	Shubham-gupta-Hs	2025-06-16T12:10:09Z	0	0	null	[ "region:us" ]	null	2025-06-16T12:09:59Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
Triangle104/Huihui-MoE-24B-A8B-abliterated-Q8_0-GGUF	Triangle104	2025-06-16T12:09:48Z	0	0	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:58:22Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-24B-A8B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Triangle104/Huihui-MoE-24B-A8B-abliterated-Q8_0-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-24B-A8B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-24B-A8B-abliterated) 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/huihui-ai/Huihui-MoE-24B-A8B-abliterated) for more details on the model. --- Huihui-MoE-24B-A8B-abliterated is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the huihui-ai/Qwen3-8B-abliterated base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 4 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications. This model combines four ablated models, and perhaps it can achieve the performance of all the ablated models? This is just a test. The exploration of merging different manifestations of models of the same type is another possibility. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q8_0-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q8_0-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q8_0-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q8_0-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q8_0.gguf -c 2048 ```
Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0616-Fork	Alvin-LiuJia	2025-06-16T12:09:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T12:07:28Z	--- base_model: unsloth/deepseek-r1-distill-qwen-1.5b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Alvin-LiuJia - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-qwen-1.5b-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Shubham-Gupta-Official-Viral-Videos/FULL.VIDEO.Shubham.Gupta.Viral.Video.Tutorial.Official	Shubham-Gupta-Official-Viral-Videos	2025-06-16T12:09:27Z	0	0	null	[ "region:us" ]	null	2025-06-16T12:09:15Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
mradermacher/RZN-T-GGUF	mradermacher	2025-06-16T12:08:49Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:phronetic-ai/RZN-T", "base_model:quantized:phronetic-ai/RZN-T", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T12:05:14Z	--- base_model: phronetic-ai/RZN-T 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/phronetic-ai/RZN-T <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.Q3_K_M.gguf) \| Q3_K_M \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.Q4_K_S.gguf) \| Q4_K_S \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.Q4_K_M.gguf) \| Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.Q6_K.gguf) \| Q6_K \| 0.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.Q8_0.gguf) \| Q8_0 \| 0.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/RZN-T-GGUF/resolve/main/RZN-T.f16.gguf) \| f16 \| 1.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
23ikram/llama3bmerged	23ikram	2025-06-16T12:08:45Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:56:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/zindi-clinical-reasoning-t5-base-GGUF	mradermacher	2025-06-16T12:05:26Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "base_model:Skier8402/zindi-clinical-reasoning-t5-base", "base_model:quantized:Skier8402/zindi-clinical-reasoning-t5-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T12:03:31Z	--- base_model: Skier8402/zindi-clinical-reasoning-t5-base language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Skier8402/zindi-clinical-reasoning-t5-base <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q5_K_S.gguf) \| Q5_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q5_K_M.gguf) \| Q5_K_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q6_K.gguf) \| Q6_K \| 0.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q8_0.gguf) \| Q8_0 \| 0.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.f16.gguf) \| f16 \| 0.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
VIRAL-Video-two-wolf-one-girl-Video/FULL.VIDEO.two.wolf.one.girl.Viral.Video.Tutorial.Official	VIRAL-Video-two-wolf-one-girl-Video	2025-06-16T12:04:02Z	0	0	null	[ "region:us" ]	null	2025-06-16T12:03:56Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
ahamedddd/showBranchesNLPDistilBertBaseUncased	ahamedddd	2025-06-16T12:03:19Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T12:02:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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jaeyong2/Qwen3-0.6B-DPO-Ja-Peft	jaeyong2	2025-06-16T12:03:11Z	24	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "ja", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-14T13:01:59Z	--- library_name: transformers license: apache-2.0 language: - ja --- ## Evaluation ``` !lm_eval --model hf \ --model_args pretrained=jaeyong2/Qwen3-0.6B-DPO-Ja-Peft \ --tasks mmlu,japanese_leaderboard,gsm8k \ --device cuda:0 \ --batch_size 1 \ --num_fewshot 5 ``` \| \| Qwen3-0.6B-DPO-ja \| Qwen3-0.6B \| \|:------------------------------\|----------------------:\|----------------------:\| \| MMLU \| 0.41 \| 0.40 \| \| ja_leaderboard_jaqket_v2 \| 0.30 \| 0.28 \| \| ja_leaderboard_jcommonsenseqa \| 0.45 \| 0.44 \| \| ja_leaderboard_jnli \| 0.24 \| 0.26 \| \| ja_leaderboard_jsquad \| 0.49 \| 0.48 \| \| ja_leaderboard_marc_ja \| 0.87 \| 0.86 \| \| ja_leaderboard_mgsm \| 0.12 \| 0.11 \| \| ja_leaderboard_xlsum \| 0.09 \| 0.08 \| \| ja_leaderboard_xwinograd \| 0.55 \| 0.55 \| \| GSM8K \| 0.44 \| 0.42 \| ## License - Qwen/Qwen3-0.6B : https://choosealicense.com/licenses/apache-2.0/ ## Acknowledgement This research is supported by TPU Research Cloud program.
TareksLab/Tesseract-DT-LLaMa-70B	TareksLab	2025-06-16T12:02:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "base_model:TareksLab/Fifth-Dimension-V1-LLaMa-70B", "base_model:merge:TareksLab/Fifth-Dimension-V1-LLaMa-70B", "base_model:TareksLab/First-Dimension-V1-LLaMa-70B", "base_model:merge:TareksLab/First-Dimension-V1-LLaMa-70B", "base_model:TareksLab/Second-Dimension-V1-LLaMa-70B", "base_model:merge:TareksLab/Second-Dimension-V1-LLaMa-70B", "base_model:TareksLab/Sixth-Dimension-V1-LLaMa-70B", "base_model:merge:TareksLab/Sixth-Dimension-V1-LLaMa-70B", "base_model:TareksLab/Third-Dimension-V1-LLaMa-70B", "base_model:merge:TareksLab/Third-Dimension-V1-LLaMa-70B", "base_model:TareksLab/Zero-Dimension-F-LLaMa-70B", "base_model:merge:TareksLab/Zero-Dimension-F-LLaMa-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:44:02Z	--- base_model: - TareksLab/Third-Dimension-V1-LLaMa-70B - TareksLab/Sixth-Dimension-V1-LLaMa-70B - TareksLab/First-Dimension-V1-LLaMa-70B - TareksLab/Zero-Dimension-F-LLaMa-70B - TareksLab/Second-Dimension-V1-LLaMa-70B - TareksLab/Fifth-Dimension-V1-LLaMa-70B library_name: transformers tags: - mergekit - merge --- # MERGE3 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 TIES](https://arxiv.org/abs/2311.03099) merge method using [TareksLab/Second-Dimension-V1-LLaMa-70B](https://huggingface.co/TareksLab/Second-Dimension-V1-LLaMa-70B) as a base. ### Models Merged The following models were included in the merge: * [TareksLab/Third-Dimension-V1-LLaMa-70B](https://huggingface.co/TareksLab/Third-Dimension-V1-LLaMa-70B) * [TareksLab/Sixth-Dimension-V1-LLaMa-70B](https://huggingface.co/TareksLab/Sixth-Dimension-V1-LLaMa-70B) * [TareksLab/First-Dimension-V1-LLaMa-70B](https://huggingface.co/TareksLab/First-Dimension-V1-LLaMa-70B) * [TareksLab/Zero-Dimension-F-LLaMa-70B](https://huggingface.co/TareksLab/Zero-Dimension-F-LLaMa-70B) * [TareksLab/Fifth-Dimension-V1-LLaMa-70B](https://huggingface.co/TareksLab/Fifth-Dimension-V1-LLaMa-70B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TareksLab/Sixth-Dimension-V1-LLaMa-70B parameters: weight: [0.1, 0.1, 0.1, 0.2, 0.5] density: 0.5 - model: TareksLab/Fifth-Dimension-V1-LLaMa-70B parameters: weight: [0.1, 0.1, 0.2, 0.4, 0.2] density: 0.5 - model: TareksLab/Zero-Dimension-F-LLaMa-70B parameters: weight: [0.1, 0.2, 0.4, 0.2, 0.1] density: 0.5 - model: TareksLab/Third-Dimension-V1-LLaMa-70B parameters: weight: [0.2, 0.4, 0.2, 0.1, 0.1] density: 0.5 - model: TareksLab/First-Dimension-V1-LLaMa-70B parameters: weight: [0.5, 0.2, 0.1, 0.1, 0.1] density: 0.5 merge_method: dare_ties base_model: TareksLab/Second-Dimension-V1-LLaMa-70B parameters: normalize: false dtype: float32 out_dtype: bfloat16 chat_template: llama3 tokenizer: source: TareksLab/Zero-Dimension-F-LLaMa-70B pad_to_multiple_of: 8 ```
New-tutorial-College-Girls-viral-video/FULL.VIDEO.College.Girls.Viral.Video.Tutorial.Official	New-tutorial-College-Girls-viral-video	2025-06-16T12:01:55Z	0	0	null	[ "region:us" ]	null	2025-06-16T12:01:48Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.05_0.75_epoch1	MinaMila	2025-06-16T12:01:34Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:59:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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robinn6/deepseek-vl-7b-coco-refg-lora-upgraded-bbox	robinn6	2025-06-16T12:01:07Z	0	0	transformers	[ "transformers", "safetensors", "vision", "referring-expression-grounding", "object-detection", "coco-refg", "deepseek", "lora", "peft", "en", "dataset:lmms-lab/RefCOCOg", "base_model:deepseek-ai/deepseek-vl-7b-chat", "base_model:adapter:deepseek-ai/deepseek-vl-7b-chat", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	object-detection	2025-06-16T12:01:00Z	--- license: apache-2.0 base_model: deepseek-ai/deepseek-vl-7b-chat tags: - vision - referring-expression-grounding - object-detection - coco-refg - deepseek - lora - peft datasets: - lmms-lab/RefCOCOg language: - en library_name: transformers pipeline_tag: object-detection metrics: - mean_iou - accuracy model-index: - name: deepseek-vl-7b-coco-refg-lora-upgraded-bbox results: - task: type: referring-expression-grounding name: Referring Expression Grounding dataset: type: RefCOCOg name: COCO-RefG (RefCOCOg) metrics: - type: mean_iou value: 0.2567 name: Mean IoU - type: accuracy value: 12.5% name: Accuracy @ IoU 0.5 --- # deepseek-vl-7b-coco-refg-lora-upgraded-bbox This model is a fine-tuned version of [deepseek-ai/deepseek-vl-7b-chat](https://huggingface.co/deepseek-ai/deepseek-vl-7b-chat) for referring expression grounding on COCO-RefG dataset. ## Model Description - Task: Referring Expression Grounding (REG) - locating objects in images based on natural language descriptions - Base Model: DeepSeek-VL 7B Chat - Training Method: LoRA (Vision + Language) + Custom BBox Head with Position Embeddings - BBox Head Architecture: EnhancedBoundingBoxHead - Dataset: COCO-RefG (RefCOCOg) - Training Samples: 7573 - Validation Samples: 5023 - Output: Bounding box coordinates [x, y, width, height] in normalized format [0,1] ## Performance \| Metric \| Value \| \|--------\|-------\| \| Mean IoU \| 0.2567 \| \| Accuracy @ IoU 0.5 \| 12.5% \| \| Accuracy @ IoU 0.75 \| 2.3% \| \| Evaluated Samples \| 5023 \| ## Usage ```python import torch from transformers import AutoModelForCausalLM from deepseek_vl.models import VLChatProcessor from deepseek_vl.utils.io import load_pil_images from PIL import Image import requests # Load the model device = torch.device("cuda" if torch.cuda.is_available() else "cpu") processor = VLChatProcessor.from_pretrained("deepseek-ai/deepseek-vl-7b-chat") model = AutoModelForCausalLM.from_pretrained( "deepseek-ai/deepseek-vl-7b-chat", torch_dtype=torch.float16, trust_remote_code=True, device_map="auto" ) # Load LoRA weights if available if lora_applied: from peft import PeftModel model = PeftModel.from_pretrained(model, "robinn6/deepseek-vl-7b-coco-refg-lora-upgraded-bbox/lora_weights") # Load the bbox head from bbox_head_model import LightweightBBoxHead bbox_head = LightweightBBoxHead(model.language_model.config.hidden_size, mid_dim=512) checkpoint = torch.load("robinn6/deepseek-vl-7b-coco-refg-lora-upgraded-bbox/bbox_head/pytorch_model.bin", map_location=device) bbox_head.load_state_dict(checkpoint['model_state_dict']) bbox_head.to(device) bbox_head.eval() # Prepare input image_path = "path/to/your/image.jpg" referring_expression = "the red car on the left" conversation = [ { "role": "User", "content": f"<image_placeholder>Where is {referring_expression}?", "images": [image_path], }, {"role": "Assistant", "content": ""}, ] # Process and predict pil_images = load_pil_images(conversation) inputs = processor(conversations=conversation, images=pil_images, force_batchify=True) inputs = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()} with torch.no_grad(): embeds = model.prepare_inputs_embeds(inputs) outputs = model.language_model.model( inputs_embeds=embeds, attention_mask=inputs.get("attention_mask"), output_hidden_states=True ) hidden_states = outputs.hidden_states[-1] last_token_hidden = hidden_states[:, -1, :] bbox_pred = bbox_head(last_token_hidden) # bbox_pred contains [x, y, width, height] in normalized coordinates x, y, w, h = bbox_pred[0].cpu().numpy() print(f"Bounding box: x={x:.3f}, y={y:.3f}, w={w:.3f}, h={h:.3f}") ``` ## Training Details - Learning Rate: 1e-4 - Optimizer: AdamW - Scheduler: MultiStepLR (milestones: [200, 400, 600], gamma: 0.5) - Loss Function: GIoU Loss (α=3.0) + L1 Loss (β=0.3) - Epochs: 3 - Batch Size: 1 - Training Time**: 239.8 ## Limitations - The model is trained on COCO-RefG dataset and may not generalize well to other domains - Performance may vary depending on the complexity of referring expressions - Bounding box predictions are in normalized coordinates [0,1] ## Citation If you use this model, please cite: ```bibtex @misc{deepseek_vl_7b_coco_refg_lora_upgraded_bbox_2024, title={deepseek-vl-7b-coco-refg-lora-upgraded-bbox: DeepSeek-VL Fine-tuned for Referring Expression Grounding}, author={{HF_USERNAME}}, year={2024}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/robinn6/deepseek-vl-7b-coco-refg-lora-upgraded-bbox}} } ```
mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF	mradermacher	2025-06-16T12:00:16Z	0	0	transformers	[ "transformers", "gguf", "moe", "en", "base_model:huihui-ai/Huihui-MoE-1.2B-A0.6B", "base_model:quantized:huihui-ai/Huihui-MoE-1.2B-A0.6B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T11:52:38Z	--- base_model: huihui-ai/Huihui-MoE-1.2B-A0.6B language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE quantized_by: mradermacher tags: - moe --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/huihui-ai/Huihui-MoE-1.2B-A0.6B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-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/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.Q2_K.gguf) \| Q2_K \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.Q3_K_S.gguf) \| Q3_K_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.Q3_K_M.gguf) \| Q3_K_M \| 0.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.Q3_K_L.gguf) \| Q3_K_L \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.IQ4_XS.gguf) \| IQ4_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.Q4_K_S.gguf) \| Q4_K_S \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.Q4_K_M.gguf) \| Q4_K_M \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.Q5_K_S.gguf) \| Q5_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.Q5_K_M.gguf) \| Q5_K_M \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.Q6_K.gguf) \| Q6_K \| 1.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.Q8_0.gguf) \| Q8_0 \| 1.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-1.2B-A0.6B-GGUF/resolve/main/Huihui-MoE-1.2B-A0.6B.f16.gguf) \| f16 \| 2.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
theSOL1/joseon-documents-translator	theSOL1	2025-06-16T11:58:30Z	0	0	peft	[ "peft", "safetensors", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct", "region:us" ]	null	2025-06-16T11:48:29Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: peft --- # theSOL1/joseon-documents-translator ## Model Details ### Model Description - Developed by: [Subin Park](https://github.com/SOL1archive), [pggggggggh](https://github.com/pggggggggh) - Model type: CLM - Language(s) (NLP): Ancient/Modern Korean - License: MIT License - Finetuned from model [optional]: [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [GitHub Link](https://github.com/SOL1archive/ancient-document-LLM)
YeonjiEllen/Womens-health-AI	YeonjiEllen	2025-06-16T11:57:26Z	0	1	null	[ "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "license:mit", "region:us" ]	null	2025-06-16T11:56:16Z	--- license: mit base_model: - google/medgemma-4b-it --- ### Role You are a knowledgeable and compassionate AI assistant specializing in women's health, focusing on gynecological symptoms and conditions. Your purpose is to help users better understand their symptoms, explain relevant medical terms clearly, and guide them toward appropriate care, all based on established medical information. ### Objectives - When a user describes symptoms (e.g., pelvic pain, irregular menstruation, abnormal discharge), provide general, medically accurate information about possible related conditions. - Explain medical vocabulary and common gynecological terms in simple language that is easy to understand. - Encourage users to seek professional medical evaluation for accurate diagnosis and treatment. - Help users recognize when symptoms could indicate a serious condition needing prompt medical attention. ### Tone and Style - Use warm, empathetic, and supportive language. - Avoid causing unnecessary alarm, while emphasizing the importance of proper medical care. - Use clear, non-technical language suitable for people without medical training. - Always be responsible and honest about the limits of the AI — it does not replace a doctor. ### Medical Accuracy and Safety - Base all responses on established, peer-reviewed medical knowledge and guidelines. - Do not guess or fabricate information. If uncertain, acknowledge the limitation. - Avoid direct diagnoses — always remind users that only a healthcare professional can diagnose and treat. ### Example Interactions User: I have severe menstrual cramps that get worse every month. Bot: Severe menstrual cramps can sometimes be caused by conditions such as endometriosis or uterine fibroids, but many women also experience painful periods without an underlying disease. If your pain is worsening or interfering with daily life, I recommend consulting a gynecologist who can provide a proper diagnosis and treatment options. User: What does “endometriosis” mean? Bot: Endometriosis is a condition where tissue similar to the lining inside the uterus grows outside of it, causing pain and sometimes fertility issues. It can lead to painful periods and pelvic pain. A doctor can diagnose and help manage this condition. ### Rules - Never provide a medical diagnosis or treatment advice. - Always include a disclaimer that the chatbot is for informational purposes only. - If a question is vague or unclear, ask the user for more details. - Encourage seeing a healthcare professional for any concerning symptoms. ### Disclaimer I am an AI language model designed to provide educational information about women's health. I do not provide medical diagnoses or replace professional medical advice. If you have health concerns, please consult a qualified healthcare provider.
fikriau/praktikum-ai-modul6-robertalargefocal	fikriau	2025-06-16T11:56:44Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "multi-label-classification", "emotion-classification", "indonesian", "en", "dataset:emotion", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T05:35:19Z	--- library_name: transformers tags: - text-classification - multi-label-classification - emotion-classification - roberta - indonesian language: en datasets: - emotion --- # Model Klasifikasi Emosi Multi-Label (Praktikum Modul 6) Model ini adalah `roberta-large` yang telah di-fine-tune untuk tugas klasifikasi emosi multi-label pada dataset teks Bahasa Inggris. Model ini mampu mendeteksi satu atau lebih dari 14 emosi yang berbeda dari sebuah teks, dan dikembangkan sebagai bagian dari pengerjaan Praktikum Modul 6 - Mata Kuliah Kecerdasan Buatan dan Pembelajaran Mesin. ## Detail Model ### Deskripsi Model Tujuan utama proyek ini adalah untuk membangun sebuah model yang akurat dalam mengklasifikasikan emosi secara multi-label, di mana sebuah teks bisa memiliki beberapa emosi sekaligus (contoh: 'joy' dan 'excitement'). Model ini dilatih dan dievaluasi berdasarkan kompetisi Kaggle dengan metrik evaluasi utama Macro F1-Score. Untuk mencapai performa terbaik dengan keterbatasan hardware (GPU 16 GB), berbagai teknik optimasi canggih diterapkan selama proses training. - Developed by: [fikriau](https://huggingface.co/fikriau) - Model type: `roberta` - Language(s) (NLP): English (`en`) - License: `apache-2.0` - Finetuned from model: `roberta-large` ### Model Sources - Repository: [https://huggingface.co/fikriau/praktikum-ai-modul6-robertalargefocal](https://huggingface.co/fikriau/praktikum-ai-modul6-robertalargefocal) ## Uses ### Direct Use Model ini dapat digunakan secara langsung untuk prediksi emosi menggunakan `pipeline` dari 🤗 Transformers. ```python from transformers import pipeline pipe = pipeline("text-classification", model="fikriau/praktikum-ai-modul6-robertalargefocal", top_k=None) text = "I can't believe I won the lottery, I'm so happy and excited!" # Hasil prediksi akan memberikan skor untuk semua 14 label results = pipe(text) print(results) # Contoh Output: # [[{'label': 'excitement', 'score': 0.98}, {'label': 'joy', 'score': 0.97}, ...]] ``` ## Bias, Risks, and Limitations Model ini dilatih pada dataset yang disediakan untuk praktikum. Bias yang ada di dalam data tersebut kemungkinan besar juga akan tercermin pada prediksi model. Performa model mungkin akan berbeda pada jenis teks yang sangat berbeda dari data latihnya (misalnya, teks formal, teks teknis, atau dialek yang unik). Pengguna disarankan untuk menyadari potensi bias ini saat menggunakan model. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model_name = "fikriau/praktikum-ai-modul6-robertolargefocal" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) text = "This is the best day of my life, I feel so much gratitude!" inputs = tokenizer(text, return_tensors="pt") with torch.no_grad(): logits = model(inputs).logits # Dapatkan probabilitas dengan sigmoid dan tentukan label dengan threshold 0.5 probabilities = torch.sigmoid(logits) predictions = (probabilities > 0.5).int() # Tampilkan hasil prediksi untuk setiap emosi for i, label in enumerate(model.config.id2label.items()): if predictions[0][i].item() == 1: print(f"Detected Emotion: {label[1]} (Score: {probabilities[0][i]:.4f})") ``` ## Training Details ### Training Data Model ini di-fine-tune pada dataset Emotion Classification yang disediakan untuk kompetisi Kaggle pada Praktikum Modul 6. Dataset ini terdiri dari 46,455 sampel teks berbahasa Inggris, masing-masing dapat memiliki satu atau lebih dari 14 label emosi. Analisis data awal (EDA) menunjukkan adanya ketidakseimbangan kelas (class imbalance) yang signifikan, di mana beberapa emosi seperti `annoyance` jauh lebih sering muncul daripada emosi lain seperti `embarrassment` dan `fear`. Temuan ini menjadi justifikasi utama untuk menggunakan `FocalLoss` sebagai fungsi kerugian (loss function) saat training. Data dibagi menjadi 90% untuk set training dan 10% untuk set validasi. ### Training Procedure Proses training dirancang untuk memaksimalkan performa dengan mengatasi tantangan ketidakseimbangan kelas dan keterbatasan memori GPU (VRAM 16 GB). #### Preprocessing 1. Text Cleaning: Teks input dibersihkan terlebih dahulu untuk menghapus artefak digital seperti URL dan `@username`. Emoji juga diubah menjadi representasi teksnya (misalnya, `😂` -> `:face_with_tears_of_joy:`) untuk mempertahankan sinyal emosionalnya. 2. Tokenization: Teks yang sudah bersih di-tokenisasi menggunakan `RobertaTokenizer` yang sesuai dengan model `roberta-large`. 3. Padding & Truncation: Semua sekuens teks dipotong atau diberi padding hingga panjang seragam `MAX_LENGTH = 128`. Panjang ini dipilih sebagai kompromi terbaik antara menangkap konteks teks dan memastikan model `roberta-large` muat di dalam memori GPU. #### Training Hyperparameters Konfigurasi training menggunakan serangkaian teknik canggih untuk mencapai performa optimal: - Loss Function: `FocalLoss` (dengan parameter default `alpha=0.25`, `gamma=2.0`) digunakan untuk memaksa model lebih fokus pada pembelajaran sampel dari kelas emosi yang jarang muncul. - Training Regime: `fp16` (mixed precision) diaktifkan untuk mengurangi penggunaan memori dan mempercepat komputasi di GPU. - Optimizer: `adamw_8bit` dari library `bitsandbytes` digunakan untuk mengurangi jejak memori dari state optimizer secara signifikan. - Batching Strategy: `per_device_train_batch_size` diatur ke 4, dan `gradient_accumulation_steps` diatur ke 8. Ini menghasilkan batch size efektif sebesar 32 (4 x 8), yang memungkinkan training stabil dengan penggunaan memori yang rendah. - Learning Rate: `2e-5` digunakan sebagai titik awal yang solid untuk fine-tuning model besar. - LR Scheduler: `cosine`, untuk membantu model mencapai konvergensi yang lebih halus. - Epochs: Model dilatih selama 3 epoch**. Evaluasi dilakukan di setiap akhir epoch untuk memonitor performa dan memilih checkpoint terbaik.
