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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-12 18:33:19	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-12 18:33:14	card stringlengths 11 1.01M
Prathyusha101/tldr-ppco-g1p0-l1p0	Prathyusha101	2025-08-19T14:52:57Z	0	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-classification", "generated_from_trainer", "dataset:trl-internal-testing/tldr-preference-sft-trl-style", "arxiv:1909.08593", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T10:22:47Z	--- datasets: trl-internal-testing/tldr-preference-sft-trl-style library_name: transformers model_name: tldr-ppco-g1p0-l1p0 tags: - generated_from_trainer licence: license --- # Model Card for tldr-ppco-g1p0-l1p0 This model is a fine-tuned version of [None](https://huggingface.co/None) on the [trl-internal-testing/tldr-preference-sft-trl-style](https://huggingface.co/datasets/trl-internal-testing/tldr-preference-sft-trl-style) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Prathyusha101/tldr-ppco-g1p0-l1p0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/prathyusha1-the-university-of-texas-at-austin/huggingface/runs/qb7oufpu) This model was trained with PPO, a method introduced in [Fine-Tuning Language Models from Human Preferences](https://huggingface.co/papers/1909.08593). ### Framework versions - TRL: 0.15.0.dev0 - Transformers: 4.53.1 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citations Cite PPO as: ```bibtex @article{mziegler2019fine-tuning, title = {{Fine-Tuning Language Models from Human Preferences}}, author = {Daniel M. Ziegler and Nisan Stiennon and Jeffrey Wu and Tom B. Brown and Alec Radford and Dario Amodei and Paul F. Christiano and Geoffrey Irving}, year = 2019, eprint = {arXiv:1909.08593} } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
forstseh/blockassist-bc-arctic_soaring_heron_1755611858	forstseh	2025-08-19T14:51:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "arctic soaring heron", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:51:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - arctic soaring heron --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755613402	hakimjustbao	2025-08-19T14:51:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:51:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
zenqqq/blockassist-bc-restless_reptilian_caterpillar_1755614989	zenqqq	2025-08-19T14:51:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "restless reptilian caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:50:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - restless reptilian caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Leoar/blockassist-bc-pudgy_toothy_cheetah_1755614865	Leoar	2025-08-19T14:49:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pudgy toothy cheetah", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:49:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pudgy toothy cheetah --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
uncahined/blockassist-bc-prowling_durable_tapir_1755614817	uncahined	2025-08-19T14:48:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "prowling durable tapir", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:48:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - prowling durable tapir --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
umairmaliick/falcon-7b-instruct-taskpro-lora	umairmaliick	2025-08-19T14:45:49Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "base_model:adapter:tiiuae/falcon-7b-instruct", "lora", "transformers", "text-generation", "conversational", "base_model:tiiuae/falcon-7b-instruct", "license:apache-2.0", "region:us" ]	text-generation	2025-08-19T13:53:18Z	--- library_name: peft license: apache-2.0 base_model: tiiuae/falcon-7b-instruct tags: - base_model:adapter:tiiuae/falcon-7b-instruct - lora - transformers pipeline_tag: text-generation model-index: - name: falcon-7b-instruct-taskpro-lora 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. --> # falcon-7b-instruct-taskpro-lora This model is a fine-tuned version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2754 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 3.2923 \| \| No log \| 2.0 \| 2 \| 3.2812 \| \| No log \| 3.0 \| 3 \| 3.2754 \| ### Framework versions - PEFT 0.17.0 - Transformers 4.55.2 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
lilTAT/blockassist-bc-gentle_rugged_hare_1755614706	lilTAT	2025-08-19T14:45:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:45:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Trelis/Qwen3-4B_ds-arc-agi-2-perfect-100_test-c8	Trelis	2025-08-19T14:45:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen3-4B", "base_model:finetune:unsloth/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T14:44:31Z	--- base_model: unsloth/Qwen3-4B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Trelis - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B This qwen3 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)
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755613191	lisaozill03	2025-08-19T14:45:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:45:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Team-Atom/act_record_pp_blue001_96_100000	Team-Atom	2025-08-19T14:41:55Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "act", "dataset:Team-Atom/PiPl_blue_001", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-19T14:41:42Z	--- datasets: Team-Atom/PiPl_blue_001 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - robotics - lerobot - act --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
lilTAT/blockassist-bc-gentle_rugged_hare_1755614412	lilTAT	2025-08-19T14:40:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:40:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Feruru/Classifier	Feruru	2025-08-19T14:36:48Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T14:35:49Z	--- license: apache-2.0 ---
asteria-life/openalex_articles_v0	asteria-life	2025-08-19T14:36:06Z	0	0	model2vec	[ "model2vec", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "license:mit", "region:us" ]	null	2025-08-19T14:35:56Z	--- library_name: model2vec license: mit model_name: tmpp0ggqq40 tags: - embeddings - static-embeddings - sentence-transformers --- # tmpp0ggqq40 Model Card This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of a Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers. ## Installation Install model2vec using pip: ``` pip install model2vec ``` ## Usage ### Using Model2Vec The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models. Load this model using the `from_pretrained` method: ```python from model2vec import StaticModel # Load a pretrained Model2Vec model model = StaticModel.from_pretrained("tmpp0ggqq40") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` ### Using Sentence Transformers You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model: ```python from sentence_transformers import SentenceTransformer # Load a pretrained Sentence Transformer model model = SentenceTransformer("tmpp0ggqq40") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` ### Distilling a Model2Vec model You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code: ```python from model2vec.distill import distill # Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256) # Save the model m2v_model.save_pretrained("m2v_model") ``` ## How it works Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence. ## Additional Resources - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e) - [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) - [Website](https://minishlab.github.io/) ## Library Authors Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @article{minishlab2024model2vec, author = {Tulkens, Stephan and {van Dongen}, Thomas}, title = {Model2Vec: Fast State-of-the-Art Static Embeddings}, year = {2024}, url = {https://github.com/MinishLab/model2vec} } ```
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755613025	Sayemahsjn	2025-08-19T14:35:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:35:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aleebaster/blockassist-bc-sly_eager_boar_1755612564	aleebaster	2025-08-19T14:34:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:34:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Jekareka/test	Jekareka	2025-08-19T14:34:22Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T14:34:22Z	--- license: apache-2.0 ---
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755612485	ihsanridzi	2025-08-19T14:34:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:34:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sarrockia/prefectIllustriousXL_v3.safetensors	sarrockia	2025-08-19T14:33:06Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T14:04:58Z	--- license: apache-2.0 ---
shanaka95/gemma-3-270m-it-rag-finetune	shanaka95	2025-08-19T14:28:51Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:shanaka95/checkpoints", "base_model:finetune:shanaka95/checkpoints", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T10:32:44Z	--- base_model: shanaka95/checkpoints library_name: transformers model_name: gemma-3-270m-it-rag-finetune tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gemma-3-270m-it-rag-finetune This model is a fine-tuned version of [shanaka95/checkpoints](https://huggingface.co/shanaka95/checkpoints). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="shanaka95/gemma-3-270m-it-rag-finetune", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0+cu129 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755611635	quantumxnode	2025-08-19T14:22:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:22:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Andra76/blockassist-bc-deadly_enormous_butterfly_1755613240	Andra76	2025-08-19T14:21:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly enormous butterfly", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:21:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly enormous butterfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
chainway9/blockassist-bc-untamed_quick_eel_1755611572	chainway9	2025-08-19T14:20:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:20:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Joetib/en-twi-qwen2.5-0.5B-Instruct	Joetib	2025-08-19T14:19:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T14:19:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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mradermacher/qqWen-32B-RL-Reasoning-GGUF	mradermacher	2025-08-19T14:19:30Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:morganstanley/qqWen-32B-RL-Reasoning", "base_model:quantized:morganstanley/qqWen-32B-RL-Reasoning", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-18T22:30:32Z	--- base_model: morganstanley/qqWen-32B-RL-Reasoning language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/morganstanley/qqWen-32B-RL-Reasoning <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#qqWen-32B-RL-Reasoning-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-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/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/qqWen-32B-RL-Reasoning-GGUF/resolve/main/qqWen-32B-RL-Reasoning.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Kdch2597/ppo-LunarLander-v2	Kdch2597	2025-08-19T14:18:49Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-08-19T14:18:31Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.01 +/- 19.22 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 ... ```
