librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
AnerYubo/blockassist-bc-reptilian_bellowing_cockroach_1755745453	AnerYubo	2025-08-21T03:04:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reptilian bellowing cockroach", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T03:04:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reptilian bellowing cockroach --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yk0/forge-e48	yk0	2025-08-21T03:03:46Z	0	0	null	[ "pytorch", "region:us" ]	null	2025-08-21T03:02:33Z	# forge-v1 Model Private testing version.
liukevin666/blockassist-bc-yawning_striped_cassowary_1755745023	liukevin666	2025-08-21T03:02:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:58:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755743784	rvipitkirubbe	2025-08-21T03:01:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mottled foraging ape", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T03:01:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mottled foraging ape --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755745209	IvanJAjebu	2025-08-21T03:01:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T03:01:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
thalhathai/distilbert-base-uncased-finetuned-emotion	thalhathai	2025-08-21T03:00:41Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-21T02:32:06Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755743635	hakimjustbao	2025-08-21T03:00:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T03:00:10Z	--- 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).
g-assismoraes/Qwen3-4B-Base-faquad	g-assismoraes	2025-08-21T03:00:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-4B-Base", "base_model:finetune:Qwen/Qwen3-4B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-21T02:02:09Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-4B-Base tags: - generated_from_trainer model-index: - name: Qwen3-4B-Base-faquad 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. --> # Qwen3-4B-Base-faquad This model is a fine-tuned version of [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9271 ## 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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2827 \| 1.0 \| 782 \| 1.3027 \| \| 0.142 \| 2.0 \| 1564 \| 1.5038 \| \| 0.0994 \| 3.0 \| 2346 \| 1.6675 \| \| 0.087 \| 4.0 \| 3128 \| 1.7409 \| \| 0.0803 \| 5.0 \| 3910 \| 1.8365 \| \| 0.0807 \| 6.0 \| 4692 \| 1.8826 \| \| 0.0763 \| 7.0 \| 5474 \| 1.9170 \| \| 0.0764 \| 8.0 \| 6256 \| 1.9271 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
roeker/blockassist-bc-quick_wiry_owl_1755745059	roeker	2025-08-21T02:58:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:58:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rourkerhotmail1/blockassist-bc-stalking_scruffy_walrus_1755742822	rourkerhotmail1	2025-08-21T02:54:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stalking scruffy walrus", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:54:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stalking scruffy walrus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
germanlunichh/blockassist-bc-mute_shaggy_alligator_1755742698	germanlunichh	2025-08-21T02:51:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute shaggy alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:50:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute shaggy alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755743073	lisaozill03	2025-08-21T02:49:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:49:49Z	--- 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).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755742849	kojeklollipop	2025-08-21T02:48:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:48:30Z	--- 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).
jakehsv/blockassist-bc-flexible_waddling_peacock_1755742765	jakehsv	2025-08-21T02:48:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flexible waddling peacock", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:48:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flexible waddling peacock --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1755744350	liukevin666	2025-08-21T02:47:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:46:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # 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_1755742548	chainway9	2025-08-21T02:43:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:43:07Z	--- 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).