alexandertam/babylm-hulda50-10m-roberta-base	alexandertam	2025-06-16T11:54:16Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-16T11:53:40Z	--- library_name: transformers license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: babylm-hulda50-10m-roberta-base 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. --> # babylm-hulda50-10m-roberta-base This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.6004 - Accuracy: 0.1688 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 9000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 10.4051 \| 0.1139 \| 100 \| 9.7325 \| 0.0476 \| \| 9.4597 \| 0.2278 \| 200 \| 9.0953 \| 0.0526 \| \| 8.7922 \| 0.3417 \| 300 \| 8.3668 \| 0.0752 \| \| 8.0662 \| 0.4556 \| 400 \| 7.6392 \| 0.0853 \| \| 7.3196 \| 0.5695 \| 500 \| 6.9607 \| 0.1033 \| \| 6.7508 \| 0.6834 \| 600 \| 6.5195 \| 0.1140 \| \| 6.4206 \| 0.7973 \| 700 \| 6.3211 \| 0.1240 \| \| 6.2816 \| 0.9112 \| 800 \| 6.2062 \| 0.1318 \| \| 6.1826 \| 1.0251 \| 900 \| 6.1258 \| 0.1360 \| \| 6.1021 \| 1.1390 \| 1000 \| 6.0655 \| 0.1389 \| \| 5.7741 \| 2.2779 \| 2000 \| 5.7992 \| 0.1567 \| \| 5.6606 \| 3.4169 \| 3000 \| 5.7170 \| 0.1613 \| \| 5.5853 \| 4.5558 \| 4000 \| 5.6723 \| 0.1629 \| \| 5.579 \| 5.6948 \| 5000 \| 5.6495 \| 0.1645 \| \| 5.5291 \| 6.8337 \| 6000 \| 5.6294 \| 0.1661 \| \| 5.5188 \| 7.9727 \| 7000 \| 5.6190 \| 0.1671 \| \| 5.4853 \| 9.1116 \| 8000 \| 5.6108 \| 0.1680 \| \| 5.5257 \| 10.2506 \| 9000 \| 5.5980 \| 0.1695 \| ### Framework versions - Transformers 4.50.3 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
kicoi/ppo-LunarLander-v2	kicoi	2025-06-16T11:53:32Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T11:14:35Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 252.43 +/- 24.33 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.25_0.25_0.75_epoch1	MinaMila	2025-06-16T11:53:25Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:51:40Z	--- 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]
Triangle104/Huihui-MoE-24B-A8B-abliterated-Q6_K-GGUF	Triangle104	2025-06-16T11:53:01Z	0	0	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:43:31Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-24B-A8B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Triangle104/Huihui-MoE-24B-A8B-abliterated-Q6_K-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-24B-A8B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-24B-A8B-abliterated) 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/huihui-ai/Huihui-MoE-24B-A8B-abliterated) for more details on the model. --- Huihui-MoE-24B-A8B-abliterated is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the huihui-ai/Qwen3-8B-abliterated base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 4 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications. This model combines four ablated models, and perhaps it can achieve the performance of all the ablated models? This is just a test. The exploration of merging different manifestations of models of the same type is another possibility. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q6_K-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q6_K-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q6_K-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q6_K-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q6_k.gguf -c 2048 ```
Shah-Sapna-Kumari-Official-Viral-Videos/FULL.VIDEO.Sapna.Shah.Viral.Video.Tutorial.Official	Shah-Sapna-Kumari-Official-Viral-Videos	2025-06-16T11:52:58Z	0	0	null	[ "region:us" ]	null	2025-06-16T11:52:50Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
dhruvsangani/FeatSystems_QA_Model	dhruvsangani	2025-06-16T11:52:57Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T11:52:45Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: dhruvsangani - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tomaarsen/splade-cocondenser-ensembledistil-nq	tomaarsen	2025-06-16T11:52:22Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sparse-encoder", "sparse", "splade", "generated_from_trainer", "dataset_size:99000", "loss:SpladeLoss", "loss:SparseMultipleNegativesRankingLoss", "loss:FlopsLoss", "feature-extraction", "en", "dataset:sentence-transformers/natural-questions", "arxiv:1908.10084", "arxiv:2205.04733", "arxiv:1705.00652", "arxiv:2004.05665", "base_model:naver/splade-cocondenser-ensembledistil", "base_model:finetune:naver/splade-cocondenser-ensembledistil", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-16T11:52:07Z	--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:99000 - loss:SpladeLoss - loss:SparseMultipleNegativesRankingLoss - loss:FlopsLoss base_model: naver/splade-cocondenser-ensembledistil widget: - text: Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower of the former World Trade Center in New York City. The introduction features Ben Stiller and Stephen Dorff mistaking Fred Durst for the valet and giving him the keys to their Bentley Azure. Also making a cameo is break dancer Mr. Wiggles. The rest of the video has several cuts to Durst and his bandmates hanging out of the Bentley as they drive about Manhattan. The song Ben Stiller is playing at the beginning is "My Generation" from the same album. The video also features scenes of Fred Durst with five girls dancing in a room. The video was filmed around the same time as the film Zoolander, which explains Stiller and Dorff's appearance. Fred Durst has a small cameo in that film. - text: 'Maze Runner: The Death Cure On April 22, 2017, the studio delayed the release date once again, to February 9, 2018, in order to allow more time for post-production; months later, on August 25, the studio moved the release forward two weeks.[17] The film will premiere on January 26, 2018 in 3D, IMAX and IMAX 3D.[18][19]' - text: who played the dj in the movie the warriors - text: Lionel Messi Born and raised in central Argentina, Messi was diagnosed with a growth hormone deficiency as a child. At age 13, he relocated to Spain to join Barcelona, who agreed to pay for his medical treatment. After a fast progression through Barcelona's youth academy, Messi made his competitive debut aged 17 in October 2004. Despite being injury-prone during his early career, he established himself as an integral player for the club within the next three years, finishing 2007 as a finalist for both the Ballon d'Or and FIFA World Player of the Year award, a feat he repeated the following year. His first uninterrupted campaign came in the 2008â€“09 season, during which he helped Barcelona achieve the first treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and FIFA World Player of the Year award by record voting margins. - text: 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman''s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik''s young wife runs away with his son, and Fredrik is finally free to accept Desirée''s offer.[1]' datasets: - sentence-transformers/natural-questions pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 58.16202595921652 energy_consumed: 0.14963140390123159 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.4 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: naver/splade-cocondenser-ensembledistil finetuned on Natural-Questions tuples results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.86 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.22 name: Dot Precision@3 - type: dot_precision@5 value: 0.15600000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.086 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.78 name: Dot Recall@5 - type: dot_recall@10 value: 0.86 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6354826541219594 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5633571428571428 name: Dot Mrr@10 - type: dot_map@100 value: 0.5707568446046707 name: Dot Map@100 - type: query_active_dims value: 80.04000091552734 name: Query Active Dims - type: query_sparsity_ratio value: 0.9973776292210363 name: Query Sparsity Ratio - type: corpus_active_dims value: 187.86317443847656 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9938449913361354 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.38 name: Dot Precision@3 - type: dot_precision@5 value: 0.348 name: Dot Precision@5 - type: dot_precision@10 value: 0.292 name: Dot Precision@10 - type: dot_recall@1 value: 0.06151521998742045 name: Dot Recall@1 - type: dot_recall@3 value: 0.09888886518769423 name: Dot Recall@3 - type: dot_recall@5 value: 0.11905430040543279 name: Dot Recall@5 - type: dot_recall@10 value: 0.15207487151594892 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3662320514229047 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.524547619047619 name: Dot Mrr@10 - type: dot_map@100 value: 0.17259980178609616 name: Dot Map@100 - type: query_active_dims value: 82.4800033569336 name: Query Active Dims - type: query_sparsity_ratio value: 0.9972976868043728 name: Query Sparsity Ratio - type: corpus_active_dims value: 292.8273010253906 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9904060251285831 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.52 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.52 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.5 name: Dot Recall@1 - type: dot_recall@3 value: 0.68 name: Dot Recall@3 - type: dot_recall@5 value: 0.77 name: Dot Recall@5 - type: dot_recall@10 value: 0.8 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6646829822638323 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6318571428571428 name: Dot Mrr@10 - type: dot_map@100 value: 0.6182603061292137 name: Dot Map@100 - type: query_active_dims value: 79.45999908447266 name: Query Active Dims - type: query_sparsity_ratio value: 0.9973966319676144 name: Query Sparsity Ratio - type: corpus_active_dims value: 208.1573028564453 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9931800896777261 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.4533333333333333 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6533333333333333 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7399999999999999 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8133333333333334 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4533333333333333 name: Dot Precision@1 - type: dot_precision@3 value: 0.27999999999999997 name: Dot Precision@3 - type: dot_precision@5 value: 0.22399999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.156 name: Dot Precision@10 - type: dot_recall@1 value: 0.3271717399958068 name: Dot Recall@1 - type: dot_recall@3 value: 0.47962962172923146 name: Dot Recall@3 - type: dot_recall@5 value: 0.5563514334684776 name: Dot Recall@5 - type: dot_recall@10 value: 0.604024957171983 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5554658959362321 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5732539682539682 name: Dot Mrr@10 - type: dot_map@100 value: 0.45387231750666013 name: Dot Map@100 - type: query_active_dims value: 80.66000111897786 name: Query Active Dims - type: query_sparsity_ratio value: 0.9973573159976744 name: Query Sparsity Ratio - type: corpus_active_dims value: 219.49083174763393 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9928087664062764 name: Corpus Sparsity Ratio --- # naver/splade-cocondenser-ensembledistil finetuned on Natural-Questions tuples This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [naver/splade-cocondenser-ensembledistil](https://huggingface.co/naver/splade-cocondenser-ensembledistil) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - Model Type: SPLADE Sparse Encoder - Base model: [naver/splade-cocondenser-ensembledistil](https://huggingface.co/naver/splade-cocondenser-ensembledistil) <!-- at revision 25178a62708a3ab1b5c4b5eb30764d65bfddcfbb --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 30522 dimensions - Similarity Function: Dot Product - Training Dataset: - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Documentation: [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/splade-cocondenser-ensembledistil-nq") # Run inference queries = [ "is send in the clowns from a musical", ] documents = [ 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]', 'The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1]', 'Money in the Bank ladder match The first match was contested in 2005 at WrestleMania 21, after being invented (in kayfabe) by Chris Jericho.[1] At the time, it was exclusive to wrestlers of the Raw brand, and Edge won the inaugural match.[1] From then until 2010, the Money in the Bank ladder match, now open to all WWE brands, became a WrestleMania mainstay. 2010 saw a second and third Money in the Bank ladder match when the Money in the Bank pay-per-view debuted in July. Unlike the matches at WrestleMania, this new event featured two such ladder matches â€“ one each for a contract for the WWE Championship and World Heavyweight Championship, respectively.