Neurazum/Tbai-DPA-1.0	Neurazum	2025-08-19T14:17:43Z	0	1	transformers	[ "transformers", "safetensors", "text", "image", "brain", "dementia", "mri", "fmri", "health", "diagnosis", "diseases", "alzheimer", "parkinson", "comment", "doctor", "vbai", "tbai", "bai", "text-generation", "tr", "doi:10.57967/hf/5699", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-31T13:02:32Z	--- license: cc-by-nc-sa-4.0 language: - tr pipeline_tag: text-generation tags: - text - image - brain - dementia - mri - fmri - health - diagnosis - diseases - alzheimer - parkinson - comment - doctor - vbai - tbai - bai library_name: transformers --- # Tbai-DPA 1.0 Sürümü (TR) [BETA] ## Tanım Tbai-DPA 1.0 (Dementia, Parkinson, Alzheimer) modeli, MRI veya fMRI görüntüsü üzerinden beyin hastalıklarını yorumlayarak daha detaylı teşhis etmek amacıyla eğitilmiş ve geliştirilmiştir. Hastanın parkinson olup olmadığını, demans durumunu ve alzheimer riskini yüksek doğruluk oranı ile göstermektedir. ### Kitle / Hedef Tbai modelleri, Vbai ile birlikte çalışarak; öncelikle hastaneler, sağlık merkezleri ve bilim merkezleri için geliştirilmiştir. ### Sınıflar - Alzheimer Hastası - Ortalama Alzheimer Riski - Hafif Alzheimer Riski - Çok Hafif Alzheimer Riski - Risk Yok - Parkinson Hastası ## ---------------------------------------- # Tbai-DPA 1.0 Version (EN) [BETA] ## Description The Tbai-DPA 1.0 (Dementia, Parkinson's, Alzheimer's) model has been trained and developed to interpret brain diseases through MRI or fMRI images for more detailed diagnosis. It indicates whether the patient has Parkinson's disease, dementia, and Alzheimer's risk with a high accuracy rate. ### Audience / Target Tbai models, working in conjunction with Vbai, have been developed primarily for hospitals, health centers, and science centers. ### Classes - Alzheimer's disease - Average Risk of Alzheimer's Disease - Mild Alzheimer's Risk - Very Mild Alzheimer's Risk - No Risk - Parkinson's Disease ## Kullanım / Usage 1. Sanal ortam oluşturun. / Create a virtual environment. ```bash python -3.9.0 -m venv myenv ``` 2. Bağımlılıkları yükleyin. / Load dependencies. ```bash pip install -r requirements.txt ``` 3. Dosyayı çalıştırın. / Run the script. ```python import torch from transformers import T5Tokenizer, T5ForConditionalGeneration import warnings warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=UserWarning) def load_tbai_model(model_dir: str, device): tokenizer = T5Tokenizer.from_pretrained(model_dir) model = T5ForConditionalGeneration.from_pretrained(model_dir).to(device) return tokenizer, model def generate_comment_sampling( tokenizer, model, sinif_adi: str, device, max_length: int = 128 ) -> str: input_text = f"Sınıf: {sinif_adi}" inputs = tokenizer( input_text, return_tensors="pt", padding="longest", truncation=True, max_length=32 ).to(device) out_ids = model.generate( *inputs, max_length=max_length, do_sample=True, top_k=50, top_p=0.95, no_repeat_ngram_size=2, early_stopping=True ) comment = tokenizer.decode(out_ids[0], skip_special_tokens=True) return comment def test_with_sampling(): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") tokenizer, model = load_tbai_model( "Tbai/model/dir/path", device) test_classes = [ "alzheimer disease", "mild alzheimer risk", "moderate alzheimer risk", "very mild alzheimer risk", "no risk", "parkinson disease" ] for cls in test_classes: print(f"--- Class: {cls} (Deneme 1) ---") print(generate_comment_sampling(tokenizer, model, cls, device)) print(f"--- Class: {cls} (Deneme 2) ---") print(generate_comment_sampling(tokenizer, model, cls, device)) print() def main(): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f">>> Using device: {device}\n") model_dir = "Tbai/model/dir/path" tokenizer, model = load_tbai_model(model_dir, device) print(">>> Tokenizer ve model başarıyla yüklendi.\n") test_classes = [ "alzheimer disease", "mild alzheimer risk", "moderate alzheimer risk", "very mild alzheimer risk", "no risk", "parkinson disease" ] for cls in test_classes: generated = generate_comment_sampling(tokenizer, model, cls, device) print(f"Sınıf: {cls}") print(f"Üretilen Yorum: {generated}\n") if __name__ == "__main__": main() ``` 4. Görüntü İşleme Modeli ile Beraber Çalıştırın. / Run Together with Image Processing Model. ```python import os import time import torch import torch.nn as nn from torchvision import transforms from PIL import Image import matplotlib.pyplot as plt from thop import profile import numpy as np from datetime import datetime import warnings from sklearn.metrics import average_precision_score warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=UserWarning) from transformers import T5Tokenizer, T5ForConditionalGeneration class SimpleCNN(nn.Module): def __init__(self, model_type='c', num_classes=6): # Model tipine göre "model_type" değişkeni "f, c, q" olarak değiştirilebilir. / The ‘model_type’ variable can be changed to ‘f, c, q’ according to the model type. super(SimpleCNN, self).__init__() self.num_classes = num_classes if model_type == 'f': self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) self.fc1 = nn.Linear(64 28 * 28, 256) self.dropout = nn.Dropout(0.5) elif model_type == 'c': self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) self.fc1 = nn.Linear(128 * 28 * 28, 512) self.dropout = nn.Dropout(0.5) elif model_type == 'q': self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1) self.fc1 = nn.Linear(512 * 14 * 14, 1024) self.dropout = nn.Dropout(0.5) self.fc2 = nn.Linear(self.fc1.out_features, num_classes) self.relu = nn.ReLU() self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) def forward(self, x): x = self.pool(self.relu(self.conv1(x))) x = self.pool(self.relu(self.conv2(x))) x = self.pool(self.relu(self.conv3(x))) if hasattr(self, 'conv4'): x = self.pool(self.relu(self.conv4(x))) x = x.view(x.size(0), -1) x = self.relu(self.fc1(x)) x = self.dropout(x) x = self.fc2(x) return x def predict_image(model: nn.Module, image_path: str, transform, device): img = Image.open(image_path).convert('RGB') inp = transform(img).unsqueeze(0).to(device) model.eval() with torch.no_grad(): out = model(inp) prob = torch.nn.functional.softmax(out, dim=1) pred = prob.argmax(dim=1).item() conf = prob[0, pred].item() * 100 return pred, conf, inp, prob def calculate_performance_metrics(model: nn.Module, device, input_size=(1, 3, 224, 224)): model.to(device) x = torch.randn(input_size).to(device) flops, params = profile(model, inputs=(x,), verbose=False) cpu_start = time.time() _ = model(x) cpu_time = (time.time() - cpu_start) * 1000 return { 'size_pixels': input_size[-1], 'speed_cpu_b1': cpu_time, 'speed_cpu_b32': cpu_time / 10, 'speed_v100_b1': cpu_time / 2, 'params_million': params / 1e6, 'flops_billion': flops / 1e9 } def load_tbai_model(model_dir: str, device): tokenizer = T5Tokenizer.from_pretrained(model_dir) model = T5ForConditionalGeneration.from_pretrained(model_dir).to(device) model.eval() return tokenizer, model def generate_comment_turkce(tokenizer, model, sinif_adi: str, device, max_length: int = 64) -> str: input_text = f"Sınıf: {sinif_adi}" inputs = tokenizer( input_text, return_tensors="pt", padding="longest", truncation=True, max_length=32 ).to(device) out_ids = model.generate( *inputs, max_length=max_length, do_sample=True, top_k=50, top_p=0.95, no_repeat_ngram_size=2, early_stopping=True ) comment = tokenizer.decode(out_ids[0], skip_special_tokens=True) return comment def save_monitoring_log(predicted_class, confidence, comment_text, metrics, class_names, image_path, ap_scores=None, map_score=None, log_path='monitoring_log.txt'): os.makedirs(os.path.dirname(log_path) or '.', exist_ok=True) timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') img_name = os.path.basename(image_path) log = f""" ===== Model Monitoring Log ===== Timestamp: {timestamp} Image: {img_name} Predicted Class: {class_names[predicted_class]} Confidence: {confidence:.2f}% Comment: {comment_text} -- Performance Metrics -- Params (M): {metrics['params_million']:.2f} FLOPs (B): {metrics['flops_billion']:.2f} Image Size: {metrics['size_pixels']}x{metrics['size_pixels']} CPU Time b1 (ms): {metrics['speed_cpu_b1']:.2f} V100 Time b1 (ms): {metrics['speed_v100_b1']:.2f} V100 Time b32 (ms): {metrics['speed_cpu_b32']:.2f} -- AP/mAP Metrics --""" if ap_scores is not None and map_score is not None: log += f"\nmAP: {map_score:.4f}" for i, (class_name, ap) in enumerate(zip(class_names, ap_scores)): log += f"\nAP_{class_name}: {ap:.4f}" else: log += "\nAP/mAP: Not calculated (single image)" log += "\n================================\n" with open(log_path, 'a', encoding='utf-8') as f: f.write(log) def main(): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(device) transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) class_names = [ 'Alzheimer Disease', 'Mild Alzheimer Risk', 'Moderate Alzheimer Risk', 'Very Mild Alzheimer Risk', 'No Risk', 'Parkinson Disease' ] model = SimpleCNN(model_type='c', num_classes=len(class_names)).to(device) # Model tipine göre "model_type" değişkeni "f, c, q" olarak değiştirilebilir. / The ‘model_type’ variable can be changed to ‘f, c, q’ according to the model type. model_path = 'Vbai/model/file/path' try: model.load_state_dict(torch.load(model_path, map_location=device)) except Exception as e: print(f"Görüntü modeli yükleme hatası: {e}") return metrics = calculate_performance_metrics(model, device) tbai_model_dir = "Tbai/model/dir/path" tokenizer, tbai_model = load_tbai_model(tbai_model_dir, device) en2tr = { 'Alzheimer Disease': 'Alzheimer Hastalığı', 'Mild Alzheimer Risk': 'Hafif Alzheimer Riski', 'Moderate Alzheimer Risk': 'Orta Düzey Alzheimer Riski', 'Very Mild Alzheimer Risk': 'Çok Hafif Alzheimer Riski', 'No Risk': 'Risk Yok', 'Parkinson Disease': 'Parkinson Hastalığı' } image_path = 'test/images/path' pred_class_idx, confidence, inp_tensor, predicted_probs = predict_image(model, image_path, transform, device) predicted_class_name = class_names[pred_class_idx] print(f"Prediction: {predicted_class_name} ({confidence:.2f}%)") print(f"Confidence: {confidence:.2f}%") print(f"Params (M): {metrics['params_million']:.2f}") print(f"FLOPs (B): {metrics['flops_billion']:.2f}") print(f"Image Size: {metrics['size_pixels']}x{metrics['size_pixels']}") print(f"CPU Time b1 (ms): {metrics['speed_cpu_b1']:.2f}") print(f"V100 Time b1 (ms): {metrics['speed_v100_b1']:.2f}") print(f"V100 Time b32 (ms): {metrics['speed_cpu_b32']:.2f}") tr_class_name = en2tr.get(predicted_class_name, predicted_class_name) try: comment_text = generate_comment_turkce(tokenizer, tbai_model, tr_class_name, device) except Exception as e: print(f"Yorum üretme hatası: {e}") comment_text = "Yorum üretilemedi." print(f"\nComment (Tbai-DPA 1.0): {comment_text}") save_monitoring_log( pred_class_idx, confidence, comment_text, metrics, class_names, image_path) img_show = inp_tensor.squeeze(0).permute(1, 2, 0).cpu().numpy() mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) img_show = img_show std + mean img_show_clipped = np.clip(img_show, 0.0, 1.0) plt.imshow(img_show_clipped) plt.title(f'{predicted_class_name} — {confidence:.2f}%') plt.axis('off') plt.show() if __name__ == '__main__': main() ``` #### Lisans/License: CC-BY-NC-SA-4.0
AiArtLab/kc	AiArtLab	2025-08-19T14:17:04Z	0	2	null	[ "text-to-image", "base_model:KBlueLeaf/Kohaku-XL-Zeta", "base_model:finetune:KBlueLeaf/Kohaku-XL-Zeta", "region:us" ]	text-to-image	2025-04-30T17:10:58Z	--- base_model: - stabilityai/stable-diffusion-xl-base-1.0 - KBlueLeaf/Kohaku-XL-Zeta pipeline_tag: text-to-image --- ![result](gandr-collage.jpg) ## Description This model is a custom fine-tuned variant based on the Kohaku-XL-Zeta pretrained foundation [Kohaku-XL-Zeta](https://huggingface.co/KBlueLeaf/Kohaku-XL-Zeta). Kohaku-XL-Zeta itself is a "raw" base model trained for 1 epoch on 8+ million Danbooru(mostly) images , using 4x NVIDIA 3090 GPUs! While the original Kohaku is not user-friendly out-of-the-box, it serves as a flexible starting point for creative adaptations. To enhance encoder stability and inject cross-domain knowledge beyond Danbooru-specific features, the model was merged with ColorfulXL using cosine dissimilarity weighting (0.25 blend ratio). This integration aims to broaden the model’s understanding of natural language and artistic concepts beyond typical Danbooru tagging conventions. Post-merge stabilization involved 6 epochs at 2e-6 learning rate, followed by ongoing fine-tuning at 9e-7 learning rate to refine details. The closest publicly available fine-tune of this lineage is Illustrous, though it uses an earlier Kohaku version with weaker text comprehension. This variant leverages the improved Kohaku-Colorful hybrid (KC), prioritizing non-realistic art generation and creative flexibility over photorealism. Key Notes : - Not optimized for realism; best suited for anime/artistic styles. - Ideal for users seeking a customizable foundation for niche art generation or further fine-tuning experiments. ## Donations Please contact with us if you may provide some GPU's or money on training DOGE: DEw2DR8C7BnF8GgcrfTzUjSnGkuMeJhg83 BTC: 3JHv9Hb8kEW8zMAccdgCdZGfrHeMhH1rpN ## Contacts [recoilme](https://t.me/recoilme)
mradermacher/UI-Venus-Navi-72B-GGUF	mradermacher	2025-08-19T14:16:46Z	0	1	transformers	[ "transformers", "gguf", "en", "base_model:inclusionAI/UI-Venus-Navi-72B", "base_model:quantized:inclusionAI/UI-Venus-Navi-72B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-18T23:56:29Z	--- base_model: inclusionAI/UI-Venus-Navi-72B language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/inclusionAI/UI-Venus-Navi-72B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#UI-Venus-Navi-72B-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/UI-Venus-Navi-72B-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/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.mmproj-Q8_0.gguf) \| mmproj-Q8_0 \| 0.9 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.mmproj-f16.gguf) \| mmproj-f16 \| 1.5 \| multi-modal supplement \| \| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q2_K.gguf) \| Q2_K \| 29.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q3_K_S.gguf) \| Q3_K_S \| 34.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q3_K_M.gguf) \| Q3_K_M \| 37.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q3_K_L.gguf) \| Q3_K_L \| 39.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.IQ4_XS.gguf) \| IQ4_XS \| 40.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q4_K_S.gguf) \| Q4_K_S \| 44.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q4_K_M.gguf) \| Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q6_K.gguf.part2of2) \| Q6_K \| 64.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q8_0.gguf.part2of2) \| Q8_0 \| 77.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Kurosawama/Llama-3.2-3B-Instruct-Inference-align	Kurosawama	2025-08-19T14:15:36Z	0	0	transformers	[ "transformers", "safetensors", "trl", "dpo", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T14:15:34Z	--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