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755744113	lqpl	2025-08-21T02:43:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:42:50Z	--- 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).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755742440	coelacanthxyz	2025-08-21T02:42:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:42:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kevinshin/hunyuan-1.8b-it-dpo-lr-1e-6-batch-16-epoch-1-wildchat-cw-3k	kevinshin	2025-08-21T02:40:52Z	0	0	transformers	[ "transformers", "safetensors", "hunyuan_v1_dense", "text-generation", "generated_from_trainer", "dpo", "trl", "conversational", "dataset:kevinshin/wildchat-creative-writing-3k-pref", "arxiv:2305.18290", "base_model:tencent/Hunyuan-1.8B-Instruct", "base_model:finetune:tencent/Hunyuan-1.8B-Instruct", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T22:30:37Z	--- base_model: tencent/Hunyuan-1.8B-Instruct datasets: kevinshin/wildchat-creative-writing-3k-pref library_name: transformers model_name: hunyuan-1.8b-it-dpo-lr-1e-6-batch-16-epoch-1-wildchat-cw-3k tags: - generated_from_trainer - dpo - trl licence: license --- # Model Card for hunyuan-1.8b-it-dpo-lr-1e-6-batch-16-epoch-1-wildchat-cw-3k This model is a fine-tuned version of [tencent/Hunyuan-1.8B-Instruct](https://huggingface.co/tencent/Hunyuan-1.8B-Instruct) on the [kevinshin/wildchat-creative-writing-3k-pref](https://huggingface.co/datasets/kevinshin/wildchat-creative-writing-3k-pref) 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="kevinshin/hunyuan-1.8b-it-dpo-lr-1e-6-batch-16-epoch-1-wildchat-cw-3k", 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/myungjune-sogang-university/general_remo_train/runs/qpgof8ie) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.19.1 - Transformers: 4.55.0.dev0 - Pytorch: 2.6.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.2 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755742244	calegpedia	2025-08-21T02:39:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:38:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755743751	lqpl	2025-08-21T02:39:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:36:47Z	--- 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).
JihadSaoudQMUL/deberta-bias-detection	JihadSaoudQMUL	2025-08-21T02:38:34Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:microsoft/deberta-base", "lora", "transformers", "arxiv:1910.09700", "base_model:microsoft/deberta-base", "region:us" ]	null	2025-08-21T02:35:35Z	--- base_model: microsoft/deberta-base library_name: peft tags: - base_model:adapter:microsoft/deberta-base - lora - transformers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
roeker/blockassist-bc-quick_wiry_owl_1755743831	roeker	2025-08-21T02:37:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:37:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755743632	0xaoyama	2025-08-21T02:34:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:34:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pkshatech/GLuCoSE-base-ja	pkshatech	2025-08-21T02:34:17Z	50,548	32	sentence-transformers	[ "sentence-transformers", "pytorch", "luke", "feature-extraction", "transformers", "sentence-similarity", "ja", "dataset:mc4", "dataset:clips/mqa", "dataset:shunk031/JGLUE", "dataset:paws-x", "dataset:MoritzLaurer/multilingual-NLI-26lang-2mil7", "dataset:castorini/mr-tydi", "dataset:hpprc/jsick", "arxiv:2104.07179", "arxiv:2004.04906", "base_model:studio-ousia/luke-japanese-base-lite", "base_model:finetune:studio-ousia/luke-japanese-base-lite", "license:apache-2.0", "autotrain_compatible", "region:us" ]	sentence-similarity	2023-07-16T07:28:46Z	--- pipeline_tag: sentence-similarity language: ja license: apache-2.0 tags: - transformers - sentence-similarity - feature-extraction - sentence-transformers inference: false datasets: - mc4 - clips/mqa - shunk031/JGLUE - paws-x - MoritzLaurer/multilingual-NLI-26lang-2mil7 - castorini/mr-tydi - hpprc/jsick base_model: - studio-ousia/luke-japanese-base-lite --- # GLuCoSE (General Luke-based Contrastive Sentence Embedding)-base-Japanese [日本語のREADME/Japanese README](https://huggingface.co/pkshatech/GLuCoSE-base-ja/blob/main/README_JA.md) GLuCoSE (General LUke-based COntrastive Sentence Embedding, "glucose") is a Japanese text embedding model based on [LUKE](https://github.com/studio-ousia/luke). In order to create a general-purpose, user-friendly Japanese text embedding model, GLuCoSE has been trained on a mix of web data and various datasets associated with natural language inference and search. This