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 30522] [3, 30522] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[38.2508, 3.8446, 1.6656]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) \| Metric \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| \|:----------------------\|:------------\|:-------------\|:-----------\| \| dot_accuracy@1 \| 0.42 \| 0.42 \| 0.52 \| \| dot_accuracy@3 \| 0.66 \| 0.58 \| 0.72 \| \| dot_accuracy@5 \| 0.78 \| 0.64 \| 0.8 \| \| dot_accuracy@10 \| 0.86 \| 0.74 \| 0.84 \| \| dot_precision@1 \| 0.42 \| 0.42 \| 0.52 \| \| dot_precision@3 \| 0.22 \| 0.38 \| 0.24 \| \| dot_precision@5 \| 0.156 \| 0.348 \| 0.168 \| \| dot_precision@10 \| 0.086 \| 0.292 \| 0.09 \| \| dot_recall@1 \| 0.42 \| 0.0615 \| 0.5 \| \| dot_recall@3 \| 0.66 \| 0.0989 \| 0.68 \| \| dot_recall@5 \| 0.78 \| 0.1191 \| 0.77 \| \| dot_recall@10 \| 0.86 \| 0.1521 \| 0.8 \| \| dot_ndcg@10 \| 0.6355 \| 0.3662 \| 0.6647 \| \| dot_mrr@10 \| 0.5634 \| 0.5245 \| 0.6319 \| \| dot_map@100 \| 0.5708 \| 0.1726 \| 0.6183 \| \| query_active_dims \| 80.04 \| 82.48 \| 79.46 \| \| query_sparsity_ratio \| 0.9974 \| 0.9973 \| 0.9974 \| \| corpus_active_dims \| 187.8632 \| 292.8273 \| 208.1573 \| \| corpus_sparsity_ratio \| 0.9938 \| 0.9904 \| 0.9932 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ] } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.4533 \| \| dot_accuracy@3 \| 0.6533 \| \| dot_accuracy@5 \| 0.74 \| \| dot_accuracy@10 \| 0.8133 \| \| dot_precision@1 \| 0.4533 \| \| dot_precision@3 \| 0.28 \| \| dot_precision@5 \| 0.224 \| \| dot_precision@10 \| 0.156 \| \| dot_recall@1 \| 0.3272 \| \| dot_recall@3 \| 0.4796 \| \| dot_recall@5 \| 0.5564 \| \| dot_recall@10 \| 0.604 \| \| dot_ndcg@10 \| 0.5555 \| \| dot_mrr@10 \| 0.5733 \| \| dot_map@100 \| 0.4539 \| \| query_active_dims \| 80.66 \| \| query_sparsity_ratio \| 0.9974 \| \| corpus_active_dims \| 219.4908 \| \| corpus_sparsity_ratio \| 0.9928 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 99,000 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> \| * Samples: \| query \| answer \| \|:--------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>who played the father in papa don't preach</code> \| <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> \| \| <code>where was the location of the battle of hastings</code> \| <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> \| \| <code>how many puppies can a dog give birth to</code> \| <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> \| * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')", "lambda_corpus": 3e-05, "lambda_query": 5e-05 } ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 1,000 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> \| * Samples: \| query \| answer \| \|:-------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>where is the tiber river located in italy</code> \| <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> \| \| <code>what kind of car does jay gatsby drive</code> \| <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> \| \| <code>who sings if i can dream about you</code> \| <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> \| * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')", "lambda_corpus": 3e-05, "lambda_query": 5e-05 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| NanoMSMARCO_dot_ndcg@10 \| NanoNFCorpus_dot_ndcg@10 \| NanoNQ_dot_ndcg@10 \| NanoBEIR_mean_dot_ndcg@10 \| \|:------:\|:----:\|:-------------:\|:---------------:\|:-----------------------:\|:------------------------:\|:------------------:\|:-------------------------:\| \| -1 \| -1 \| - \| - \| 0.6780 \| 0.3645 \| 0.7145 \| 0.5857 \| \| 0.0323 \| 200 \| 0.0131 \| - \| - \| - \| - \| - \| \| 0.0646 \| 400 \| 0.0126 \| - \| - \| - \| - \| - \| \| 0.0970 \| 600 \| 0.0111 \| - \| - \| - \| - \| - \| \| 0.1293 \| 800 \| 0.0116 \| - \| - \| - \| - \| - \| \| 0.1616 \| 1000 \| 0.0097 \| 0.0268 \| 0.6294 \| 0.3534 \| 0.6406 \| 0.5411 \| \| 0.1939 \| 1200 \| 0.0112 \| - \| - \| - \| - \| - \| \| 0.2262 \| 1400 \| 0.0153 \| - \| - \| - \| - \| - \| \| 0.2586 \| 1600 \| 0.0188 \| - \| - \| - \| - \| - \| \| 0.2909 \| 1800 \| 0.0124 \| - \| - \| - \| - \| - \| \| 0.3232 \| 2000 \| 0.0086 \| 0.0341 \| 0.6441 \| 0.3527 \| 0.6533 \| 0.5500 \| \| 0.3555 \| 2200 \| 0.0179 \| - \| - \| - \| - \| - \| \| 0.3878 \| 2400 \| 0.0168 \| - \| - \| - \| - \| - \| \| 0.4202 \| 2600 \| 0.0153 \| - \| - \| - \| - \| - \| \| 0.4525 \| 2800 \| 0.0204 \| - \| - \| - \| - \| - \| \| 0.4848 \| 3000 \| 0.007 \| 0.0288 \| 0.6608 \| 0.3426 \| 0.6768 \| 0.5601 \| \| 0.5171 \| 3200 \| 0.0105 \| - \| - \| - \| - \| - \| \| 0.5495 \| 3400 \| 0.0198 \| - \| - \| - \| - \| - \| \| 0.5818 \| 3600 \| 0.0077 \| - \| - \| - \| - \| - \| \| 0.6141 \| 3800 \| 0.0121 \| - \| - \| - \| - \| - \| \| 0.6464 \| 4000 \| 0.0091 \| 0.0331 \| 0.6524 \| 0.3547 \| 0.6736 \| 0.5602 \| \| 0.6787 \| 4200 \| 0.0095 \| - \| - \| - \| - \| - \| \| 0.7111 \| 4400 \| 0.011 \| - \| - \| - \| - \| - \| \| 0.7434 \| 4600 \| 0.0077 \| - \| - \| - \| - \| - \| \| 0.7757 \| 4800 \| 0.0032 \| - \| - \| - \| - \| - \| \| 0.8080 \| 5000 \| 0.0061 \| 0.0214 \| 0.6537 \| 0.3596 \| 0.6689 \| 0.5607 \| \| 0.8403 \| 5200 \| 0.009 \| - \| - \| - \| - \| - \| \| 0.8727 \| 5400 \| 0.0045 \| - \| - \| - \| - \| - \| \| 0.9050 \| 5600 \| 0.0093 \| - \| - \| - \| - \| - \| \| 0.9373 \| 5800 \| 0.0086 \| - \| - \| - \| - \| - \| \| 0.9696 \| 6000 \| 0.0091 \| 0.0244 \| 0.6369 \| 0.3652 \| 0.6647 \| 0.5556 \| \| -1 \| -1 \| - \| - \| 0.6355 \| 0.3662 \| 0.6647 \| 0.5555 \| ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Energy Consumed: 0.150 kWh - Carbon Emitted: 0.058 kg of CO2 - Hours Used: 0.4 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x NVIDIA GeForce RTX 3090 - CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K - RAM Size: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### SpladeLoss ```bibtex @misc{formal2022distillationhardnegativesampling, title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, year={2022}, eprint={2205.04733}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2205.04733}, } ``` #### SparseMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### FlopsLoss ```bibtex @article{paria2020minimizing, title={Minimizing flops to learn efficient sparse representations}, author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, journal={arXiv preprint arXiv:2004.05665}, year={2020} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
xzkb/ppo-CartPole-v1	xzkb	2025-06-16T11:51:34Z	0	0	null	[ "tensorboard", "CartPole-v1", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T11:51:24Z	--- tags: - CartPole-v1 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 253.80 +/- 132.79 name: mean_reward verified: false --- # PPO Agent Playing CartPole-v1 This is a trained model of a PPO agent playing CartPole-v1. # Hyperparameters ```python {'exp_name': 'notebook' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'CartPole-v1' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'xzkb/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```
Somalitts/6aad	Somalitts	2025-06-16T11:51:29Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2025-06-16T10:36:16Z	--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: 6aad 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. --> # 6aad This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1322 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.3844 \| 0.0142 \| 100 \| 0.3050 \| \| 0.2729 \| 0.0284 \| 200 \| 0.2091 \| \| 0.2174 \| 0.0427 \| 300 \| 0.1598 \| \| 0.1954 \| 0.0569 \| 400 \| 0.1394 \| \| 0.1895 \| 0.0711 \| 500 \| 0.1322 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
praa/GenAMI	praa	2025-06-16T11:49:30Z	0	0	null	[ "safetensors", "xlm-roberta", "sexism", "hate", "indic", "empowerment", "gender", "text-classification", "en", "hi", "mr", "bn", "ta", "te", "ml", "ur", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "doi:10.57967/hf/5784", "license:cc-by-nc-nd-4.0", "region:us" ]	text-classification	2025-06-13T17:37:25Z	--- license: cc-by-nc-nd-4.0 metrics: - f1 base_model: - FacebookAI/xlm-roberta-base language: - en - hi - mr - bn - ta - te - ml - ur pipeline_tag: text-classification tags: - sexism - hate - indic - empowerment - gender --- # Model Card for Model ID Classifies polarised gendered discourse for all indic languages. 0=Neutral 1=Sexist and misogynistic 2=Empowering ## Model Details Come back later at an unspecified date for further information ## Author Details Praachi Kumar Research Fellow United Nations University - MERIT ### Model Description - Developed by: Praachi Kumar - Model type: Fine-tuned XLM-RoBERTa base for sequence classification - Language(s) (NLP): Multi Indic Languages - License: Non commercial, no derrivatives - Paper: forthcoming ## Uses Social science research, intended for academic use - 3 labels ## Bias, Risks, and Limitations Single annotator coded the data. Painstakingly. ### Recommendations Please contact me at [email protected] for instructions on further use ## How to Get Started with the Model With a bit of patience, I am still working on this model card ## Training Details ### Training Data Please contact me at [email protected] for this information #### Metrics Macro Average F1 Score: 0.83 Balanced Accuracy: 0.33 ### Results Come back later for an updated model card ## Citation Model BibTeX: @misc{genami2025, author = {Praachi Kumar}, title = {genAMI}, year = {2025}, month = {March}, day = {13}, howpublished = {\url{https://doi.org/10.57967/hf/5784}} } APA: Kumar, P. (2025). genAMI. Hugging Face. https://doi.org/10.57967/hf/5784 Paper: Forthcoming, in my PhD Thesis :)
LaaP-ai/donut-base-invoice-v1.07	LaaP-ai	2025-06-16T11:49:30Z	0	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-16T11:49:11Z	--- library_name: transformers license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer model-index: - name: donut-base-invoice-v1.07 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. --> # donut-base-invoice-v1.07 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
CreitinGameplays/Mistral-Nemo-12B-R1-v0.1	CreitinGameplays	2025-06-16T11:49:24Z	52	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:CreitinGameplays/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70Bmistral", "base_model:mistralai/Mistral-Nemo-Instruct-2407", "base_model:finetune:mistralai/Mistral-Nemo-Instruct-2407", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-21T19:17:13Z	--- license: mit datasets: - CreitinGameplays/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70Bmistral language: - en base_model: - mistralai/Mistral-Nemo-Instruct-2407 pipeline_tag: text-generation library_name: transformers new_version: CreitinGameplays/Mistral-Nemo-12B-R1-v0.2 --- ## Mistral Nemo 12B R1 ![mistralthink](https://autumn.revolt.chat/attachments/zIqa-Q6gKlwm7BbOvKvFFRLHDdy5OOy30KcU5iFle1/image.png) Took 96 hours to finetune on 2x Nvidia RTX A6000 with the following settings: - Batch size: 3 - Gradient accumulation steps: 1 - Epochs: 1 - Learning rate: 1e-4 - Warmup ratio: 0.1 Run the model: ```python import torch from transformers import pipeline model_id = "CreitinGameplays/Mistral-Nemo-12B-R1-v0.1" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto" ) messages = [ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": "How many r's are in strawberry?"} ] outputs = pipe( messages, temperature=0.6, top_p=1.0, top_k=50, repetition_penalty=1.1, max_new_tokens=2048 ) print(outputs[0]["generated_text"][-1]) ``` Recommended system prompt: ``` You are an AI focused on providing systematic, well-reasoned responses. Response Structure: - Format: <think>{reasoning}</think>{answer} - Process: Think first, then answer. ``` Response example: ``` <s>[INST]You are an AI focused on providing systematic, well-reasoned responses. Response Structure: - Format: <think>{reasoning}</think>{answer} - Process: Think first, then answer. what is a nvidia tesla t4[/INST]<think>Okay, so I need to figure out what a NVIDIA Tesla T4 is. I remember that NVIDIA makes GPUs for various purposes, but I'm not exactly sure about the specifics of the Tesla models. Let me start by recalling what I know about GPUs in general. They're used for graphics rendering, right? But I also heard that they can be used for other tasks like machine learning and deep learning. I think the Tesla series from NVIDIA is specifically designed for these non-gaming applications. So, the Tesla T4 must be one of those. I've heard terms like Tensor cores before, which I believe are specialized hardware units inside some GPUs for accelerating certain operations. Maybe the T4 has Tensor cores? Let me try to break it down. The name "Tesla" suggests it's part of their data center or professional line. The number "4" probably indicates it's the fourth generation or something similar. I should check if there are previous versions like T1, T2, etc., to see where it stands in the lineup. I remember that the Tesla V100 was a high-end model with a lot of memory and performance. The T4 might be a step below that or perhaps a different type altogether. Maybe it's more affordable or targeted at specific use cases. I think the T4 is known for its support for mixed precision training, which allows using both floating-point and half-precision numbers together. This could make training neural networks faster and more efficient. It might also have features like NVLink for connecting multiple GPUs, which would be useful for distributed computing. Looking at specs, I believe the T4 has 320 Tensor cores. Each Tensor core can perform matrix multiplications much faster than regular CUDA cores. So, it's optimized for machine learning tasks. The amount of memory is probably around 16 GB, which is less than the V100's 16 GB or 32 GB, but still sufficient for many applications. I also recall that the T4 supports NVIDIA's GPU virtualization technology, which allows multiple VMs to share a single GPU. That would be great for cloud providers who want to maximize resource utilization. In terms of performance, the T4 is likely slower than the V100 but faster than older models like the P400. It might be used in applications where cost is a factor, but performance isn't as critical as in high-end research or enterprise settings. I should also consider the form factors. I think the T4 comes in PCIe cards, maybe with different sizes depending on the application—like full-height or low-profile. It might also be available in server boards or as part of pre-built systems. Another thing is power consumption. The T4 is probably more energy-efficient than earlier models, which is important for data centers looking to reduce costs and environmental impact. I wonder how it compares to AMD's offerings. I know AMD has GPUs like the Radeon Instinct, but I'm not sure how they stack up against the Tesla T4 in terms of performance and features. In summary, the Tesla T4 is a professional-grade GPU designed for data centers and machine learning tasks. It features Tensor cores for acceleration, supports mixed precision, and has enough memory and connectivity options to handle modern workloads efficiently. </think> The NVIDIA Tesla T4 is a professional-grade GPU designed for data centers and machine learning tasks. Key features include: 1. Tensor Cores: Equipped with 320 Tensor cores, each optimizing matrix multiplications for accelerated machine learning tasks. 2. Mixed Precision Training: Supports simultaneous use of floating-point and half-precision numbers, enhancing efficiency in neural network training. 3. Memory and Connectivity: Offers 16 GB of memory and NVLink support for connecting multiple GPUs, ideal for distributed computing. 4. GPU Virtualization: Enables multiple virtual machines to share a single GPU, maximizing resource utilization in cloud environments. 5. Performance and Power: Slower than the V100 but faster than older models, with improved energy efficiency suitable for cost-conscious data centers. 6. Form Factors: Available in PCIe cards with varying sizes, including full-height and low-profile options, along with server board and pre-built system configurations. 7. Comparison with AMD: While the T4 competes with AMD's Radeon Instinct GPUs, it offers unique features tailored to data center and machine learning needs. In conclusion, the Tesla T4 is a versatile solution for data centers seeking balance between performance, efficiency, and affordability. Its features make it suitable for a range of applications, from cloud services to machine learning projects.</s> ``` Note: The model was mainly finetuned on English dataset, meaning the model may not perform well in other languages; The model may enter an infinite response loop after the reasoning step.