mang3dd/blockassist-bc-tangled_slithering_alligator_1755611332	mang3dd	2025-08-19T14:15:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:15:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ibm-granite/granite-speech-3.3-2b	ibm-granite	2025-08-19T14:14:22Z	44,969	22	transformers	[ "transformers", "safetensors", "granite_speech", "automatic-speech-recognition", "multilingual", "en", "fr", "de", "es", "pt", "arxiv:2505.08699", "base_model:ibm-granite/granite-3.3-2b-instruct", "base_model:finetune:ibm-granite/granite-3.3-2b-instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-28T15:25:29Z	--- license: apache-2.0 language: - multilingual - en - fr - de - es - pt base_model: - ibm-granite/granite-3.3-2b-instruct library_name: transformers --- # Granite-speech-3.3-2b (revision 3.3.2) Model Summary: Granite-speech-3.3-2b is a compact and efficient speech-language model, specifically designed for automatic speech recognition (ASR) and automatic speech translation (AST). Granite-speech-3.3-2b uses a two-pass design, unlike integrated models that combine speech and language into a single pass. Initial calls to granite-speech-3.3-2b will transcribe audio files into text. To process the transcribed text using the underlying Granite language model, users must make a second call as each step must be explicitly initiated. The model was trained on a collection of public corpora comprising diverse datasets for ASR and AST as well as synthetic datasets tailored to support the speech translation task. Granite-speech-3.3-2b was trained by modality aligning granite-3.3-2b-instruct (https://huggingface.co/ibm-granite/granite-3.3-2b-instruct) to speech on publicly available open source corpora containing audio inputs and text targets. Compared to the initial release, revision 3.3.2 * supports multilingual speech inputs in English, French, German, Spanish and Portuguese, * provides transcription accuracy improvements for English ASR by using a deeper acoustic encoder and additional training data. Evaluations: We evaluated granite-speech-3.3-2b revision 3.3.2 alongside granite-speech-3.3-8b (https://huggingface.co/ibm-granite/granite-speech-3.3-8b) and other speech-language models in the less than 8b parameter range as well as dedicated ASR and AST systems on standard benchmarks. The evaluation spanned multiple public benchmarks, with particular emphasis on English ASR tasks while also including multilingual ASR and AST for X-En and En-X translations. <br> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/666ec38102791b3b49f453e8/eKVw1fB17fNXOH8_ho9bm.png) <br> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/666ec38102791b3b49f453e8/XQPd-gPTnZx07URanW98N.png) <br> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/666ec38102791b3b49f453e8/WaMJrqQcJ8jtp9MNfq9ea.png) <br> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/666ec38102791b3b49f453e8/WhbjCEOjOQDVElfwGx6Zu.png) <br> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/666ec38102791b3b49f453e8/ZBD8wxhbFa0ZezH0aW5Oe.png) <br> Release Date: June 19, 2025 License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) Supported Languages: English, French, German, Spanish, Portuguese Intended Use: The model is intended to be used in enterprise applications that involve processing of speech inputs. In particular, the model is well-suited for English, French, German, Spanish and Portuguese speech-to-text and speech translations to and from English for the same languages plus English-to-Japanese and English-to-Mandarin. The model can also be used for tasks that involve text-only input since it calls the underlying granite-3.3-2b-instruct when the user specifies a prompt that does not contain audio. ## Generation: Granite Speech model is supported natively in `transformers` from the `main` branch. Below is a simple example of how to use the `granite-speech-3.3-2b` revision 3.3.2 model. ### Usage with `transformers` First, make sure to install a recent version of transformers: ```shell pip install transformers>=4.52.4 torchaudio peft soundfile ``` Then run the code: ```python import torch import torchaudio from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq from huggingface_hub import hf_hub_download device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "ibm-granite/granite-speech-3.3-2b" processor = AutoProcessor.from_pretrained(model_name) tokenizer = processor.tokenizer model = AutoModelForSpeechSeq2Seq.from_pretrained( model_name, device_map=device, torch_dtype=torch.bfloat16 ) # load audio audio_path = hf_hub_download(repo_id=model_name, filename="10226_10111_000000.wav") wav, sr = torchaudio.load(audio_path, normalize=True) assert wav.shape[0] == 1 and sr == 16000 # mono, 16khz # create text prompt system_prompt = "Knowledge Cutoff Date: April 2024.\nToday's Date: April 9, 2025.\nYou are Granite, developed by IBM. You are a helpful AI assistant" user_prompt = "<\|audio\|>can you transcribe the speech into a written format?" chat = [ dict(role="system", content=system_prompt), dict(role="user", content=user_prompt), ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) # run the processor+model model_inputs = processor(prompt, wav, device=device, return_tensors="pt").to(device) model_outputs = model.generate(*model_inputs, max_new_tokens=200, do_sample=False, num_beams=1) # Transformers includes the input IDs in the response. num_input_tokens = model_inputs["input_ids"].shape[-1] new_tokens = torch.unsqueeze(model_outputs[0, num_input_tokens:], dim=0) output_text = tokenizer.batch_decode( new_tokens, add_special_tokens=False, skip_special_tokens=True ) print(f"STT output = {output_text[0].upper()}") ``` ### Usage with `vLLM` First, make sure to install the latest version of vLLM: ```shell pip install vllm --upgrade ``` Code for offline mode: ```python from transformers import AutoTokenizer from vllm import LLM, SamplingParams from vllm.assets.audio import AudioAsset from vllm.lora.request import LoRARequest model_id = "ibm-granite/granite-speech-3.3-2b" tokenizer = AutoTokenizer.from_pretrained(model_id) def get_prompt(question: str, has_audio: bool): """Build the input prompt to send to vLLM.""" if has_audio: question = f"<\|audio\|>{question}" chat = [ { "role": "user", "content": question } ] return tokenizer.apply_chat_template(chat, tokenize=False) # NOTE - you may see warnings about multimodal lora layers being ignored; # this is okay as the lora in this model is only applied to the LLM. model = LLM( model=model_id, enable_lora=True, max_lora_rank=64, max_model_len=2048, # This may be needed for lower resource devices. limit_mm_per_prompt={"audio": 1}, ) ### 1. Example with Audio [make sure to use the lora] question = "can you transcribe the speech into a written format?" prompt_with_audio = get_prompt( question=question, has_audio=True, ) audio = AudioAsset("mary_had_lamb").audio_and_sample_rate inputs = { "prompt": prompt_with_audio, "multi_modal_data": { "audio": audio, } } outputs = model.generate( inputs, sampling_params=SamplingParams( temperature=0.2, max_tokens=64, ), lora_request=[LoRARequest("speech", 1, model_id)] ) print(f"Audio Example - Question: {question}") print(f"Generated text: {outputs[0].outputs[0].text}") ### 2. Example without Audio [do NOT use the lora] question = "What is the capital of Brazil?" prompt = get_prompt( question=question, has_audio=False, ) outputs = model.generate( {"prompt": prompt}, sampling_params=SamplingParams( temperature=0.2, max_tokens=12, ), ) print(f"Text Only Example - Question: {question}") print(f"Generated text: {outputs[0].outputs[0].text}") ``` * Code for online mode: ```python """ Launch the vLLM server with the following command: vllm serve ibm-granite/granite-speech-3.3-2b \ --api-key token-abc123 \ --max-model-len 2048 \ --enable-lora \ --lora-modules speech=ibm-granite/granite-speech-3.3-2b \ --max-lora-rank 64 """ import base64 import requests from openai import OpenAI from vllm.assets.audio import AudioAsset # Modify OpenAI's API key and API base to use vLLM's API server. openai_api_key = "token-abc123" openai_api_base = "http://localhost:8000/v1" client = OpenAI( # defaults to os.environ.get("OPENAI_API_KEY") api_key=openai_api_key, base_url=openai_api_base, ) base_model_name = "ibm-granite/granite-speech-3.3-2b" lora_model_name = "speech" # Any format supported by librosa is supported audio_url = AudioAsset("mary_had_lamb").url # Use base64 encoded audio in the payload def encode_audio_base64_from_url(audio_url: str) -> str: """Encode an audio retrieved from a remote url to base64 format.""" with requests.get(audio_url) as response: response.raise_for_status() result = base64.b64encode(response.content).decode('utf-8') return result audio_base64 = encode_audio_base64_from_url(audio_url=audio_url) ### 1. Example with Audio # NOTE: we pass the name of the lora model (`speech`) here because we have audio. question = "can you transcribe the speech into a written format?" chat_completion_with_audio = client.chat.completions.create( messages=[{ "role": "user", "content": [ { "type": "text", "text": question }, { "type": "audio_url", "audio_url": { # Any format supported by librosa is supported "url": f"data:audio/ogg;base64,{audio_base64}" }, }, ], }], temperature=0.2, max_tokens=64, model=lora_model_name, ) print(f"Audio Example - Question: {question}") print(f"Generated text: {chat_completion_with_audio.choices[0].message.content}") ### 2. Example without Audio # NOTE: we pass the name of the base model here because we do not have audio. question = "What is the capital of Brazil?" chat_completion_with_audio = client.chat.completions.create( messages=[{ "role": "user", "content": [ { "type": "text", "text": question }, ], }], temperature=0.2, max_tokens=12, model=base_model_name, ) print(f"Text Only Example - Question: {question}") print(f"Generated text: {chat_completion_with_audio.choices[0].message.content}") ``` Model Architecture: The architecture of granite-speech-3.3-2b revision 3.3.2 consists of the following components: (1) Speech encoder: 16 conformer blocks trained with Connectionist Temporal Classification (CTC) on character-level targets on the subset containing only ASR corpora (see configuration below). In addition, our CTC encoder uses block-attention with 4-seconds audio blocks and self-conditioned CTC from the middle layer. \| Configuration parameter \| Value \| \|-----------------\|----------------------\| \| Input dimension \| 160 (80 logmels x 2) \| \| Nb. of layers \| 16 \| \| Hidden dimension \| 1024 \| \| Nb. of attention heads \| 8 \| \| Attention head size \| 128 \| \| Convolution kernel size \| 15 \| \| Output dimension \| 256 \| (2) Speech projector and temporal downsampler (speech-text modality adapter): we use a 2-layer window query transformer (q-former) operating on blocks of 15 1024-dimensional acoustic embeddings coming out of the last conformer block of the speech encoder that get downsampled by a factor of 5 using 3 trainable queries per block and per layer. The total temporal downsampling factor is 10 (2x from the encoder and 5x from the projector) resulting in a 10Hz acoustic embeddings rate for the LLM. The encoder, projector and LoRA adapters were fine-tuned/trained jointly on all the corpora mentioned under Training Data. (3) Large language model: granite-3.3-2b-instruct with 128k context length (https://huggingface.co/ibm-granite/granite-3.3-2b-instruct). (4) LoRA adapters: rank=64 applied to the query, value projection matrices Training Data: Overall, our training data is largely comprised of two key sources: (1) publicly available datasets (2) Synthetic data created from publicly available datasets specifically targeting the speech translation task. A detailed description of the training datasets can be found in the table below: \| Name \| Task \| Nb. hours \| Source \| \|-----------\|--------------\|----------------\|--------------\| \| CommonVoice-17 En,De,Es,Fr,Pt \| ASR \| 5600 \| https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0 \| \| MLS En,De,Es,Fr,Pt \| ASR \| 48000 \| https://huggingface.co/datasets/facebook/multilingual_librispeech \| \| Librispeech English \| ASR \| 1000 \| https://huggingface.co/datasets/openslr/librispeech_asr \| \| VoxPopuli En,De,Fr,Es \| ASR \| 1100 \| https://huggingface.co/datasets/facebook/voxpopuli \| \| AMI English \| ASR \| 100 \| https://huggingface.co/datasets/edinburghcstr/ami \| \| YODAS English \| ASR \| 10000 \| https://huggingface.co/datasets/espnet/yodas \| \| Earnings-22 English \| ASR \| 105 \| https://huggingface.co/datasets/esb/datasets \| \| Switchboard English \| ASR \| 260 \| https://catalog.ldc.upenn.edu/LDC97S62 \| \| CallHome English \| ASR \| 18 \| https://catalog.ldc.upenn.edu/LDC97T14 \| \| Fisher English \| ASR \| 2000 \| https://catalog.ldc.upenn.edu/LDC2004S13 \| \| Voicemail part I English \| ASR \| 40 \| https://catalog.ldc.upenn.edu/LDC98S77 \| \| Voicemail part II English \| ASR \| 40 \| https://catalog.ldc.upenn.edu/LDC2002S35 \| \| CommonVoice-17 De,Es,Fr,Pt->En \| AST \| 3000 \| Translations with Granite-3 and Phi-4 \| \| CommonVoice-17 En->De,Es,Fr,It,Ja,Pt,Zh \| AST \| 18000 \| Translations with Phi-4 and MADLAD \| Infrastructure: We train Granite Speech using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs. The training of this particular model was completed in 13 days on 32 H100 GPUs. Ethical Considerations and Limitations: The use of Large Speech and Language Models can trigger certain risks and ethical considerations. Although our alignment processes include safety considerations, the model may in some cases produce inaccurate, biased, offensive or unwanted responses to user prompts. Additionally, whether smaller models may exhibit increased susceptibility to hallucination in generation scenarios due to their reduced sizes, which could limit their ability to generate coherent and contextually accurate responses, remains uncertain. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. IBM recommends using this model for automatic speech recognition and translation tasks. The model's modular design improves safety by limiting how audio inputs can influence the system. If an unfamiliar or malformed prompt is received, the model simply echoes it with its transcription. This minimizes the risk of adversarial inputs, unlike integrated models that directly interpret audio and may be more exposed to such attacks. Note that more general speech tasks may pose higher inherent risks of triggering unwanted outputs. To enhance safety, we recommend using granite-speech-3.3-2b alongside Granite Guardian. Granite Guardian is a fine-tuned instruct model designed to detect and flag risks in prompts and responses across key dimensions outlined in the IBM AI Risk Atlas. Resources - 📄 Read the full technical report: https://arxiv.org/abs/2505.08699 (covers initial release only) - 🔧 Notebooks: [Finetune on custom data](https://github.com/ibm-granite/granite-speech-models/blob/main/notebooks/fine_tuning_granite_speech.ipynb), [two-pass spoken question answering](https://github.com/ibm-granite/granite-speech-models/blob/main/notebooks/two_pass_spoken_qa.ipynb) - ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite - 🚀 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/ - 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources
vrbhalaaji/my_policy	vrbhalaaji	2025-08-19T14:13:45Z	0	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:vrbhalaaji/orange-pick-test", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-19T14:13:00Z	--- datasets: vrbhalaaji/orange-pick-test library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - act - lerobot - robotics --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755611000	kojeklollipop	2025-08-19T14:11:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:11:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
doriankim/gemma3-4b-skin-cancer-classifier	doriankim	2025-08-19T14:10:34Z	0	0	null	[ "safetensors", "gemma3", "region:us" ]	null	2025-08-17T05:47:36Z	# Gemma3 4B Skin Cancer Classifier ## 모델 개요 이 모델은 Gemma-3 4B를 기반으로 피부암 진단을 위해 파인튜닝된 분류 모델입니다. ## 모델 상세 정보 - Base Model: Gemma-3 4B - Task: 피부암 이미지 분류 - Fine-tuning Steps: 1000 steps - LoRA Rank (r): 32 - Batch Size: 8 ## 훈련 설정 ```json { "model_name": "gemma-3-4b", "training_steps": 1000, "lora_rank": 32, "batch_size": 8, "learning_rate": "auto", "optimizer": "adamw" } ``` ## 평가 결과 자세한 평가 결과는 `evaluation/` 폴더에서 확인하실 수 있습니다. ### 주요 성능 지표 - 정확도: [evaluation 폴더에서 확인] - 정밀도: [evaluation 폴더에서 확인] - 재현율: [evaluation 폴더에서 확인] - F1 스코어: [evaluation 폴더에서 확인] ## 사용 방법 ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # 모델과 토크나이저 로드 model_name = "doriankim/gemma3-4b-skin-cancer-classifier" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) # 추론 예시 def predict_skin_condition(text): inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) with torch.no_grad(): outputs = model(*inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) return predictions ``` ## 폴더 구조 ``` ├── config.json # 모델 설정 ├── pytorch_model.bin # 모델 가중치 ├── tokenizer files # 토크나이저 관련 파일들 ├── evaluation/ # 평가 결과 및 분석 │ ├── results/ # 성능 지표 │ ├── plots/ # 시각화 결과 │ └── logs/ # 평가 로그 └── README.md # 이 파일 ``` ## 라이선스 [라이선스 정보를 여기에 추가하세요] ## 인용 이 모델을 사용하시는 경우 다음과 같이 인용해 주세요: ``` @misc{gemma3_4b_skin_cancer_classifier, author = {doriankim}, title = {Gemma-3 4B Skin Cancer Classifier}, year = {2024}, publisher = {Hugging Face}, url = {https://huggingface.co/doriankim/gemma3-4b-skin-cancer-classifier} } ``` --- 이 모델은 연구 목적으로 개발되었으며, 실제 의료 진단에 사용하기 전에 전문의와 상담하시기 바랍니다.*
Muapi/flux.1_mechanical-bloom-surreal-anime-style-portrait	Muapi	2025-08-19T14:09:15Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T14:09:02Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Flux.1_Mechanical Bloom · Surreal Anime-style Portrait ![preview](./preview.jpg) Base model: Flux.1 D Trained words: CynthiaPortrait ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1001492@1122429", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/oldnokia-ultrareal	Muapi	2025-08-19T14:07:27Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T14:07:15Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # oldNokia Ultrareal ![preview](./preview.jpg) Base model: Flux.1 D Trained words: n0k1a ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1808651@2046810", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
0xZeno/sdxl-base-1.0-wharton-footballer-optimized-loss	0xZeno	2025-08-19T14:06:53Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-08-19T10:40:12Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of sks football player widget: - text: a photo of sks football player playing football on a beach output: url: image_0.png - text: a photo of sks football player playing football on a beach output: url: image_1.png - text: a photo of sks football player playing football on a beach output: url: image_2.png - text: a photo of sks football player playing football on a beach output: url: image_3.png tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - 0xZeno/sdxl-base-1.0-wharton-footballer-optimized-loss <Gallery /> ## Model description These are 0xZeno/sdxl-base-1.0-wharton-footballer-optimized-loss LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks football player to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](0xZeno/sdxl-base-1.0-wharton-footballer-optimized-loss/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
alok0777/blockassist-bc-masked_pensive_lemur_1755612218	alok0777	2025-08-19T14:05:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked pensive lemur", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:04:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - masked pensive lemur --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755611085	Sayemahsjn	2025-08-19T14:02:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:02:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Kurosawama/Llama-3.2-3B-Translation-align	Kurosawama	2025-08-19T14:00:59Z	0	0	transformers	[ "transformers", "safetensors", "trl", "dpo", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T06:10:57Z	--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755610312	katanyasekolah	2025-08-19T14:00:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T14:00:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/1980-s-style-xl-f1d	Muapi	2025-08-19T14:00:35Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T14:00:26Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # 1980's style XL + F1D ![preview](./preview.jpg) Base model: Flux.1 D Trained words: 1980 style ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:376914@894083", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755610342	ihsanridzi	2025-08-19T13:59:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:59:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/radiant-realism-pro-realistic-makeup-skin-texture-skin-color-flux.1d	Muapi	2025-08-19T13:56:35Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:56:23Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Radiant Realism Pro (Realistic, Makeup, Skin Texture, Skin Color) Flux.1D ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:970421@1086588", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755610017	pempekmangedd	2025-08-19T13:55:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned sturdy dolphin", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:55:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned sturdy dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755610110	helmutsukocok	2025-08-19T13:54:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:54:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/alejandro-jodorowsky-style	Muapi	2025-08-19T13:53:43Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:53:34Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Alejandro Jodorowsky Style ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Alejandro Jodorowsky Style ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:62712@1403331", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/ethereal-fantasy	Muapi	2025-08-19T13:50:26Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:50:13Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Ethereal Fantasy ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1017670@1141054", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
QuantTrio/Qwen3-235B-A22B-Instruct-2507-AWQ	QuantTrio	2025-08-19T13:50:01Z	908	7	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "Qwen3", "AWQ", "量化修复", "vLLM", "conversational", "arxiv:2505.09388", "base_model:Qwen/Qwen3-235B-A22B-Instruct-2507", "base_model:quantized:Qwen/Qwen3-235B-A22B-Instruct-2507", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2025-07-24T04:37:10Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507/blob/main/LICENSE pipeline_tag: text-generation tags: - Qwen3 - AWQ - 量化修复 - vLLM base_model: - Qwen/Qwen3-235B-A22B-Instruct-2507 base_model_relation: quantized --- # Qwen3-235B-A22B-Instruct-2507-AWQ Base model [Qwen/Qwen3-235B-A22B-Instruct-2507](https://www.modelscope.cn/models/Qwen/Qwen3-235B-A22B-Instruct-2507) ### 【VLLM Launch Command for 8 GPUs (Single Node)】 <i>Note: When launching with 8 GPUs, --enable-expert-parallel must be specified; otherwise, the expert tensors cannot be evenly split across tensor parallel ranks. This option is not required for 4-GPU setups. </i> ``` CONTEXT_LENGTH=32768 # 262144 vllm serve \ QuantTrio/Qwen3-235B-A22B-Instruct-2507-AWQ \ --served-model-name Qwen3-235B-A22B-Instruct-2507-AWQ \ --enable-expert-parallel \ --swap-space 16 \ --max-num-seqs 512 \ --max-model-len $CONTEXT_LENGTH \ --max-seq-len-to-capture $CONTEXT_LENGTH \ --gpu-memory-utilization 0.9 \ --tensor-parallel-size 8 \ --trust-remote-code \ --disable-log-requests \ --host 0.0.0.0 \ --port 8000 ``` ### 【Dependencies】 ``` vllm>=0.9.2 ``` ### 【Model Update History】 ``` 2025-08-19 1.[BugFix] Fix compatibility issues with vLLM 0.10.1 2025-07-23 1. fast commit ``` ### 【Model Files】 \| File Size \| Last Updated \| \|---------\|--------------\| \| `116GB` \| `2025-07-23` \| ### 【Model Download】 ```python from huggingface_hub import snapshot_download snapshot_download('QuantTrio/Qwen3-235B-A22B-Instruct-2507-AWQ', cache_dir="your_local_path") ``` ### 【Description】 # Qwen3-235B-A22B-Instruct-2507 <a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Highlights We introduce the updated version of the Qwen3-235B-A22B non-thinking mode, named Qwen3-235B-A22B-Instruct-2507, featuring the following key enhancements: - Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage. - Substantial gains in long-tail knowledge coverage across multiple languages. - Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. - Enhanced capabilities in 256K long-context understanding. ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/62430a8522549d0917bfeb5a/0d7zztq4GB7G2ZYowO-dQ.jpeg) ## Model Overview Qwen3-235B-A22B-Instruct-2507 has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 235B in total and 22B activated - Number of Paramaters (Non-Embedding): 234B - Number of Layers: 94 - Number of Attention Heads (GQA): 64 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: 262,144 natively. NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Performance \| \| Deepseek-V3-0324 \| GPT-4o-0327 \| Claude Opus 4 Non-thinking \| Kimi K2 \| Qwen3-235B-A22B Non-thinking \| Qwen3-235B-A22B-Instruct-2507 \| \|--- \| --- \| --- \| --- \| --- \| --- \| ---\| \| Knowledge \| \| \| \| \| \| \| \| MMLU-Pro \| 81.2 \| 79.8 \| 86.6 \| 81.1 \| 75.2 \| 83.0 \| \| MMLU-Redux \| 90.4 \| 91.3 \| 94.2 \| 92.7 \| 89.2 \| 93.1 \| \| GPQA \| 68.4 \| 66.9 \| 74.9 \| 75.1 \| 62.9 \| 77.5 \| \| SuperGPQA \| 57.3 \| 51.0 \| 56.5 \| 57.2 \| 48.2 \| 62.6 \| \| SimpleQA \| 27.2 \| 40.3 \| 22.8 \| 31.0 \| 12.2 \| 54.3 \| \| CSimpleQA \| 71.1 \| 60.2 \| 68.0 \| 74.5 \| 60.8 \| 84.3 \| \| Reasoning \| \| \| \| \| \| \| \| AIME25 \| 46.6 \| 26.7 \| 33.9 \| 49.5 \| 24.7 \| 70.3 \| \| HMMT25 \| 27.5 \| 7.9 \| 15.9 \| 38.8 \| 10.0 \| 55.4 \| \| ARC-AGI \| 9.0 \| 8.8 \| 30.3 \| 13.3 \| 4.3 \| 41.8 \| \| ZebraLogic \| 83.4 \| 52.6 \| - \| 89.0 \| 37.7 \| 95.0 \| \| LiveBench 20241125 \| 66.9 \| 63.7 \| 74.6 \| 76.4 \| 62.5 \| 75.4 \| \| Coding \| \| \| \| \| \| \| \| LiveCodeBench v6 (25.02-25.05) \| 45.2 \| 35.8 \| 44.6 \| 48.9 \| 32.9 \| 51.8 \| \| MultiPL-E \| 82.2 \| 82.7 \| 88.5 \| 85.7 \| 79.3 \| 87.9 \| \| Aider-Polyglot \| 55.1 \| 45.3 \| 70.7 \| 59.0 \| 59.6 \| 57.3 \| \| Alignment \| \| \| \| \| \| \| \| IFEval \| 82.3 \| 83.9 \| 87.4 \| 89.8 \| 83.2 \| 88.7 \| \| Arena-Hard v2* \| 45.6 \| 61.9 \| 51.5 \| 66.1 \| 52.0 \| 79.2 \| \| Creative Writing v3 \| 81.6 \| 84.9 \| 83.8 \| 88.1 \| 80.4 \| 87.5 \| \| WritingBench \| 74.5 \| 75.5 \| 79.2 \| 86.2 \| 77.0 \| 85.2 \| \| Agent \| \| \| \| \| \| \| \| BFCL-v3 \| 64.7 \| 66.5 \| 60.1 \| 65.2 \| 68.0 \| 70.9 \| \| TAU-Retail \| 49.6 \| 60.3# \| 81.4 \| 70.7 \| 65.2 \| 71.3 \| \| TAU-Airline \| 32.0 \| 42.8# \| 59.6 \| 53.5 \| 32.0 \| 44.0 \| \| Multilingualism \| \| \| \| \| \| \| \| MultiIF \| 66.5 \| 70.4 \| - \| 76.2 \| 70.2 \| 77.5 \| \| MMLU-ProX \| 75.8 \| 76.2 \| - \| 74.5 \| 73.2 \| 79.4 \| \| INCLUDE \| 80.1 \| 82.1 \| - \| 76.9 \| 75.6 \| 79.5 \| \| PolyMATH \| 32.2 \| 25.5 \| 30.0 \| 44.8 \| 27.0 \| 50.2 \| : For reproducibility, we report the win rates evaluated by GPT-4.1. \#: Results were generated using GPT-4o-20241120, as access to the native function calling API of GPT-4o-0327 was unavailable. ## Quickstart The code of Qwen3-MoE has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3_moe' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-235B-A22B-Instruct-2507" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=16384 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) ``` For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B-Instruct-2507 --tp 8 --context-length 262144 ``` - vLLM: ```shell vllm serve Qwen/Qwen3-235B-A22B-Instruct-2507 --tensor-parallel-size 8 --max-model-len 262144 ``` Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.* For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python from qwen_agent.agents import Assistant # Define LLM llm_cfg = { 'model': 'Qwen3-235B-A22B-Instruct-2507', # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - We suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. Adequate Output Length: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models. 3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, } ```
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755611176	lqpl	2025-08-19T13:47:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:47:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hairy insectivorous antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Jocelyn-Martin/gemma-3-270m-it-fine-tuned__2025_13_08_17_02_LlamaUX_conversational	Jocelyn-Martin	2025-08-19T13:46:34Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T13:46:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mang3dd/blockassist-bc-tangled_slithering_alligator_1755609477	mang3dd	2025-08-19T13:45:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:44:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lilTAT/blockassist-bc-gentle_rugged_hare_1755610875	lilTAT	2025-08-19T13:41:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:41:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
indoempatnol/blockassist-bc-fishy_wary_swan_1755608989	indoempatnol	2025-08-19T13:38:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:38:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lilTAT/blockassist-bc-gentle_rugged_hare_1755610530	lilTAT	2025-08-19T13:35:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:35:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/felix-meynet	Muapi	2025-08-19T13:29:03Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:28:57Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Felix Meynet ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Art by Felix Meynet ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1021589@1441868", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Growcompany/gemma-3-270m-it-Q4_K_M-GGUF	Growcompany	2025-08-19T13:26:11Z	0	0	transformers	[ "transformers", "gguf", "gemma3", "unsloth", "gemma", "google", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:unsloth/gemma-3-270m-it", "base_model:quantized:unsloth/gemma-3-270m-it", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-08-19T13:26:05Z	--- base_model: unsloth/gemma-3-270m-it license: gemma tags: - gemma3 - unsloth - gemma - google - llama-cpp - gguf-my-repo pipeline_tag: text-generation library_name: transformers --- # Growcompany/gemma-3-270m-it-Q4_K_M-GGUF This model was converted to GGUF format from [`unsloth/gemma-3-270m-it`](https://huggingface.co/unsloth/gemma-3-270m-it) 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/unsloth/gemma-3-270m-it) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Growcompany/gemma-3-270m-it-Q4_K_M-GGUF --hf-file gemma-3-270m-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Growcompany/gemma-3-270m-it-Q4_K_M-GGUF --hf-file gemma-3-270m-it-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 Growcompany/gemma-3-270m-it-Q4_K_M-GGUF --hf-file gemma-3-270m-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Growcompany/gemma-3-270m-it-Q4_K_M-GGUF --hf-file gemma-3-270m-it-q4_k_m.gguf -c 2048 ```
Growcompany/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF	Growcompany	2025-08-19T13:24:52Z	0	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-08-19T13:24:45Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # Growcompany/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Growcompany/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-0.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Growcompany/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-0.5b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Growcompany/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-0.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Growcompany/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-0.5b-instruct-q4_k_m.gguf -c 2048 ```
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755609775	canoplos112	2025-08-19T13:24:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:23:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/flux-flux-hanfu-belly-wrap	Muapi	2025-08-19T13:23:35Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:23:02Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # FLUX汉服肚兜 \| FLUX Hanfu belly wrap ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:653935@731600", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755608212	helmutsukocok	2025-08-19T13:23:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:22:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Qwen3_Medical_GRPO-i1-GGUF	mradermacher	2025-08-19T13:22:18Z	791	2	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen3", "medical", "en", "zh", "dataset:FreedomIntelligence/medical-o1-reasoning-SFT", "dataset:lastmass/medical-o1-reasoning-SFT-keywords", "base_model:lastmass/Qwen3_Medical_GRPO", "base_model:quantized:lastmass/Qwen3_Medical_GRPO", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-07-24T16:02:43Z	--- base_model: lastmass/Qwen3_Medical_GRPO datasets: - FreedomIntelligence/medical-o1-reasoning-SFT - lastmass/medical-o1-reasoning-SFT-keywords language: - en - zh library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen3 - medical --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/lastmass/Qwen3_Medical_GRPO <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3_Medical_GRPO-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q2_K.gguf) \| i1-Q2_K \| 1.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 2.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ3_S.gguf) \| i1-IQ3_S \| 2.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ3_M.gguf) \| i1-IQ3_M \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 2.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 2.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3_Medical_GRPO-i1-GGUF/resolve/main/Qwen3_Medical_GRPO.i1-Q6_K.gguf) \| i1-Q6_K \| 3.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
lilTAT/blockassist-bc-gentle_rugged_hare_1755609552	lilTAT	2025-08-19T13:19:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:19:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755607889	vwzyrraz7l	2025-08-19T13:18:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:18:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/velvet-s-epic-dragons-flux	Muapi	2025-08-19T13:18:29Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:18:18Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Velvet's Epic Dragons \| Flux ![preview](./preview.jpg) Base model: Flux.1 D Trained words: FluxEpicDragon ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:715643@800301", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
GFIO/Fish_NC	GFIO	2025-08-19T13:16:48Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-08-19T13:16:21Z	--- 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]
AliaeAI/setfit_nli_v5	AliaeAI	2025-08-19T13:11:44Z	0	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "region:us" ]	text-classification	2025-08-19T13:11:27Z	--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: I find moments in my day where I carve out time to read, even for just a few minutes I do not usually feel energetic in the mornings when I wake up really. Why are you changing topics? [SEP] What problems would you like to put on the agenda? - text: I’ve been trying to manage my energy levels, but it feels like an uphill battle some days. Any tips on balancing work and rest? It’s really tough to gauge when I encounter difficulties in managing daily stress. Some days I feel like I’m handling it well, but other days, even small tasks seem overwhelming. [SEP] Can you describe the consistency and appearance of your stools? Have you noticed any changes recently? - text: Yeah, there are specific foods or drinks that seem to trigger the pain, anything spicy or greasy sets it off. I've been trying to avoid those. It comes and goes, usually worse after I eat. Lately, I've also been feeling pretty bloated. [SEP] Have you noticed any changes in your sleep patterns related to your meal times or food choices? - text: I have not noticed any changes in my weight along with experiencing abdominal pain really, my weight's been pretty stable. I'm more concerned about this constant pain—it's wearing me down mentally too. Sometimes it feels like stress makes it worse, but it's hard to pinpoint specific triggers. I've been trying to keep a food diary to see if certain foods make a difference, but so far, no clear patterns. [SEP] Is the abdominal pain constant, or does it come and go? - text: It's the little things now, like getting out of the ambulance or even writing reports that feel like a marathon. I've started avoiding stairs whenever I can. I definitely need more breaks than before. Even just bending down to check a patient's vitals can leave me winded these days. [SEP] When you realize you don’t have enough energy to do what you want, does it leave you feeling annoyed or discouraged? metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| <ul><li>"It's probably just my body's way of dealing with the discomfort. Some days, it feels like everything is a bit too much to handle. I wish I could do more, but the pain often holds me back. It's frustrating when I have to cancel plans or take breaks just to manage it. [SEP] I love running. It's one of the most popular recreational activities in the world. Do you like running?"</li><li>"I can't even finish folding the laundry without needing to sit down. It's the simple things that are getting harder. I’m sorry, but I have to go. Goodbye. [SEP] It was great talking with you today and hearing about your experiences! Even though things are feeling tough right now, remember that small steps forward can lead to big changes over time. Keep focusing on those little victories and I'm sure you'll find your way back to a place of greater energy. I look forward to our next chat!"</li><li>'hey ok my night [SEP] What is your favorite thing to do in your spare time? What do you like to do for fun?'</li></ul> \| \| 1 \| <ul><li>'I do not often join in on their game nights as often as I used to. The evenings can be a bit tough these days. They love a good round of Scrabble or Chess. Keeps their minds sharp, they say. [SEP] What are some things that usually help you feel more energized?'</li><li>"The breathlessness comes and goes, but it's definitely worse after treatment. Even just trying to change my clothes can leave me winded. I used to love reading the morning paper, but lately, I can barely focus long enough to get through an article. It's just not the same anymore. [SEP] Is there anything specific about your environment or surroundings that you think might be affecting your concentration?"</li><li>"I'm sorry to hear you're dealing with such unpleasant symptoms. It sounds really challenging. Well, the abdominal pain and diarrhea have been happening for a few weeks now. I've been having trouble keeping food down, and there's been some blood in my stools too. [SEP] that sounds really tough. i'm glad you're getting help though. hopefully you'll start feeling better soon. Could you tell more precisely where the stomach pain is located?"</li></ul> \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("AliaeAI/setfit_nli_v5") # Run inference preds = model("I find moments in my day where I carve out time to read, even for just a few minutes I do not usually feel energetic in the mornings when I wake up really. Why are you changing topics? [SEP] What problems would you like to put on the agenda?") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 6 \| 55.0309 \| 130 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0 \| 874 \| \| 1 \| 872 \| ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (3, 3) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 10 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0009 \| 1 \| 0.2838 \| - \| \| 0.0458 \| 50 \| 0.2677 \| - \| \| 0.0916 \| 100 \| 0.2551 \| - \| \| 0.1374 \| 150 \| 0.2544 \| - \| \| 0.1832 \| 200 \| 0.2463 \| - \| \| 0.2289 \| 250 \| 0.253 \| - \| \| 0.2747 \| 300 \| 0.2427 \| - \| \| 0.3205 \| 350 \| 0.2223 \| - \| \| 0.3663 \| 400 \| 0.2129 \| - \| \| 0.4121 \| 450 \| 0.1816 \| - \| \| 0.4579 \| 500 \| 0.1496 \| - \| \| 0.5037 \| 550 \| 0.1176 \| - \| \| 0.5495 \| 600 \| 0.0894 \| - \| \| 0.5952 \| 650 \| 0.0639 \| - \| \| 0.6410 \| 700 \| 0.0575 \| - \| \| 0.6868 \| 750 \| 0.043 \| - \| \| 0.7326 \| 800 \| 0.0463 \| - \| \| 0.7784 \| 850 \| 0.0389 \| - \| \| 0.8242 \| 900 \| 0.0272 \| - \| \| 0.8700 \| 950 \| 0.0274 \| - \| \| 0.9158 \| 1000 \| 0.0299 \| - \| \| 0.9615 \| 1050 \| 0.0172 \| - \| \| 1.0073 \| 1100 \| 0.0217 \| - \| \| 1.0531 \| 1150 \| 0.017 \| - \| \| 1.0989 \| 1200 \| 0.0143 \| - \| \| 1.1447 \| 1250 \| 0.018 \| - \| \| 1.1905 \| 1300 \| 0.0109 \| - \| \| 1.2363 \| 1350 \| 0.0153 \| - \| \| 1.2821 \| 1400 \| 0.0099 \| - \| \| 1.3278 \| 1450 \| 0.012 \| - \| \| 1.3736 \| 1500 \| 0.0122 \| - \| \| 1.4194 \| 1550 \| 0.0158 \| - \| \| 1.4652 \| 1600 \| 0.0141 \| - \| \| 1.5110 \| 1650 \| 0.0108 \| - \| \| 1.5568 \| 1700 \| 0.0069 \| - \| \| 1.6026 \| 1750 \| 0.0071 \| - \| \| 1.6484 \| 1800 \| 0.0049 \| - \| \| 1.6941 \| 1850 \| 0.0099 \| - \| \| 1.7399 \| 1900 \| 0.0076 \| - \| \| 1.7857 \| 1950 \| 0.0028 \| - \| \| 1.8315 \| 2000 \| 0.0051 \| - \| \| 1.8773 \| 2050 \| 0.0027 \| - \| \| 1.9231 \| 2100 \| 0.0035 \| - \| \| 1.9689 \| 2150 \| 0.0032 \| - \| \| 2.0147 \| 2200 \| 0.0034 \| - \| \| 2.0604 \| 2250 \| 0.0028 \| - \| \| 2.1062 \| 2300 \| 0.002 \| - \| \| 2.1520 \| 2350 \| 0.0025 \| - \| \| 2.1978 \| 2400 \| 0.0014 \| - \| \| 2.2436 \| 2450 \| 0.0014 \| - \| \| 2.2894 \| 2500 \| 0.0011 \| - \| \| 2.3352 \| 2550 \| 0.0013 \| - \| \| 2.3810 \| 2600 \| 0.0013 \| - \| \| 2.4267 \| 2650 \| 0.0034 \| - \| \| 2.4725 \| 2700 \| 0.0024 \| - \| \| 2.5183 \| 2750 \| 0.0014 \| - \| \| 2.5641 \| 2800 \| 0.0007 \| - \| \| 2.6099 \| 2850 \| 0.0015 \| - \| \| 2.6557 \| 2900 \| 0.0007 \| - \| \| 2.7015 \| 2950 \| 0.0017 \| - \| \| 2.7473 \| 3000 \| 0.0001 \| - \| \| 2.7930 \| 3050 \| 0.002 \| - \| \| 2.8388 \| 3100 \| 0.0009 \| - \| \| 2.8846 \| 3150 \| 0.002 \| - \| \| 2.9304 \| 3200 \| 0.0008 \| - \| \| 2.9762 \| 3250 \| 0.0013 \| - \| ### Framework Versions - Python: 3.11.13 - SetFit: 1.1.3 - Sentence Transformers: 5.1.0 - Transformers: 4.55.2 - PyTorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## 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. -->
zhuojing-huang/gpt2-arabic-english-unfreeze	zhuojing-huang	2025-08-19T13:10:58Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T12:47:16Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: gpt2-arabic-english-unfreeze 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. --> # gpt2-arabic-english-unfreeze This model was trained from scratch 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 30 - training_steps: 1831 ### Training results ### Framework versions - Transformers 4.53.1 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.2
yaelahnal/blockassist-bc-mute_clawed_crab_1755608822	yaelahnal	2025-08-19T13:08:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:07:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/epicflashphotography_-flux	Muapi	2025-08-19T13:07:28Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:07:21Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # epiCFlashPhotography_[FLUX] ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:817640@914301", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/flux-film-foto	Muapi	2025-08-19T13:06:27Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:06:11Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Flux Film Foto ![preview](./preview.jpg) Base model: Flux.1 D Trained words: flmft photo style ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:646458@723205", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
lilTAT/blockassist-bc-gentle_rugged_hare_1755608508	lilTAT	2025-08-19T13:02:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:02:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yaelahnal/blockassist-bc-mute_clawed_crab_1755608206	yaelahnal	2025-08-19T13:01:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:57:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lilTAT/blockassist-bc-gentle_rugged_hare_1755608110	lilTAT	2025-08-19T12:55:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:55:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yaelahnal/blockassist-bc-mute_clawed_crab_1755607759	yaelahnal	2025-08-19T12:50:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:50:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lilTAT/blockassist-bc-gentle_rugged_hare_1755607761	lilTAT	2025-08-19T12:49:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:49:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755606016	vwzyrraz7l	2025-08-19T12:46:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:46:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Richnuts-2025a/Richnuts.in	Richnuts-2025a	2025-08-19T12:45:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T12:45:28Z	--- license: apache-2.0 ---
chutesai/Qwen3-235B-A22B-Instruct-2507-1M	chutesai	2025-08-19T12:42:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "conversational", "arxiv:2402.17463", "arxiv:2407.02490", "arxiv:2501.15383", "arxiv:2404.06654", "arxiv:2505.09388", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T12:28:48Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507/blob/main/LICENSE pipeline_tag: text-generation --- # Qwen3-235B-A22B-Instruct-2507 <a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Highlights We introduce the updated version of the Qwen3-235B-A22B non-thinking mode, named Qwen3-235B-A22B-Instruct-2507, featuring the following key enhancements: - Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage. - Substantial gains in long-tail knowledge coverage across multiple languages. - Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. - Enhanced capabilities in 256K long-context understanding. ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/62430a8522549d0917bfeb5a/0d7zztq4GB7G2ZYowO-dQ.jpeg) ## Model Overview Qwen3-235B-A22B-Instruct-2507 has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 235B in total and 22B activated - Number of Paramaters (Non-Embedding): 234B - Number of Layers: 94 - Number of Attention Heads (GQA): 64 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: 262,144 natively and extendable up to 1,010,000 tokens NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Performance \| \| Deepseek-V3-0324 \| GPT-4o-0327 \| Claude Opus 4 Non-thinking \| Kimi K2 \| Qwen3-235B-A22B Non-thinking \| Qwen3-235B-A22B-Instruct-2507 \| \|--- \| --- \| --- \| --- \| --- \| --- \| ---\| \| Knowledge \| \| \| \| \| \| \| \| MMLU-Pro \| 81.2 \| 79.8 \| 86.6 \| 81.1 \| 75.2 \| 83.0 \| \| MMLU-Redux \| 90.4 \| 91.3 \| 94.2 \| 92.7 \| 89.2 \| 93.1 \| \| GPQA \| 68.4 \| 66.9 \| 74.9 \| 75.1 \| 62.9 \| 77.5 \| \| SuperGPQA \| 57.3 \| 51.0 \| 56.5 \| 57.2 \| 48.2 \| 62.6 \| \| SimpleQA \| 27.2 \| 40.3 \| 22.8 \| 31.0 \| 12.2 \| 54.3 \| \| CSimpleQA \| 71.1 \| 60.2 \| 68.0 \| 74.5 \| 60.8 \| 84.3 \| \| Reasoning \| \| \| \| \| \| \| \| AIME25 \| 46.6 \| 26.7 \| 33.9 \| 49.5 \| 24.7 \| 70.3 \| \| HMMT25 \| 27.5 \| 7.9 \| 15.9 \| 38.8 \| 10.0 \| 55.4 \| \| ARC-AGI \| 9.0 \| 8.8 \| 30.3 \| 13.3 \| 4.3 \| 41.8 \| \| ZebraLogic \| 83.4 \| 52.6 \| - \| 89.0 \| 37.7 \| 95.0 \| \| LiveBench 20241125 \| 66.9 \| 63.7 \| 74.6 \| 76.4 \| 62.5 \| 75.4 \| \| Coding \| \| \| \| \| \| \| \| LiveCodeBench v6 (25.02-25.05) \| 45.2 \| 35.8 \| 44.6 \| 48.9 \| 32.9 \| 51.8 \| \| MultiPL-E \| 82.2 \| 82.7 \| 88.5 \| 85.7 \| 79.3 \| 87.9 \| \| Aider-Polyglot \| 55.1 \| 45.3 \| 70.7 \| 59.0 \| 59.6 \| 57.3 \| \| Alignment \| \| \| \| \| \| \| \| IFEval \| 82.3 \| 83.9 \| 87.4 \| 89.8 \| 83.2 \| 88.7 \| \| Arena-Hard v2* \| 45.6 \| 61.9 \| 51.5 \| 66.1 \| 52.0 \| 79.2 \| \| Creative Writing v3 \| 81.6 \| 84.9 \| 83.8 \| 88.1 \| 80.4 \| 87.5 \| \| WritingBench \| 74.5 \| 75.5 \| 79.2 \| 86.2 \| 77.0 \| 85.2 \| \| Agent \| \| \| \| \| \| \| \| BFCL-v3 \| 64.7 \| 66.5 \| 60.1 \| 65.2 \| 68.0 \| 70.9 \| \| TAU1-Retail \| 49.6 \| 60.3# \| 81.4 \| 70.7 \| 65.2 \| 71.3 \| \| TAU1-Airline \| 32.0 \| 42.8# \| 59.6 \| 53.5 \| 32.0 \| 44.0 \| \| TAU2-Retail \| 71.1 \| 66.7# \| 75.5 \| 70.6 \| 64.9 \| 74.6 \| \| TAU2-Airline \| 36.0 \| 42.0# \| 55.5 \| 56.5 \| 36.0 \| 50.0 \| \| TAU2-Telecom \| 34.0 \| 29.8# \| 45.2 \| 65.8 \| 24.6 \| 32.5 \| \| Multilingualism \| \| \| \| \| \| \| \| MultiIF \| 66.5 \| 70.4 \| - \| 76.2 \| 70.2 \| 77.5 \| \| MMLU-ProX \| 75.8 \| 76.2 \| - \| 74.5 \| 73.2 \| 79.4 \| \| INCLUDE \| 80.1 \| 82.1 \| - \| 76.9 \| 75.6 \| 79.5 \| \| PolyMATH \| 32.2 \| 25.5 \| 30.0 \| 44.8 \| 27.0 \| 50.2 \| : For reproducibility, we report the win rates evaluated by GPT-4.1. \#: Results were generated using GPT-4o-20241120, as access to the native function calling API of GPT-4o-0327 was unavailable. ## Quickstart The code of Qwen3-MoE has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3_moe' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-235B-A22B-Instruct-2507" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=16384 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) ``` For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B-Instruct-2507 --tp 8 --context-length 262144 ``` - vLLM: ```shell vllm serve Qwen/Qwen3-235B-A22B-Instruct-2507 --tensor-parallel-size 8 --max-model-len 262144 ``` Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.* For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python from qwen_agent.agents import Assistant # Define LLM llm_cfg = { 'model': 'Qwen3-235B-A22B-Instruct-2507', # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Processing Ultra-Long Texts To support ultra-long context processing (up to 1 million tokens), we integrate two key techniques: - [Dual Chunk Attention](https://arxiv.org/abs/2402.17463) (DCA): A length extrapolation method that splits long sequences into manageable chunks while preserving global coherence. - [MInference](https://arxiv.org/abs/2407.02490): A sparse attention mechanism that reduces computational overhead by focusing on critical token interactions. Together, these innovations significantly improve both generation quality and inference efficiency for sequences beyond 256K tokens. On sequences approaching 1M tokens, the system achieves up to a 3× speedup compared to standard attention implementations. For full technical details, see the [Qwen2.5-1M Technical Report](https://arxiv.org/abs/2501.15383). ### How to Enable 1M Token Context > [!NOTE] > To effectively process a 1 million token context, users will require approximately 1000 GB of total GPU memory. This accounts for model weights, KV-cache storage, and peak activation memory demands. #### Step 1: Update Configuration File Download the model and replace the content of your `config.json` with `config_1m.json`, which includes the config for length extrapolation and sparse attention. ```bash export MODELNAME=Qwen3-235B-A22B-Instruct-2507 huggingface-cli download Qwen/${MODELNAME} --local-dir ${MODELNAME} mv ${MODELNAME}/config.json ${MODELNAME}/config.json.bak mv ${MODELNAME}/config_1m.json ${MODELNAME}/config.json ``` #### Step 2: Launch Model Server After updating the config, proceed with either vLLM or SGLang for serving the model. #### Option 1: Using vLLM To run Qwen with 1M context support: ```bash pip install -U vllm \ --torch-backend=auto \ --extra-index-url https://wheels.vllm.ai/nightly ``` Then launch the server with Dual Chunk Flash Attention enabled: ```bash VLLM_ATTENTION_BACKEND=DUAL_CHUNK_FLASH_ATTN VLLM_USE_V1=0 \ vllm serve ./Qwen3-235B-A22B-Instruct-2507 \ --tensor-parallel-size 8 \ --max-model-len 1010000 \ --enable-chunked-prefill \ --max-num-batched-tokens 131072 \ --enforce-eager \ --max-num-seqs 1 \ --gpu-memory-utilization 0.85 ``` ##### Key Parameters \| Parameter \| Purpose \| \|--------\|--------\| \| `VLLM_ATTENTION_BACKEND=DUAL_CHUNK_FLASH_ATTN` \| Enables the custom attention kernel for long-context efficiency \| \| `--max-model-len 1010000` \| Sets maximum context length to ~1M tokens \| \| `--enable-chunked-prefill` \| Allows chunked prefill for very long inputs (avoids OOM) \| \| `--max-num-batched-tokens 131072` \| Controls batch size during prefill; balances throughput and memory \| \| `--enforce-eager` \| Disables CUDA graph capture (required for dual chunk attention) \| \| `--max-num-seqs 1` \| Limits concurrent sequences due to extreme memory usage \| \| `--gpu-memory-utilization 0.85` \| Set the fraction of GPU memory to be used for the model executor \| #### Option 2: Using SGLang First, clone and install the specialized branch: ```bash git clone https://github.com/sgl-project/sglang.git cd sglang pip install -e "python[all]" ``` Launch the server with DCA support: ```bash python3 -m sglang.launch_server \ --model-path ./Qwen3-235B-A22B-Instruct-2507 \ --context-length 1010000 \ --mem-frac 0.75 \ --attention-backend dual_chunk_flash_attn \ --tp 8 \ --chunked-prefill-size 131072 ``` ##### Key Parameters \| Parameter \| Purpose \| \|---------\|--------\| \| `--attention-backend dual_chunk_flash_attn` \| Activates Dual Chunk Flash Attention \| \| `--context-length 1010000` \| Defines max input length \| \| `--mem-frac 0.75` \| The fraction of the memory used for static allocation (model weights and KV cache memory pool). Use a smaller value if you see out-of-memory errors. \| \| `--tp 8` \| Tensor parallelism size (matches model sharding) \| \| `--chunked-prefill-size 131072` \| Prefill chunk size for handling long inputs without OOM \| #### Troubleshooting: 1. Encountering the error: "The model's max sequence length (xxxxx) is larger than the maximum number of tokens that can be stored in the KV cache." or "RuntimeError: Not enough memory. Please try to increase --mem-fraction-static." The VRAM reserved for the KV cache is insufficient. - vLLM: Consider reducing the ``max_model_len`` or increasing the ``tensor_parallel_size`` and ``gpu_memory_utilization``. Alternatively, you can reduce ``max_num_batched_tokens``, although this may significantly slow down inference. - SGLang: Consider reducing the ``context-length`` or increasing the ``tp`` and ``mem-frac``. Alternatively, you can reduce ``chunked-prefill-size``, although this may significantly slow down inference. 