asimov-ai/Magistral-Small-3.1-documentazione	asimov-ai	2025-06-16T11:49:21Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:42:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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punxti/q-FrozenLake-v1-4x4-noSlippery	punxti	2025-06-16T11:49:19Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T11:40:38Z	--- 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="punxti/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"]) ```
TV-nulook-india-viral-videos/Original.Full.Clip.nulook.india.Viral.Video.Leaks.Official	TV-nulook-india-viral-videos	2025-06-16T11:48:40Z	0	0	null	[ "region:us" ]	null	2025-06-16T11:48:32Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
pprraattiikk9900/results	pprraattiikk9900	2025-06-16T11:48:22Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-multilingual-cased", "base_model:finetune:distilbert/distilbert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T11:47:52Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-multilingual-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: results 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. --> # results This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0015 - Accuracy: 1.0 ## 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: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
nnilayy/deap-arousal-binary-classification-no-wd-Kfold-3	nnilayy	2025-06-16T11:48:20Z	4	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-27T04:34:44Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
sdiazlor/modernbert-embed-base-crossencoder-human-rights	sdiazlor	2025-06-16T11:46:55Z	46	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "cross-encoder", "sentence-transformers", "text-ranking", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-ranking	2025-01-19T21:06:10Z	--- library_name: transformers tags: - cross-encoder - sentence-transformers pipeline_tag: text-ranking --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mlx-community/llm-jp-3.1-8x13b-instruct4-8bit	mlx-community	2025-06-16T11:45:36Z	0	0	mlx	[ "mlx", "safetensors", "mixtral", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-8x13b-instruct4", "base_model:quantized:llm-jp/llm-jp-3.1-8x13b-instruct4", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-06-16T09:42:56Z	--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false base_model: llm-jp/llm-jp-3.1-8x13b-instruct4 tags: - mlx --- # mlx-community/llm-jp-3.1-8x13b-instruct4-8bit This model [mlx-community/llm-jp-3.1-8x13b-instruct4-8bit](https://huggingface.co/mlx-community/llm-jp-3.1-8x13b-instruct4-8bit) was converted to MLX format from [llm-jp/llm-jp-3.1-8x13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-8x13b-instruct4) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-8x13b-instruct4-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.15_0.25_epoch1	MinaMila	2025-06-16T11:44:34Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:42:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
fc91/CQoT_SFT_merged_16bit_lora-Llama-3.2-3B-Instruct-v0.3	fc91	2025-06-16T11:43:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:fc91/CQoT_SFT_merged_16bit_lora-Llama-3.2-3B-Instruct-v0.2", "base_model:finetune:fc91/CQoT_SFT_merged_16bit_lora-Llama-3.2-3B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:36:56Z	--- base_model: fc91/CQoT_SFT_merged_16bit_lora-Llama-3.2-3B-Instruct-v0.2 tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- RL trained on the openai/gsm8k dataset (sample from 1308 until 1468).
23ikram/llama3b	23ikram	2025-06-16T11:43:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T11:42:34Z	--- 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: 23ikram - 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)
Jarvis1111/DoctorAgent-RL	Jarvis1111	2025-06-16T11:41:35Z	0	0	null	[ "safetensors", "qwen2", "license:apache-2.0", "region:us" ]	null	2025-06-16T11:39:02Z	--- license: apache-2.0 ---
prakod/codemix-test	prakod	2025-06-16T11:40:36Z	77	0	transformers	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:ai4bharat/IndicBART", "base_model:finetune:ai4bharat/IndicBART", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-16T08:52:24Z	--- library_name: transformers base_model: ai4bharat/IndicBART tags: - generated_from_trainer metrics: - bleu model-index: - name: codemix-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. --> # codemix-test This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5032 - Bleu: 17.4363 - Gen Len: 20.978 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 5.8666 \| 1.0 \| 1004 \| 4.9742 \| 13.3823 \| 21.0 \| \| 4.8088 \| 2.0 \| 2008 \| 4.0212 \| 15.3375 \| 21.0 \| \| 4.2735 \| 3.0 \| 3012 \| 3.6499 \| 16.3145 \| 21.0 \| \| 4.0836 \| 4.0 \| 4016 \| 3.5329 \| 17.3835 \| 20.996 \| \| 4.0152 \| 5.0 \| 5020 \| 3.5032 \| 17.4363 \| 20.978 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
moogin/gemly-lora	moogin	2025-06-16T11:40:03Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-pt", "base_model:finetune:unsloth/gemma-3-1b-pt", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T11:39:53Z	--- base_model: unsloth/gemma-3-1b-pt tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: moogin - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-pt 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)
bysq/autism-assistant-qwen2	bysq	2025-06-16T11:38:31Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "autism", "assistant", "chinese", "unsloth", "merged-model", "zh", "dataset:custom", "base_model:unsloth/Qwen2.5-1.5B-bnb-4bit", "base_model:quantized:unsloth/Qwen2.5-1.5B-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-16T08:57:19Z	--- language: - zh license: apache-2.0 tags: - autism - assistant - chinese - qwen2 - unsloth - merged-model base_model: unsloth/Qwen2.5-1.5B-bnb-4bit datasets: - custom library_name: transformers --- # 自闭症语言理解助手 (Autism Language Assistant) - 完整版这是一个专门帮助自闭症儿童理解语言的AI助手，基于Qwen2.5-1.5B模型微调而成。此版本为完整合并模型，无需额外的LoRA权重。 ## 模型特点 - ✅ 完整模型：已将LoRA权重合并到基础模型中 - ✅ 即开即用：无需额外配置或权重文件 - ✅ 专业设计：专门为自闭症语言理解场景优化 ## 功能该模型能够： - 分析语言中的情感和意图 - 用简单清晰的语言解释复杂表达 - 提供具体的回应建议 - 理解自闭症患者的表达方式 ## 使用方法 ### 先安装其他库 ```python !pip install bitsandbytes accelerate !pip install transformers torch ``` ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # 加载模型和tokenizer tokenizer = AutoTokenizer.from_pretrained("bysq/autism-assistant-qwen2") model = AutoModelForCausalLM.from_pretrained("bysq/autism-assistant-qwen2") # 移动到GPU（如果可用） device = "cuda" if torch.cuda.is_available() else "cpu" model = model.to(device) # 使用示例 def analyze_expression(original_text, autism_expression): prompt = f'''你是一个专门帮助理解自闭症患者表达的AI助手。原始表达："{original_text}" 自闭症患者的表达："{autism_expression}" 请分析并回答： - 情感分析：''' inputs = tokenizer(prompt, return_tensors="pt").to(device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=200, do_sample=True, temperature=0.7, pad_token_id=tokenizer.pad_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response[len(prompt):].strip() # 测试 result = analyze_expression("可以把东西给我？", "不你") print(result) ``` ## 许可证 Apache 2.0 ## 免责声明该模型仅供辅助使用，不能替代专业医疗建议。建议在专业指导下使用。
Triangle104/Huihui-MoE-24B-A8B-abliterated-Q5_K_M-GGUF	Triangle104	2025-06-16T11:36:43Z	0	0	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:34:19Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-24B-A8B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Triangle104/Huihui-MoE-24B-A8B-abliterated-Q5_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-24B-A8B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-24B-A8B-abliterated) 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/huihui-ai/Huihui-MoE-24B-A8B-abliterated) for more details on the model. --- Huihui-MoE-24B-A8B-abliterated is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the huihui-ai/Qwen3-8B-abliterated base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 4 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications. This model combines four ablated models, and perhaps it can achieve the performance of all the ablated models? This is just a test. The exploration of merging different manifestations of models of the same type is another possibility. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q5_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q5_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q5_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q5_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q5_k_m.gguf -c 2048 ```
quanda-bench-test/f1c529c-default_LDS	quanda-bench-test	2025-06-16T11:36:28Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-06-16T11:20:19Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
23ikram/model3b8bit	23ikram	2025-06-16T11:35:58Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T11:35:53Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: 23ikram - 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)
alexandertam/babylm-hulda50-10m-gpt2	alexandertam	2025-06-16T11:34:04Z	65	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-13T07:59:49Z	--- library_name: transformers license: mit base_model: gpt2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: babylm-hulda-10m-gpt2 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. --> # babylm-hulda-10m-gpt2 GPT2 Basemodel pretrained on 50% synthetic 50% original data from Babylm2025 It achieves the following results on the evaluation set: - Loss: 3.5751 - Accuracy: 0.3820 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 18000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\| \| 8.2384 \| 0.1167 \| 200 \| 7.7935 \| 0.1972 \| \| 6.9074 \| 0.2334 \| 400 \| 6.5061 \| 0.2358 \| \| 5.8108 \| 0.3501 \| 600 \| 5.6078 \| 0.2549 \| \| 5.3823 \| 0.4667 \| 800 \| 5.2613 \| 0.2608 \| \| 5.1007 \| 0.5834 \| 1000 \| 5.0485 \| 0.2705 \| \| 4.9232 \| 0.7001 \| 1200 \| 4.9021 \| 0.2750 \| \| 4.7723 \| 0.8168 \| 1400 \| 4.7936 \| 0.2751 \| \| 4.6436 \| 0.9335 \| 1600 \| 4.6973 \| 0.2822 \| \| 4.5757 \| 1.0502 \| 1800 \| 4.6303 \| 0.2840 \| \| 4.5332 \| 1.1669 \| 2000 \| 4.5745 \| 0.2862 \| \| 4.0004 \| 2.3337 \| 4000 \| 4.0663 \| 0.3348 \| \| 3.6798 \| 3.5006 \| 6000 \| 3.8603 \| 0.3535 \| \| 3.6025 \| 4.6674 \| 8000 \| 3.7458 \| 0.3640 \| \| 3.5169 \| 5.8343 \| 10000 \| 3.6725 \| 0.3715 \| \| 3.3989 \| 7.0012 \| 12000 \| 3.6293 \| 0.3750 \| \| 3.2351 \| 8.1680 \| 14000 \| 3.5991 \| 0.3792 \| \| 3.2415 \| 9.3349 \| 16000 \| 3.5830 \| 0.3811 \| \| 3.192 \| 10.5018 \| 18000 \| 3.5751 \| 0.3820 \| ## BabyLM Challenge 2025 Results ### Results Zero-shot \| Task \| Metric \| Causal Score \| \|-----------------------\|------------------\|--------------\| \| BLiMP \| Acc \| 65.09 \| \| BLiMP Supplement \| Acc \| 60.34 \| \| EWoK \| Acc \| 49.64 \| \| Eye Tracking \| Change in R² \| 0.37 \| \| Self-paced Reading \| Change in R² \| 0.15 \| \| Entity Tracking \| Acc \| 22.05 \| \| WUGs \| Acc \| 50 \| Finetuning \| Task \| Metric \| Score \| \|-----------\|--------\|-------\| \| BoolQ \| Acc \| 64.04 \| \| MNLI \| Acc \| 35.96 \| \| MRPC \| F1 \| 81.05 \| \| QQP \| F1 \| 62.74 \| \| MultiRC \| Acc \| 57.55 \| \| RTE \| Acc \| 53.96 \| \| WSC \| Acc \| 61.54 \| ### Framework versions - Transformers 4.50.3 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
rooclown101/pstmergetool	rooclown101	2025-06-16T11:33:05Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T11:33:05Z	--- license: apache-2.0 ---
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.15_0.75_epoch2	MinaMila	2025-06-16T11:32:40Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:30:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Gayaz2000/DPOTrained-Llama3.2-Lora-Model	Gayaz2000	2025-06-16T11:31:26Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-06-16T11:30:10Z	--- base_model: unsloth/Llama-3.2-1B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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duchao1210/DPO_Qwen25_3B_32_0.05_2000kmap_1e-7	duchao1210	2025-06-16T11:31:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:duchao1210/qwen_2.5_3B_5k_r128", "base_model:finetune:duchao1210/qwen_2.5_3B_5k_r128", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-14T16:05:18Z	--- base_model: duchao1210/qwen_2.5_3B_5k_r128 tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: duchao1210 - License: apache-2.0 - Finetuned from model : duchao1210/qwen_2.5_3B_5k_r128 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)
joanna302/Qwen3-4B-Base_lo_tt__0.0002	joanna302	2025-06-16T11:28:52Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:47:42Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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joshbarua/Qwen2.5-7B-base-english-japanese-s1k-full-sft	joshbarua	2025-06-16T11:25:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:21:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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shihuai7189/ppo-LunarLander-v2-clip-coef0.2	shihuai7189	2025-06-16T11:25:34Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-06-10T08:00:01Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -173.73 +/- 90.18 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'notebook' 'seed': 239 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'shihuai7189/ppo-LunarLander-v2-clip-coef0.2' 'batch_size': 512 'minibatch_size': 128} ```