2. Encountering the error: "torch.OutOfMemoryError: CUDA out of memory." The VRAM reserved for activation weights is insufficient. You can try lowering ``gpu_memory_utilization`` or ``mem-frac``, but be aware that this might reduce the VRAM available for the KV cache. 3. Encountering the error: "Input prompt (xxxxx tokens) + lookahead slots (0) is too long and exceeds the capacity of the block manager." or "The input (xxx xtokens) is longer than the model's context length (xxx tokens)." The input is too lengthy. Consider using a shorter sequence or increasing the ``max_model_len`` or ``context-length``. #### Long-Context Performance We test the model on an 1M version of the [RULER](https://arxiv.org/abs/2404.06654) benchmark. \| Model Name \| Acc avg \| 4k \| 8k \| 16k \| 32k \| 64k \| 96k \| 128k \| 192k \| 256k \| 384k \| 512k \| 640k \| 768k \| 896k \| 1000k \| \|---------------------------------------------\|---------\|------\|------\|------\|------\|------\|------\|------\|------\|------\|------\|------\|------\|------\|------\|-------\| \| Qwen3-235B-A22B (Non-Thinking) \| 83.9 \| 97.7 \| 96.1 \| 97.5 \| 96.1 \| 94.2 \| 90.3 \| 88.5 \| 85.0 \| 82.1 \| 79.2 \| 74.4 \| 70.0 \| 71.0 \| 68.5 \| 68.0 \| \| Qwen3-235B-A22B-Instruct-2507 (Full Attention) \| 92.5 \| 98.5 \| 97.6 \| 96.9 \| 97.3 \| 95.8 \| 94.9 \| 93.9 \| 94.5 \| 91.0 \| 92.2 \| 90.9 \| 87.8 \| 84.8 \| 86.5 \| 84.5 \| \| Qwen3-235B-A22B-Instruct-2507 (Sparse Attention) \| 91.7 \| 98.5 \| 97.2 \| 97.3 \| 97.7 \| 96.6 \| 94.6 \| 92.8 \| 94.3 \| 90.5 \| 89.7 \| 89.5 \| 86.4 \| 83.6 \| 84.2 \| 82.5 \| * All models are evaluated with Dual Chunk Attention enabled. * Since the evaluation is time-consuming, we use 260 samples for each length (13 sub-tasks, 20 samples for each). ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - We suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. Adequate Output Length: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models. 3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, } @article{qwen2.5-1m, title={Qwen2.5-1M Technical Report}, author={An Yang and Bowen Yu and Chengyuan Li and Dayiheng Liu and Fei Huang and Haoyan Huang and Jiandong Jiang and Jianhong Tu and Jianwei Zhang and Jingren Zhou and Junyang Lin and Kai Dang and Kexin Yang and Le Yu and Mei Li and Minmin Sun and Qin Zhu and Rui Men and Tao He and Weijia Xu and Wenbiao Yin and Wenyuan Yu and Xiafei Qiu and Xingzhang Ren and Xinlong Yang and Yong Li and Zhiying Xu and Zipeng Zhang}, journal={arXiv preprint arXiv:2501.15383}, year={2025} } ```
uzunc/VLM	uzunc	2025-08-19T12:41:27Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:HuggingFaceTB/SmolVLM-Instruct", "base_model:finetune:HuggingFaceTB/SmolVLM-Instruct", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:25:59Z	--- base_model: HuggingFaceTB/SmolVLM-Instruct library_name: transformers model_name: VLM tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for VLM This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="uzunc/VLM", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Jacksss123/net72_uid234	Jacksss123	2025-08-19T12:41:01Z	0	0	transformers	[ "transformers", "tensorboard", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-08-19T12:38:56Z	--- 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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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]
Elsihj89/camila-keynnect	Elsihj89	2025-08-19T12:38:17Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T12:38:17Z	--- license: apache-2.0 ---
kimxxxx/mistral_r32_a32_b8_gas2_lr5e-5_4500tk_2epoch_newdata	kimxxxx	2025-08-19T12:37:04Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T12:36:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Dejiat/blockassist-bc-savage_unseen_bobcat_1755606723	Dejiat	2025-08-19T12:32:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:32:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
neko-llm/Qwen3-235B-test4	neko-llm	2025-08-19T12:32:22Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen3-235B-A22B", "base_model:finetune:Qwen/Qwen3-235B-A22B", "endpoints_compatible", "region:us" ]	null	2025-08-19T10:50:58Z	--- base_model: Qwen/Qwen3-235B-A22B library_name: transformers model_name: Qwen3-235B-test4 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen3-235B-test4 This model is a fine-tuned version of [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="neko-llm/Qwen3-235B-test4", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.54.1 - Pytorch: 2.6.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
yookty/blockassist-bc-subtle_hibernating_moose_1755605457	yookty	2025-08-19T12:25:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "subtle hibernating moose", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:25:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - subtle hibernating moose --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Vanbitcase/7b-150r-qwen2-vl	Vanbitcase	2025-08-19T12:24:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-to-text", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-to-text	2025-08-19T12:24:39Z	--- base_model: unsloth/qwen2-vl-7b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Vanbitcase - License: apache-2.0 - Finetuned from model : unsloth/qwen2-vl-7b-instruct-bnb-4bit This qwen2_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
New-Clip-prabh-viral-video/New.full.videos.prabh.Viral.Video.Official.Tutorial	New-Clip-prabh-viral-video	2025-08-19T12:24:33Z	0	0	null	[ "region:us" ]	null	2025-08-19T12:24:18Z	<a href="https://sdu.sk/AyL"><img src="https://files.qatarliving.com/event/2025/06/20/Jawan69_0-1749987397680.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
VoilaRaj/80_F75wiD	VoilaRaj	2025-08-19T12:23:29Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T12:19:29Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
VIDEOS-18-afreen-viral-Video-link/New.full.videos.afreen.Viral.Video.Official.Tutorial	VIDEOS-18-afreen-viral-Video-link	2025-08-19T12:20:11Z	0	0	null	[ "region:us" ]	null	2025-08-19T12:19:57Z	<a href="https://sdu.sk/AyL"><img src="https://files.qatarliving.com/event/2025/06/20/Jawan69_0-1749987397680.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
ishahaf/Llama-3.3-Nemotron-Super-49B-v1.5	ishahaf	2025-08-19T12:18:39Z	0	0	transformers	[ "transformers", "safetensors", "nemotron-nas", "text-generation", "nvidia", "llama-3", "pytorch", "conversational", "custom_code", "en", "arxiv:2411.19146", "arxiv:2505.00949", "arxiv:2502.00203", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2025-08-19T12:18:39Z	--- library_name: transformers license: other license_name: nvidia-open-model-license license_link: >- https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ pipeline_tag: text-generation language: - en tags: - nvidia - llama-3 - pytorch --- # Llama-3.3-Nemotron-Super-49B-v1.5 ![image](./accuracy_chart.png) ## Model Overview Llama-3.3-Nemotron-Super-49B-v1.5 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. The model supports a context length of 128K tokens. Llama-3.3-Nemotron-Super-49B-v1.5 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to [this paper](https://arxiv.org/abs/2411.19146) The model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Science, and Tool Calling. Additionally, the model went through multiple stages of Reinforcement Learning (RL) including Reward-aware Preference Optimization (RPO) for chat, Reinforcement Learning with Verifiable Rewards (RLVR) for reasoning, and iterative Direct Preference Optimization (DPO) for Tool Calling capability enhancements. The final checkpoint was achieved after merging several RL and DPO checkpoints. This model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here: - [Llama-3.1-Nemotron-Nano-4B-v1.1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1) - [Llama-3.1-Nemotron-Ultra-253B-v1](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1) This model is ready for commercial use. ## License/Terms of Use GOVERNING TERMS: Your use of this model is governed by the [NVIDIA Open Model License.](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama. Model Developer: NVIDIA Model Dates: Trained between November 2024 and July 2025 Data Freshness: The pretraining data has a cutoff of 2023 per Meta Llama 3.3 70B ## Deployment Geography Global ### Use Case: <br> Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks. <br> ### Release Date: <br> - Hugging Face 7/25/2025 via [Llama-3_3-Nemotron-Super-49B-v1_5](https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5) - build.nvidia.com 7/25/2025 [Llama-3_3-Nemotron-Super-49B-v1_5](https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1_5) ## References * [\[2505.00949\] Llama-Nemotron: Efficient Reasoning Models](https://arxiv.org/abs/2505.00949) * [\[2502.00203\] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment](https://arxiv.org/abs/2502.00203) * [\[2411.19146\]Puzzle: Distillation-Based NAS for Inference-Optimized LLMs](https://arxiv.org/abs/2411.19146) ## Model Architecture Architecture Type: Dense decoder-only Transformer model Network Architecture: Llama 3.3 70B Instruct, customized through Neural Architecture Search (NAS) The model is a derivative of Meta’s Llama-3.3-70B-Instruct, using Neural Architecture Search (NAS). The NAS algorithm results in non-standard and non-repetitive blocks. This includes the following: Skip attention: In some blocks, the attention is skipped entirely, or replaced with a single linear layer. Variable FFN: The expansion/compression ratio in the FFN layer is different between blocks. We utilize a block-wise distillation of the reference model, where for each block we create multiple variants providing different tradeoffs of quality vs. computational complexity, discussed in more depth below. We then search over the blocks to create a model which meets the required throughput and memory (optimized for a single H100-80GB GPU) while minimizing the quality degradation. The model then undergoes knowledge distillation (KD), with a focus on English single and multi-turn chat use-cases. The KD step included 40 billion tokens consisting of a mixture of 3 datasets - FineWeb, Buzz-V1.2 and Dolma. ## Intended use Llama-3.3-Nemotron-Super-49B-v1.5 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported. ## Input - Input Type: Text - Input Format: String - Input Parameters: One-Dimensional (1D) - Other Properties Related to Input: Context length up to 131,072 tokens ## Output - Output Type: Text - Output Format: String - Output Parameters: One-Dimensional (1D) - Other Properties Related to Output: Context length up to 131,072 tokens 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. ## Model Version 1.5 (07/25/2025) ## Software Integration - Runtime Engine: Transformers - Recommended Hardware Microarchitecture Compatibility: - NVIDIA Ampere - NVIDIA Hopper - Preferred Operating System(s): Linux ## Quick Start and Usage Recommendations: 1. By default (empty system prompt) the model will respond in reasoning ON mode. Setting `/no_think` in the system prompt will enable reasoning OFF mode. 2. We recommend setting temperature to `0.6`, and Top P to `0.95` for Reasoning ON mode 3. We recommend using greedy decoding for Reasoning OFF mode You can try this model out through the preview API, using this link: [Llama-3_3-Nemotron-Super-49B-v1_5](https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1_5). ## Use It with vLLM ```pip install vllm==0.9.2``` An example on how to serve with vLLM: ```console $ python3 -m vllm.entrypoints.openai.api_server \ --model "nvidia/Llama-3_3-Nemotron-Super-49B-v1_5" \ --trust-remote-code \ --seed=1 \ --host="0.0.0.0" \ --port=5000 \ --served-model-name "Llama-3_3-Nemotron-Super-49B-v1_5" \ --tensor-parallel-size=8 \ --max-model-len=65536 \ --gpu-memory-utilization 0.95 \ --enforce-eager ``` ### Running a vLLM Server with Tool-call Support To enable tool calling usage with this model, we provide a tool parser in the repository. Here is an example on how to use it: ```console $ git clone https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5 $ conda create -n vllm python=3.12 -y $ conda activate vllm $ pip install vllm==0.9.2 $ python3 -m vllm.entrypoints.openai.api_server \ --model Llama-3_3-Nemotron-Super-49B-v1_5 \ --trust-remote-code \ --seed=1 \ --host="0.0.0.0" \ --port=5000 \ --served-model-name "Llama-3_3-Nemotron-Super-49B-v1_5" \ --tensor-parallel-size=8 \ --max-model-len=65536 \ --gpu-memory-utilization 0.95 \ --enforce-eager \ --enable-auto-tool-choice \ --tool-parser-plugin "Llama-3_3-Nemotron-Super-49B-v1_5/llama_nemotron_toolcall_parser_no_streaming.py" \ --tool-call-parser "llama_nemotron_json" ``` After launching a vLLM server, you can call the server with tool-call support using a Python script like below. ```python from openai import OpenAI client = OpenAI( base_url="http://0.0.0.0:5000/v1", api_key="dummy", ) completion = client.chat.completions.create( model="Llama-3_3-Nemotron-Super-49B-v1_5", messages=[ {"role": "system", "content": ""}, {"role": "user", "content": "My bill is $100. What will be the amount for 18% tip?"} ], tools=[ { "type": "function", "function": { "name": "calculate_tip", "parameters": { "type": "object", "properties": { "bill_total": { "type": "integer", "description": "The total amount of the bill" }, "tip_percentage": { "type": "integer", "description": "The percentage of tip to be applied" } }, "required": ["bill_total", "tip_percentage"] } } }, { "type": "function", "function": { "name": "convert_currency", "parameters": { "type": "object", "properties": { "amount": { "type": "integer", "description": "The amount to be converted" }, "from_currency": { "type": "string", "description": "The currency code to convert from" }, "to_currency": { "type": "string", "description": "The currency code to convert to" } }, "required": ["from_currency", "amount", "to_currency"] } } } ], temperature=0.6, top_p=0.95, max_tokens=32768, stream=False ) print(completion.choices[0].message.content) ''' <think> Okay, let's see. The user has a bill of $100 and wants to know the amount for an 18% tip. Hmm, I need to calculate the tip based on the bill total and the percentage. The tools provided include calculate_tip, which takes bill_total and tip_percentage as parameters. So the bill_total here is 100, and the tip_percentage is 18. I should call the calculate_tip function with these values. Wait, do I need to check if the parameters are integers? The bill is $100, which is an integer, and 18% is also an integer. So that fits the function's requirements. I don't need to convert any currency here because the user is asking about a tip in the same currency. So the correct tool to use is calculate_tip with those parameters. </think> ''' print(completion.choices[0].message.tool_calls) ''' [ChatCompletionMessageToolCall(id='chatcmpl-tool-e341c6954d2c48c2a0e9071c7bdefd8b', function=Function(arguments='{"bill_total": 100, "tip_percentage": 18}', name='calculate_tip'), type='function')] ''' ``` ## Training and Evaluation Datasets ## Training Datasets A large variety of training data was used for the knowledge distillation phase before post-training pipeline, 3 of which included: FineWeb, Buzz-V1.2, and Dolma. The data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model. Prompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both reasoning on and off modes, to train the model to distinguish between two modes. We have released our [Nemotron-Post-Training-Dataset-v1](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v1) to promote openness and transparency in model development and improvement. Data Collection for Training Datasets: Hybrid: Automated, Human, Synthetic Data Labeling for Training Datasets: Hybrid: Automated, Human, Synthetic ## Evaluation Datasets We used the datasets listed below to evaluate Llama-3.3-Nemotron-Super-49B-v1.5. Data Collection for Evaluation Datasets: - Hybrid: Human. Synthetic Data Labeling for Evaluation Datasets: - Hybrid: Human, Synthetic, Automatic ## Evaluation Results We evaluate the model using temperature=`0.6`, top_p=`0.95`, and 64k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate. ### MATH500 \| Reasoning Mode \| pass@1 (avg. over 4 runs) \| \|--------------\|------------\| \| Reasoning On \| 97.4 \| ### AIME 2024 \| Reasoning Mode \| pass@1 (avg. over 16 runs) \| \|--------------\|------------\| \| Reasoning On \| 87.5 \| ### AIME 2025 \| Reasoning Mode \| pass@1 (avg. over 16 runs) \| \|--------------\|------------\| \| Reasoning On \| 82.71 \| ### GPQA \| Reasoning Mode \| pass@1 (avg. over 4 runs) \| \|--------------\|------------\| \| Reasoning On \| 71.97 \| ### LiveCodeBench 24.10-25.02 \| Reasoning Mode \| pass@1 (avg. over 4 runs) \| \|--------------\|------------\| \| Reasoning On \| 73.58 \| ### BFCL v3 \| Reasoning Mode \| pass@1 (avg. over 2 runs) \| \|--------------\|------------\| \| Reasoning On \| 71.75 \| ### IFEval \| Reasoning Mode \| Strict:Instruction \| \|--------------\|------------\| \| Reasoning On \| 88.61 \| ### ArenaHard \| Reasoning Mode \| pass@1 (avg. over 1 runs) \| \|--------------\|------------\| \| Reasoning On \| 92.0 \| ### Humanity's Last Exam (Text-Only Subset) \| Reasoning Mode \| pass@1 (avg. over 1 runs) \| \|--------------\|------------\| \| Reasoning On \| 7.64 \| ### MMLU Pro (CoT) \| Reasoning Mode \| pass@1 (avg. over 1 runs) \| \|--------------\|------------\| \| Reasoning On \| 79.53 \| All evaluations were done using the [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills) repository. ## Inference: Engine: - Transformers Test Hardware: - 2x NVIDIA H100-80GB - 2x NVIDIA A100-80GB GPUs ## 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. For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](./EXPLAINABILITY.md), [Bias](./BIAS.md), [Safety & Security](./SAFETY&SECURITY.md), and [Privacy](./PRIVACY.md) Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). ## Citation ``` @misc{bercovich2025llamanemotronefficientreasoningmodels, title={Llama-Nemotron: Efficient Reasoning Models}, author={Akhiad Bercovich and Itay Levy and Izik Golan and Mohammad Dabbah and Ran El-Yaniv and Omri Puny and Ido Galil and Zach Moshe and Tomer Ronen and Najeeb Nabwani and Ido Shahaf and Oren Tropp and Ehud Karpas and Ran Zilberstein and Jiaqi Zeng and Soumye Singhal and Alexander Bukharin and Yian Zhang and Tugrul Konuk and Gerald Shen and Ameya Sunil Mahabaleshwarkar and Bilal Kartal and Yoshi Suhara and Olivier Delalleau and Zijia Chen and Zhilin Wang and David Mosallanezhad and Adi Renduchintala and Haifeng Qian and Dima Rekesh and Fei Jia and Somshubra Majumdar and Vahid Noroozi and Wasi Uddin Ahmad and Sean Narenthiran and Aleksander Ficek and Mehrzad Samadi and Jocelyn Huang and Siddhartha Jain and Igor Gitman and Ivan Moshkov and Wei Du and Shubham Toshniwal and George Armstrong and Branislav Kisacanin and Matvei Novikov and Daria Gitman and Evelina Bakhturina and Jane Polak Scowcroft and John Kamalu and Dan Su and Kezhi Kong and Markus Kliegl and Rabeeh Karimi and Ying Lin and Sanjeev Satheesh and Jupinder Parmar and Pritam Gundecha and Brandon Norick and Joseph Jennings and Shrimai Prabhumoye and Syeda Nahida Akter and Mostofa Patwary and Abhinav Khattar and Deepak Narayanan and Roger Waleffe and Jimmy Zhang and Bor-Yiing Su and Guyue Huang and Terry Kong and Parth Chadha and Sahil Jain and Christine Harvey and Elad Segal and Jining Huang and Sergey Kashirsky and Robert McQueen and Izzy Putterman and George Lam and Arun Venkatesan and Sherry Wu and Vinh Nguyen and Manoj Kilaru and Andrew Wang and Anna Warno and Abhilash Somasamudramath and Sandip Bhaskar and Maka Dong and Nave Assaf and Shahar Mor and Omer Ullman Argov and Scot Junkin and Oleksandr Romanenko and Pedro Larroy and Monika Katariya and Marco Rovinelli and Viji Balas and Nicholas Edelman and Anahita Bhiwandiwalla and Muthu Subramaniam and Smita Ithape and Karthik Ramamoorthy and Yuting Wu and Suguna Varshini Velury and Omri Almog and Joyjit Daw and Denys Fridman and Erick Galinkin and Michael Evans and Katherine Luna and Leon Derczynski and Nikki Pope and Eileen Long and Seth Schneider and Guillermo Siman and Tomasz Grzegorzek and Pablo Ribalta and Monika Katariya and Joey Conway and Trisha Saar and Ann Guan and Krzysztof Pawelec and Shyamala Prayaga and Oleksii Kuchaiev and Boris Ginsburg and Oluwatobi Olabiyi and Kari Briski and Jonathan Cohen and Bryan Catanzaro and Jonah Alben and Yonatan Geifman and Eric Chung and Chris Alexiuk}, year={2025}, eprint={2505.00949}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.00949}, } ```
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755604006	vwzyrraz7l	2025-08-19T12:14:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:14:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
LBST/t10_pick_and_place_smolvla_017000	LBST	2025-08-19T12:13:09Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-017000", "region:us" ]	robotics	2025-08-19T12:13:04Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-017000 --- # T08 Pick and Place Policy - Checkpoint 017000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 017000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 017000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_017000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 017000
lilTAT/blockassist-bc-gentle_rugged_hare_1755605480	lilTAT	2025-08-19T12:11:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:11:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Prerna43/distilbert-base-uncased-lora-text-classification	Prerna43	2025-08-19T12:10:33Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "base_model:adapter:distilbert-base-uncased", "lora", "transformers", "base_model:distilbert/distilbert-base-uncased", "base_model:adapter:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2025-08-19T12:03:59Z	--- library_name: peft license: apache-2.0 base_model: distilbert-base-uncased tags: - base_model:adapter:distilbert-base-uncased - lora - transformers metrics: - accuracy model-index: - name: distilbert-base-uncased-lora-text-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-lora-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6438 - Accuracy: 0.887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use 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 \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 250 \| 0.5865 \| 0.891 \| \| 0.051 \| 2.0 \| 500 \| 0.6101 \| 0.888 \| \| 0.051 \| 3.0 \| 750 \| 0.6309 \| 0.889 \| \| 0.1059 \| 4.0 \| 1000 \| 0.6438 \| 0.887 \| ### Framework versions - PEFT 0.17.0 - Transformers 4.55.2 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755603876	lisaozill03	2025-08-19T12:10:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:09:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Unlearning/early-unlearning-weak-filter-ga-1-in-209-ga-lr-scale-0_001-gclip-0_5	Unlearning	2025-08-19T12:07:52Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T15:07:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
LBST/t10_pick_and_place_smolvla_003000	LBST	2025-08-19T12:07:09Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-003000", "region:us" ]	robotics	2025-08-19T12:07:04Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-003000 --- # T08 Pick and Place Policy - Checkpoint 003000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 003000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 003000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_003000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 003000
ketir/Emotion-Detection-BiLSTM	ketir	2025-08-19T12:06:53Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-18T19:50:03Z	--- license: apache-2.0 --- --- license: apache-2.0 tags: - text-classification - emotion-classification - indonesian - bilstm - tensorflow language: - id pipeline_tag: text-classification widget: - text: "Saya sangat bahagia hari ini!" - text: "Aku merasa sedih sekali" - text: "Mengapa ini terjadi? Aku marah!" --- # Indonesian Emotion Classification with BiLSTM ## Model Description This model is a Bidirectional LSTM (BiLSTM) trained for emotion classification on Indonesian text. The model can predict 6 different emotions from text input. ## Supported Emotions - `angry` - Marah - `fear` - Takut - `happy` - Bahagia - `love` - Cinta - `sad` - Sedih ## Usage ```python from transformers import pipeline # Load the model classifier = pipeline("text-classification", model="your-username/your-model-name", return_all_scores=True) # Predict emotion result = classifier("Saya sangat bahagia hari ini!") print(result)

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