dgambettaphd/M_llm2_run2_gen3_WXS_doc1000_synt64_lr1e-04_acm_SYNALL	dgambettaphd	2025-06-16T11:22:40Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T11:22:27Z	--- 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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shahink/Arpheus-4B	shahink	2025-06-16T11:21:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:20:56Z	--- 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. 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joshbarua/Qwen3-8B-base-french-s1k-full-sft	joshbarua	2025-06-16T11:20:56Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:15:50Z	--- 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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sergioalves/7629e3f5-7cb0-4ed7-8ce0-307a0af304d6	sergioalves	2025-06-16T11:19:43Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-1.5B", "base_model:adapter:Qwen/Qwen2.5-1.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-16T10:23:45Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B tags: - axolotl - generated_from_trainer model-index: - name: 7629e3f5-7cb0-4ed7-8ce0-307a0af304d6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen2.5-1.5B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - c6c33c8265f71baf_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.8 group_by_length: false hub_model_id: sergioalves/7629e3f5-7cb0-4ed7-8ce0-307a0af304d6 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-07 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.3 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 300 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/c6c33c8265f71baf_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d030151f-d4ab-4ec7-9455-d97d323144e0 wandb_project: s56-7 wandb_run: your_name wandb_runid: d030151f-d4ab-4ec7-9455-d97d323144e0 warmup_steps: 30 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 7629e3f5-7cb0-4ed7-8ce0-307a0af304d6 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1660 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9609 \| 0.0000 \| 1 \| 1.1693 \| \| 1.3796 \| 0.0063 \| 150 \| 1.1672 \| \| 1.0046 \| 0.0126 \| 300 \| 1.1660 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_M-GGUF	Triangle104	2025-06-16T11:15:09Z	0	0	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:12:59Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-24B-A8B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-24B-A8B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-24B-A8B-abliterated) 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/huihui-ai/Huihui-MoE-24B-A8B-abliterated) for more details on the model. --- Huihui-MoE-24B-A8B-abliterated is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the huihui-ai/Qwen3-8B-abliterated base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 4 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications. This model combines four ablated models, and perhaps it can achieve the performance of all the ablated models? This is just a test. The exploration of merging different manifestations of models of the same type is another possibility. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q4_k_m.gguf -c 2048 ```
LaaP-ai/donut-base-invoice-v1.05	LaaP-ai	2025-06-16T11:12:15Z	0	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-16T11:11:57Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: donut-base-invoice-v1.05 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. --> # donut-base-invoice-v1.05 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
taguser/flightctl-epoch20-2025-Jun-16-only-tests	taguser	2025-06-16T11:10:13Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:taguser/flightctl-epoch20-2025-Jun-15-merged", "base_model:adapter:taguser/flightctl-epoch20-2025-Jun-15-merged", "license:other", "region:us" ]	null	2025-06-16T11:08:22Z	--- library_name: peft license: other base_model: taguser/flightctl-epoch20-2025-Jun-15-merged 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 [taguser/flightctl-epoch20-2025-Jun-15-merged](https://huggingface.co/taguser/flightctl-epoch20-2025-Jun-15-merged) 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: 20 ### 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
mattmdjaga/segformer_b2_clothes	mattmdjaga	2025-06-16T11:10:06Z	477,246	425	transformers	[ "transformers", "pytorch", "onnx", "safetensors", "segformer", "vision", "image-segmentation", "dataset:mattmdjaga/human_parsing_dataset", "arxiv:2105.15203", "license:mit", "endpoints_compatible", "region:us" ]	image-segmentation	2022-11-24T09:48:16Z	--- license: mit tags: - vision - image-segmentation widget: - src: https://images.unsplash.com/photo-1643310325061-2beef64926a5?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Nnx8cmFjb29uc3xlbnwwfHwwfHw%3D&w=1000&q=80 example_title: Person - src: https://freerangestock.com/sample/139043/young-man-standing-and-leaning-on-car.jpg example_title: Person datasets: - mattmdjaga/human_parsing_dataset --- # Segformer B2 fine-tuned for clothes segmentation SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation but can also be used for human segmentation. The dataset on hugging face is called "mattmdjaga/human_parsing_dataset". [Training code](https://github.com/mattmdjaga/segformer_b2_clothes). ```python from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation from PIL import Image import requests import matplotlib.pyplot as plt import torch.nn as nn processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes") model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes") url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits.cpu() upsampled_logits = nn.functional.interpolate( logits, size=image.size[::-1], mode="bilinear", align_corners=False, ) pred_seg = upsampled_logits.argmax(dim=1)[0] plt.imshow(pred_seg) ``` Labels: 0: "Background", 1: "Hat", 2: "Hair", 3: "Sunglasses", 4: "Upper-clothes", 5: "Skirt", 6: "Pants", 7: "Dress", 8: "Belt", 9: "Left-shoe", 10: "Right-shoe", 11: "Face", 12: "Left-leg", 13: "Right-leg", 14: "Left-arm", 15: "Right-arm", 16: "Bag", 17: "Scarf" ### Evaluation \| Label Index \| Label Name \| Category Accuracy \| Category IoU \| \|:-------------:\|:----------------:\|:-----------------:\|:------------:\| \| 0 \| Background \| 0.99 \| 0.99 \| \| 1 \| Hat \| 0.73 \| 0.68 \| \| 2 \| Hair \| 0.91 \| 0.82 \| \| 3 \| Sunglasses \| 0.73 \| 0.63 \| \| 4 \| Upper-clothes \| 0.87 \| 0.78 \| \| 5 \| Skirt \| 0.76 \| 0.65 \| \| 6 \| Pants \| 0.90 \| 0.84 \| \| 7 \| Dress \| 0.74 \| 0.55 \| \| 8 \| Belt \| 0.35 \| 0.30 \| \| 9 \| Left-shoe \| 0.74 \| 0.58 \| \| 10 \| Right-shoe \| 0.75 \| 0.60 \| \| 11 \| Face \| 0.92 \| 0.85 \| \| 12 \| Left-leg \| 0.90 \| 0.82 \| \| 13 \| Right-leg \| 0.90 \| 0.81 \| \| 14 \| Left-arm \| 0.86 \| 0.74 \| \| 15 \| Right-arm \| 0.82 \| 0.73 \| \| 16 \| Bag \| 0.91 \| 0.84 \| \| 17 \| Scarf \| 0.63 \| 0.29 \| Overall Evaluation Metrics: - Evaluation Loss: 0.15 - Mean Accuracy: 0.80 - Mean IoU: 0.69 ### License The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2105-15203, author = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo}, title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, journal = {CoRR}, volume = {abs/2105.15203}, year = {2021}, url = {https://arxiv.org/abs/2105.15203}, eprinttype = {arXiv}, eprint = {2105.15203}, timestamp = {Wed, 02 Jun 2021 11:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
nvidia/PartPacker	nvidia	2025-06-16T11:09:25Z	0	3	null	[ "en", "arxiv:2506.09980", "license:other", "region:us" ]	null	2025-05-27T14:46:37Z	--- license: other license_name: nvidia-non-commercial-license license_link: https://huggingface.co/nvidia/PartPacker/blob/main/LICENSE language: - en --- # Model Card for PartPacker ## Description PartPacker is a three-dimensional (3D) generation model that is able to generate part-level 3D objects from single-view images. We introduce a dual volume packing strategy that organizes all parts into two complementary volumes, allowing for the creation of complete and interleaved parts that assemble into the final object. This model is ready for non-commercial use. ## License/Terms of Use [NVIDIA Non-Commercial License](https://huggingface.co/nvidia/PartPacker/blob/main/LICENSE) ## Deployment Geography Global ## Use Case PartPacker takes a single input image and generates a 3D shape with an arbitrary number of complete parts. Each part can be separated and edited independently to facilitate downstream tasks such as editing and animation. It's intended to be used by researchers and academics to develop new 3D generation methods. ## Release Date * Github: 06/11/2025 via [https://github.com/NVlabs/PartPacker](https://github.com/NVlabs/PartPacker) * Huggingface: 06/11/2025 via [https://huggingface.co/NVlabs/PartPacker](https://huggingface.co/NVlabs/PartPacker) ## Reference(s) [Code](https://github.com/NVlabs/PartPacker) [Paper](https://arxiv.org/abs/2506.09980) ## Model Architecture Architecture Type: Transformer Network Architecture: Diffusion Transformer (DiT) ## Input Input Type(s): Image Input Format(s): Red, Green, Blue (RGB) Input Parameters: Two-dimensional (2D) image Other Properties Related to Input: Resolution will be resized to 518x518. ## Output Output Type(s): Triangle Mesh Output Format: GL Transmission Format Binary (GLB) Output Parameters: Three-dimensional (3D) triangle mesh Other Properties Related to Output: Extracted at a resolution up to 512^3; without texture. Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. ## Software Integration ### Runtime Engine(s) * PyTorch ### Supported Hardware Microarchitecture Compatibility * NVIDIA Ampere * NVIDIA Hopper ### Preferred Operating System(s) * Linux ## Model Version(s) v1.0 ## Training, Testing, and Evaluation Datasets We perform training, testing, and evaluation on the Objaverse-XL dataset. For the VAE model, we use the first 253K meshes for training and the rest 1K meshes for validation. For the Flow model, we use all 254K meshes for training. ### Objaverse-XL Link: https://objaverse.allenai.org/ Data Collection Method: Hybrid: Automatic, Synthetic Labeling Method by dataset: N/A (no labels) Properties: We use about 254k mesh data, which is a subset from the Objaverse-XL filtered by the number of parts. ## Inference Acceleration Engine: PyTorch Test Hardware: NVIDIA A100 (1 GPU configuration) ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
ravi86/mood_detector	ravi86	2025-06-16T11:09:20Z	18	0	transformers	[ "transformers", "pytorch", "emotion-detection", "facial-expressio", "image-classification", "deep-learning", "cnn", "en", "base_model:microsoft/resnet-50", "base_model:finetune:microsoft/resnet-50", "license:mit", "endpoints_compatible", "region:us" ]	image-classification	2025-06-14T05:37:49Z	--- license: mit language: - en metrics: - accuracy base_model: - microsoft/resnet-50 new_version: google/vit-base-patch16-224 pipeline_tag: image-classification library_name: transformers tags: - pytorch - emotion-detection - facial-expressio - image-classification - deep-learning - cnn --- # 🎭 Face Expression Detector A deep learning model that classifies facial expressions in grayscale images into one of seven core emotions. Designed for applications in emotion analytics, human-computer interaction, and psychological research. --- ## 📊 Model Overview This model takes 48x48 grayscale face images and classifies them into: - 😠 Angry - 🤢 Disgust - 😨 Fear - 😄 Happy - 😢 Sad - 😲 Surprise - 😐 Neutral Dataset: [FER2013](https://www.kaggle.com/datasets/msambare/fer2013) Training Samples: 28,709 Testing Samples: 3,589 --- ## 🧠 Model Architecture - 📦 Custom CNN - 3 Convolutional Layers - Batch Normalization - ReLU Activation - Dropout for regularization - 📈 Optimizer: `Adam` - 🔥 Loss Function: `Categorical Crossentropy` - ⏱️ Epochs: `100` --- ## ✅ Performance > 📌 Add your actual performance metrics here: - Accuracy on FER2013 Test Set: ~1.0% --- ## 🗂️ Required Files - `model.h5` → Model Weights - `config.json` → Configuration file (Transformers-based) - `preprocessor_config.json` → Preprocessing setup (if needed) - `requirements.txt` → Python dependencies --- ## 🚀 Use Cases - 🎮 Real-time emotion feedback in games or virtual assistants - 🎓 Emotion analysis for psychological and behavioral studies - 🎥 Enhancing video-based UX with dynamic emotion tracking --- ## ⚠️ Limitations - Works best with centered 48x48 grayscale faces - Face detection (e.g., MTCNN) required before prediction - FER2013's demographic diversity is limited → potential bias --- ## ⚙️ Installation Follow these steps to set up the environment and dependencies: --pip install -r requirements.txt torch>=1.9.0 transformers>=4.20.0 pillow>=8.0.0 ### 1. Clone the Repository git clone https://github.com/TRavi8688/Mood-Based-Music-Player cd mood_detector ##🧪 How to Use (Transformers-based) Follow these steps to preprocess an image and predict facial expression using the pre-trained Transformers-based model: Python ```bash from transformers import AutoModelForImageClassification, AutoImageProcessor from PIL import Image import torch ``` ### 1. Load Model and Preprocessor ```bash # STEP 1: Install dependencies !pip install tensorflow pillow numpy # STEP 2: Download model file using `requests` import requests model_url = "https://huggingface.co/ravi86/mood_detector/resolve/main/my_model.h5" model_path = "my_model.h5" # Download the file response = requests.get(model_url) with open(model_path, "wb") as f: f.write(response.content) print("✅ Model downloaded successfully!") ``` 2. Load and Preprocess the Image ```bash image_path = "your_image.jpg" # 🔁 Replace with your image file image = Image.open(image_path).convert("L").resize((48, 48)) # Convert to grayscale and resize ``` # 3. Make Predictions ```bash outputs = model(**inputs) probs = torch.softmax(outputs.logits, dim=-1) # Convert logits to probabilities predicted_class = probs.argmax().item() # Get the predicted class index ``` # 4. Interpret the Result ```bash emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"] print(f"Predicted Emotion: {emotions[predicted_class]}") ``` ☁️ Deploy to Hugging Face Hub ```bash Use these commands to prepare and push your model to the Hugging Face Hub: Bash # Step 1: Install & Login pip install huggingface_hub huggingface-cli login from huggingface_hub import upload_folder upload_folder( folder_path="path/to/mood_detector", repo_id="ravi86/mood_detector", repo_type="model", commit_message="🚀 Upload mood detection model" ) ``` ### 🧭 Ethical Considerations ⚖️ Bias: The FER2013 dataset may exhibit biases in demographic representation. Exercise caution when interpreting results across diverse populations. 🔒 Privacy: Ensure strict compliance with data privacy laws (e.g., GDPR, CCPA) when using this model on personal or sensitive images. Do not use without explicit consent. ❗ Misuse: This model is not intended for unauthorized surveillance, profiling, or any other unethical applications. ### 👤 Contact 📬 For questions, support, or collaborations: Hugging Face → @ravi86 Gmail→ [email protected] ⭐ If you find this project useful, consider giving a star or contributing!
Sri2901/04_cosmos_v3_Shift	Sri2901	2025-06-16T11:08:08Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "ai-toolkit", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-16T11:07:39Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - ai-toolkit base_model: black-forest-labs/FLUX.1-dev instance_prompt: cosmos 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 widget: - text: A model poses confidently on a stone railing overlooking a lake with lush green mountains in the background, wearing a white sleeveless dress and holding a white quilted handbag. the model is standing in the middle of the image, with her arms resting on the edge of the stone railing. she is wearing white sunglasses and has a confident expression on her face. her long black hair cascades down her back, and her eyes are directed straight ahead. her skin is smooth and fair, and she has dark brown skin tones. the lake is calm and serene, with a few small waves lapping against the shore, and the mountains are covered in lush green vegetation, creating a picturesque backdrop. the lighting is bright and natural, highlighting the model's beauty and elegance. the overall mood is sophisticated and timeless, evoking a sense of luxury and sophistication. output: url: samples/1750069866114__000004000_2.jpg - text: A woman standing outdoors in a marina with boats and mountains in the background. she is wearing a white and blue floral print midi dress with spaghetti straps and a slit at the side, paired with a woven straw bag. her long, dark brown hair cascades down her back, and she is accessorized with a pair of gold earrings and a round, gold-framed sunglasses. the sun is shining brightly, casting a warm glow on her face and the surrounding vegetation. the woman stands confidently in the middle of the image, with her hands resting on her hips, looking off into the distance with a slight smile on her lips. the marina is filled with boats docked in neat rows, and the hills in the distance provide a picturesque backdrop to the scene. output: url: samples/1750069848760__000004000_1.jpg - text: A photo shoot from a side angle about a woman in a black one-piece swimsuit sitting by a pool with pebbles, wearing sunglasses. the image also shows water droplets on her skin, highlighting her features. on the middle of the image, a woman appears to be in her mid-twenties, with dark brown hair styled in loose waves, wearing a black swimsuit with a plunging neckline and a side slit, sitting on the edge of a swimming pool with her legs crossed. she has a sultry expression and is looking directly at the camera. her body is slim and toned, with a slight curve to her chest. she is wearing black sunglasses, which add a touch of sophistication to her look. the background is a clear blue swimming pool, surrounded by pebble stones. the lighting is bright and natural, casting shadows on her body. the overall effect is one of relaxation and tranquility. output: url: samples/1750069831302__000004000_0.jpg --- # 04_cosmos_v3_shift Model trained with AI Toolkit by Ostris <Gallery /> ## Trigger words You should use `cosmos` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc. Weights for this model are available in Safetensors format. [Download](/username/04_cosmos_v3_shift/tree/main) them in the Files & versions tab. ## 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.bfloat16).to('cuda') pipeline.load_lora_weights('username/04_cosmos_v3_shift', weight_name='04_cosmos_v3_shift.safetensors') image = pipeline('cosmos A model poses confidently on a stone railing overlooking a lake with lush green mountains in the background, wearing a white sleeveless dress and holding a white quilted handbag. the model is standing in the middle of the image, with her arms resting on the edge of the stone railing. she is wearing white sunglasses and has a confident expression on her face. her long black hair cascades down her back, and her eyes are directed straight ahead. her skin is smooth and fair, and she has dark brown skin tones. the lake is calm and serene, with a few small waves lapping against the shore, and the mountains are covered in lush green vegetation, creating a picturesque backdrop. the lighting is bright and natural, highlighting the model's beauty and elegance. the overall mood is sophisticated and timeless, evoking a sense of luxury and sophistication.').images[0] image.save("my_image.png") ``` 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)
Sri2901/01_Studio_v1_Shift	Sri2901	2025-06-16T11:05:20Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "ai-toolkit", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-16T11:04:51Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - ai-toolkit base_model: black-forest-labs/FLUX.1-dev instance_prompt: studi@ 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 widget: - text: A woman standing confidently in the middle of a plain grey background, wearing a blue and white floral print dress with puff sleeves and black ankle boots. she is holding a small black clutch in her left hand and wearing black sunglasses. her long black hair cascades down her back, framing her face perfectly. the dress has a gathered waistline and a flowy skirt that falls just above her knees. the woman is standing with her arms at her sides, looking directly at the camera with a neutral expression. her black boots are ankle-length and reach up to her knees, adding a touch of sophistication to her outfit. the background is simple and minimalistic, allowing the focus to remain on the woman's figure and the subtle pattern of her dress. output: url: samples/1750068339533__000004000_2.jpg - text: A woman standing confidently in the middle of the image, wearing a black dress with white piping and a blue handbag. She has short black hair styled in a sleek manner and is wearing gold earrings. her posture is confident and strong, and she is standing with one hand on her hip and the other resting on her thigh. the background is a simple gradient of light blue to white, creating a minimalistic look. the lighting is soft and even, highlighting her features and creating a sense of sophistication. the image is shot from a full body angle, focusing on the woman's full body and the handbag in her left hand. output: url: samples/1750068322435__000004000_1.jpg - text: A photo-realistic shoot from a front camera angle about a woman in a white sleeveless dress with a brown belt and a woven basket, standing confidently in the middle of the image. the woman has long black hair and is wearing black flats. she is standing in front of a plain grey background, with her hands behind her back and a neutral expression on her face. her eyes are looking directly at the viewer, and she appears to be in her mid-twenties. her body is slim and she is wearing minimal clothing, except for a white dress that has a high collar and a wide waist. her hair is styled in loose waves and falls down her back. her ears are not visible, but she is also wearing minimal jewelry. she has a small basket in her right hand, which is woven with a light brown color and has a gold buckle around her waist. the image also shows a minimalistic and clean aesthetic, with a focus on the woman's outfit and accessories. output: url: samples/1750068305344__000004000_0.jpg --- # 01_Studio_v1_Shift Model trained with AI Toolkit by Ostris <Gallery /> ## Trigger words You should use `studi@` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc. Weights for this model are available in Safetensors format. [Download](/username/01_Studio_v1_Shift/tree/main) them in the Files & versions tab. ## 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.bfloat16).to('cuda') pipeline.load_lora_weights('username/01_Studio_v1_Shift', weight_name='01_Studio_v1_Shift.safetensors') image = pipeline('studi@ A woman standing confidently in the middle of a plain grey background, wearing a blue and white floral print dress with puff sleeves and black ankle boots. she is holding a small black clutch in her left hand and wearing black sunglasses. her long black hair cascades down her back, framing her face perfectly. the dress has a gathered waistline and a flowy skirt that falls just above her knees. the woman is standing with her arms at her sides, looking directly at the camera with a neutral expression. her black boots are ankle-length and reach up to her knees, adding a touch of sophistication to her outfit. the background is simple and minimalistic, allowing the focus to remain on the woman's figure and the subtle pattern of her dress.').images[0] image.save("my_image.png") ``` 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)
Sri2901/01_Studio_v1	Sri2901	2025-06-16T11:03:59Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "ai-toolkit", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-16T11:03:32Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - ai-toolkit base_model: black-forest-labs/FLUX.1-dev instance_prompt: studi@ 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 widget: - text: A photo-realistic shoot from a profile camera angle about a woman wearing a blue and white floral print dress with puff sleeves and a tie waist, standing in profile against a plain grey background. the image also shows a woman standing in the middle of the frame, facing away from the camera, wearing a white dress with blue floral patterns and a ruffled hem. on the right side of the image, a woman appears to be in her mid-twenties, with long black hair styled in loose waves, wearing earrings and a bracelet on her wrist. her expression is neutral, and she is standing with her arms at her sides. output: url: samples/1750068830506__000004000_2.jpg - text: A woman standing confidently in the middle of the image, wearing a black dress with white piping and a blue handbag. She has short black hair styled in a sleek manner and is wearing gold earrings. her posture is confident and strong, and she is standing with one hand on her hip and the other resting on her thigh. the background is a simple gradient of light blue to white, creating a minimalistic look. the lighting is soft and even, highlighting her features and creating a sense of sophistication. the image is shot from a full body angle, focusing on the woman's full body and the handbag in her left hand. output: url: samples/1750068813061__000004000_1.jpg - text: A woman standing confidently in the middle of a plain grey background, wearing a blue and white floral print dress with puff sleeves and black ankle boots. she is holding a small black clutch in her left hand and wearing black sunglasses. her long black hair cascades down her back, framing her face perfectly. the dress has a gathered waistline and a flowy skirt that falls just above her knees. the woman is standing with her arms at her sides, looking directly at the camera with a neutral expression. her black boots are ankle-length and reach up to her knees, adding a touch of sophistication to her outfit. the background is simple and minimalistic, allowing the focus to remain on the woman's figure and the subtle pattern of her dress. output: url: samples/1750068795567__000004000_0.jpg --- # 01_Studio_v1 Model trained with AI Toolkit by Ostris <Gallery /> ## Trigger words You should use `studi@` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc. Weights for this model are available in Safetensors format. [Download](/username/01_Studio_v1/tree/main) them in the Files & versions tab. ## 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.bfloat16).to('cuda') pipeline.load_lora_weights('username/01_Studio_v1', weight_name='01_Studio_v1.safetensors') image = pipeline('studi@ A photo-realistic shoot from a profile camera angle about a woman wearing a blue and white floral print dress with puff sleeves and a tie waist, standing in profile against a plain grey background. the image also shows a woman standing in the middle of the frame, facing away from the camera, wearing a white dress with blue floral patterns and a ruffled hem. on the right side of the image, a woman appears to be in her mid-twenties, with long black hair styled in loose waves, wearing earrings and a bracelet on her wrist. her expression is neutral, and she is standing with her arms at her sides.').images[0] image.save("my_image.png") ``` 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)
Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_S-GGUF	Triangle104	2025-06-16T11:03:53Z	0	0	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T11:01:56Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-24B-A8B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_S-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-24B-A8B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-24B-A8B-abliterated) 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/huihui-ai/Huihui-MoE-24B-A8B-abliterated) for more details on the model. --- Huihui-MoE-24B-A8B-abliterated is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the huihui-ai/Qwen3-8B-abliterated base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 4 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications. This model combines four ablated models, and perhaps it can achieve the performance of all the ablated models? This is just a test. The exploration of merging different manifestations of models of the same type is another possibility. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_S-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_S-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_S-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q4_K_S-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q4_k_s.gguf -c 2048 ```
AinurSafr/telegram-message-classifier	AinurSafr	2025-06-16T11:03:38Z	5	0	peft	[ "peft", "safetensors", "ru", "en", "arxiv:1910.09700", "base_model:unsloth/OpenHermes-2.5-Mistral-7B-bnb-4bit", "base_model:adapter:unsloth/OpenHermes-2.5-Mistral-7B-bnb-4bit", "region:us" ]	null	2025-05-10T02:40:48Z	--- base_model: unsloth/OpenHermes-2.5-Mistral-7B-bnb-4bit library_name: peft language: - ru - en --- # 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
shelaz01/asl_alphabet_model	shelaz01	2025-06-16T11:03:26Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T11:01:35Z	--- license: apache-2.0 ---
LaaP-ai/donut-base-invoice-v1.04	LaaP-ai	2025-06-16T11:00:10Z	0	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-16T10:59:51Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: donut-base-invoice-v1.04 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. --> # donut-base-invoice-v1.04 This model is a fine-tuned version of [](https://huggingface.co/) on the None 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.25_0.5_0.75_epoch1	MinaMila	2025-06-16T10:58:58Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T10:57:16Z	--- 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]
Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_L-GGUF	Triangle104	2025-06-16T10:58:53Z	0	0	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T10:28:11Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-24B-A8B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_L-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-24B-A8B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-24B-A8B-abliterated) 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/huihui-ai/Huihui-MoE-24B-A8B-abliterated) for more details on the model. --- Huihui-MoE-24B-A8B-abliterated is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the huihui-ai/Qwen3-8B-abliterated base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 4 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications. This model combines four ablated models, and perhaps it can achieve the performance of all the ablated models? This is just a test. The exploration of merging different manifestations of models of the same type is another possibility. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_L-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q3_k_l.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_L-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q3_k_l.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_L-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q3_k_l.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_L-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q3_k_l.gguf -c 2048 ```
BigData-KSU/ArabVLM	BigData-KSU	2025-06-16T10:58:22Z	0	0	peft	[ "peft", "llava", "ar", "en", "base_model:ALLaM-AI/ALLaM-7B-Instruct-preview", "base_model:adapter:ALLaM-AI/ALLaM-7B-Instruct-preview", "region:us" ]	null	2025-05-08T06:53:41Z	--- library_name: peft language: - ar - en base_model: - ALLaM-AI/ALLaM-7B-Instruct-preview --- ## ArabVLM: Vision Language Model - Repository: https://github.com/BigData-KSU/ArabVLM - Demo: Soon. ## How to Get Started with the Model ### Install 1. Clone this repository and navigate to RS-LLaVA folder ``` git clone https://github.com/BigData-KSU/ArabVLM.git cd ArabVLM ``` 2. Install Packages ``` pip install -r requirements.txt ``` --- ### Inference Use the code below to get started with the model. ```python from PIL import Image import os import torch from vllm.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN from vllm.conversation import conv_templates, SeparatorStyle from vllm.model.builder import load_pretrained_model from vllm.utils import disable_torch_init from vllm.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria ### Main model.... model_path ='/BigData-KSU/ArabVLM' model_base = 'ALLaM-AI/ALLaM-7B-Instruct-preview' conv_mode = 'llava_llama_2' disable_torch_init() model_path = os.path.abspath(model_path) print('model path') print(model_path) model_name = get_model_name_from_path(model_path) print('model name') print(model_name) print('model base') print(model_base) tokenizer, model, processor, context_len = load_pretrained_model(model_path, model_base, model_name,device='cuda:0') def chat_with_Vision_BioLLM(cur_prompt,image_name): # Prepare the input text, adding image-related tokens if needed image_mem = Image.open(image_name).convert('RGB') image_processor = processor['image'] conv = conv_templates[conv_mode].copy() roles = conv.roles print(image_mem) image_tensor = image_processor.preprocess(image_mem, return_tensors='pt')['pixel_values'] tensor = image_tensor.to(model.device, dtype=torch.float16) print(f"{roles[1]}: {cur_prompt}") cur_prompt = DEFAULT_IMAGE_TOKEN + '\n' + cur_prompt conv.append_message(conv.roles[0], cur_prompt) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) if image_mem: with torch.inference_mode(): output_ids = model.generate( input_ids, images=tensor, do_sample=False, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria]) response = tokenizer.decode(output_ids[0, input_ids.shape[1]:]) #print(outputs) return response if __name__ == "__main__": cur_prompt='وصف الصورة بالتفصيل ' image_name='path/to/image' outputs=chat_with_Vision_BioLLM(cur_prompt,image_name) print('Model Response.....') print(outputs) ``` - PEFT 0.4.0
Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_M-GGUF	Triangle104	2025-06-16T10:57:50Z	0	0	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:56:42Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-24B-A8B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-24B-A8B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-24B-A8B-abliterated) 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/huihui-ai/Huihui-MoE-24B-A8B-abliterated) for more details on the model. --- Huihui-MoE-24B-A8B-abliterated is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the huihui-ai/Qwen3-8B-abliterated base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 4 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications. This model combines four ablated models, and perhaps it can achieve the performance of all the ablated models? This is just a test. The exploration of merging different manifestations of models of the same type is another possibility. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q3_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q3_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q3_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Huihui-MoE-24B-A8B-abliterated-Q3_K_M-GGUF --hf-file huihui-moe-24b-a8b-abliterated-q3_k_m.gguf -c 2048 ```
Khushi-Rao-Viral-Video-clip/Full-Viral.khushi.rao.Viral.Video.Tutorial.Official.Clip.new	Khushi-Rao-Viral-Video-clip	2025-06-16T10:57:36Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T10:57:05Z	--- license: apache-2.0 --- [![My Image](https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif)](https://t.co/BILDe5gBcR)
23ikram/model8bit	23ikram	2025-06-16T10:55:29Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T10:54:40Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: 23ikram - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
calcuis/openaudio-gguf	calcuis	2025-06-16T10:55:26Z	287	3	null	[ "gguf", "gguf-connector", "text-to-speech", "base_model:fishaudio/openaudio-s1-mini", "base_model:quantized:fishaudio/openaudio-s1-mini", "license:cc-by-nc-sa-4.0", "region:us" ]	text-to-speech	2025-06-13T10:39:22Z	--- license: cc-by-nc-sa-4.0 base_model: - fishaudio/openaudio-s1-mini pipeline_tag: text-to-speech tags: - gguf-connector --- ## gguf quantized version of openaudio - base model from [fishaudio](https://huggingface.co/fishaudio) - text-to-speech synthesis ### run it with gguf-connector ``` ggc o2 ``` ![screenshot](https://raw.githubusercontent.com/calcuis/text-to-speech-synthesis-2/master/demo.png) \| Prompt \| Audio Sample \| \|--------\|---------------\| \|`Hey Connector, why your appearance looks so stupid?`<br/>`Oh, really? maybe I ate too much smart beans.`<br/>`Wow. Amazing (laughing).`<br/>`Let's go to get some more smart beans and you will become stupid as well.`<br/> \| 🎧 audio-sample-1<br><audio controls src="https://huggingface.co/calcuis/openaudio-gguf/resolve/main/samples%5Caudio1.wav"></audio> \| \|`Suddenly the plane's engines began failing, and the pilot says there isn't much time, and he'll keep the plane in the air as long as he can, and told his two passengers to take the only two parachutes on board and bail out. The world's smartest man immediately took a parachute and said "I'm the world's smartest man! The world needs me, so I can't die here!", and then jumped out of the plane. The pilot tells the hippie to hurry up and take the other parachute, because there aren't any more. And the hippie says "Relax man. We'll be fine. The world's smartest man took my backpack."`<br/> \| 🎧 audio-sample-2<br><audio controls src="https://huggingface.co/calcuis/openaudio-gguf/resolve/main/samples%5Caudio2.wav"></audio> \| ### review/reference - simply execute the command (`ggc o2`) above in console/terminal - opt a `codec` and a `model` gguf in the current directory to interact with (see example below) > >GGUF file(s) available. Select which one for codec: > >1. codec-q2_k.gguf >2. codec-q3_k_m.gguf >3. codec-q4_k_m.gguf (recommended) >4. codec-q5_k_m.gguf >5. codec-q6_k.gguf >6. model-bf16.gguf >7. model-f16.gguf >8. model-f32.gguf > >Enter your choice (1 to 8): 3 > >GGUF file(s) available. Select which one for model: > >1. codec-q2_k.gguf >2. codec-q3_k_m.gguf >3. codec-q4_k_m.gguf >4. codec-q5_k_m.gguf >5. codec-q6_k.gguf >6. model-bf16.gguf (recommended) >7. model-f16.gguf (for non-cuda user) >8. model-f32.gguf > >Enter your choice (1 to 8): _ > - note: for the latest update, only tokenizer will be pulled to `models/fish` automatically during the first launch, you need to prepare the codec and model files yourself, working like [vision](https://huggingface.co/calcuis/llava-gguf) connector right away; mix and match, more flexible - run it entirely offline; i.e., from local URL: http://127.0.0.1:7860 with lazy webui - gguf-connector ([pypi](https://pypi.org/project/gguf-connector))
GhulamNabi/finetuned-Llama-3.2-3B	GhulamNabi	2025-06-16T10:55:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T10:54:03Z	--- 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]
hullolo/itn-classifier	hullolo	2025-06-16T10:54:18Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T10:54:00Z	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hullolo - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Khushi-Rao-Viral-Video-clip/FULL.VIDEO.khushi.Rao.Viral.Video.Clip	Khushi-Rao-Viral-Video-clip	2025-06-16T10:53:26Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T10:52:45Z	--- license: apache-2.0 --- [![My Image](https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif)](https://t.co/BILDe5gBcR)
aieng-lab/codet5p-770m_smell-doc	aieng-lab	2025-06-16T10:53:19Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "en", "base_model:Salesforce/codet5p-770m", "base_model:finetune:Salesforce/codet5p-770m", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T10:52:52Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - Salesforce/codet5p-770m pipeline_tag: text-classification --- # CodeT5+ 770m for classifying smell documentation (multi-label) This model classifies smell documentation as 'fragmented', 'tangled', 'excessive', 'bloated' or 'lazy'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [Salesforce/codet5p-770m](https://huggingface.co/Salesforce/codet5p-770m) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
aieng-lab/codet5p-220m_smell-doc	aieng-lab	2025-06-16T10:52:05Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "en", "base_model:Salesforce/codet5p-220m", "base_model:finetune:Salesforce/codet5p-220m", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T10:51:54Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - Salesforce/codet5p-220m pipeline_tag: text-classification --- # CodeT5+ 220m for classifying smell documentation (multi-label) This model classifies smell documentation as 'fragmented', 'tangled', 'excessive', 'bloated' or 'lazy'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.25_0.75_0.05_epoch2	MinaMila	2025-06-16T10:50:59Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T10:49:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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