model is not only suitable for sentence vector similarity tasks but also for semantic search tasks. - Maximum token count: 512 - Output dimension: 768 - Pooling: mean pooling - Supported language: Japanese ## Usage You can use this model easily with [sentence-transformers](https://www.SBERT.net). First, install sentence-transformers with pip as follows: ``` pip install -U sentence-transformers ``` You can load the model and convert sentences into dense vectors as shown below: ```python from sentence_transformers import SentenceTransformer sentences = [ "PKSHA Technologyは機械学習/深層学習技術に関わるアルゴリズムソリューションを展開している。", "この深層学習モデルはPKSHA Technologyによって学習され、公開された。", "広目天は、仏教における四天王の一尊であり、サンスクリット語の「種々の眼をした者」を名前の由来とする。", ] model = SentenceTransformer('pkshatech/GLuCoSE-base-ja') embeddings = model.encode(sentences) print(embeddings) ``` Since the loss function used during training is cosine similarity, we recommend using cosine similarity for downstream tasks. This text embedding model can also be used in LangChain. Please refer to [this page](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers) for more information. ## Resources Used The following resources were used to train this model. ### Pre-trained model - [studio-ousia/luke-japanese-base-lite](https://huggingface.co/studio-ousia/luke-japanese-base-lite) ### Datasets - [mC4](https://huggingface.co/datasets/mc4) - [MQA](https://huggingface.co/datasets/clips/mqa) - [JNLI](https://github.com/yahoojapan/JGLUE) - [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [JSeM](https://github.com/DaisukeBekki/JSeM) - [MoritzLaurer/multilingual-NLI-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) - [MultiNLI](https://huggingface.co/datasets/multi_nli) - [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) - [FeverNLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md) - [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) - [JSICK](https://github.com/verypluming/JSICK) - [Mr.Tidy](https://huggingface.co/datasets/castorini/mr-tydi) - [JSTS](https://github.com/yahoojapan/JGLUE) (used for validation) [^1] ## Benchmarks ### Semantic Similarity Calculation ([JSTS](https://github.com/yahoojapan/JGLUE) dev set) Evaluation by Spearman's correlation coefficient and Pearson's correlation coefficient. \| Model \| Spearman \| Pearson \| \| --- \| --- \| --- \| \| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) \|0.837[^2] \| 0.790[^2] \| \| [pkshatech/simcse-ja-bert-base-clcmlp](https://huggingface.co/pkshatech/simcse-ja-bert-base-clcmlp)[^3] \| 0.850 \| 0.801 \| \| pkshatech/GLuCoSE-base-ja \| 0.864 \| 0.818 \| ### Zero-shot Search ([AIO3](https://sites.google.com/view/project-aio/competition3?authuser=0) dev set) Evaluation by top-k retrieval accuracy[^4] (the fraction of questions that have a correct answer in the top-k retrieved documents at least once.) \| Model \| Top-1 \| Top-5 \| Top-10 \| Top-50 \| \| --- \| --- \| --- \| --- \| --- \| \| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) \| 33.50 \| 57.80 \| 65.10 \| 76.60 \| \| [pkshatech/simcse-ja-bert-base-clcmlp](https://huggingface.co/pkshatech/simcse-ja-bert-base-clcmlp)[^3] \| 30.60 \| 54.50 \| 62.50 \| 76.70 \| \| pkshatech/GLuCoSE-base-ja \| 36.10 \| 59.40 \| 66.40 \| 78.30 \| # Authors [Akihiko Fukuchi](https://huggingface.co/akiFQC), [Yuichiro Hoshino](https://huggingface.co/Yuichiroh), [Yotarow Watanabe](https://huggingface.co/yotarow) ## Citation ``` @misc{pkshatech-GLuCoSE-base-ja, title={pkshatech/GLuCoSE-base-ja}, url={https://huggingface.co/pkshatech/GLuCoSE-base-ja}, author={Akihiko Fukuchi, Yuichiro Hoshino, Yotarow Watanabe}, year={2023}, } ``` ## License This model is published under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). [^1]: When we trained this model, the test data of JGLUE was not released, so we used the dev set of JGLUE as a private evaluation data. Therefore, we selected the checkpoint on the train set of JGLUE insted of its dev set. [^2]: https://qiita.com/akeyhero/items/ce371bfed64399027c23 [^3]: This is the model we have released before. [^4]: For more details, please refer to https://arxiv.org/pdf/2004.04906.pdf.
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755743552	IvanJAjebu	2025-08-21T02:33:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:33:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755741980	ihsanridzi	2025-08-21T02:31:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:31:28Z	--- 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).
wrl2003/test-ltf	wrl2003	2025-08-21T02:29:12Z	0	0	null	[ "region:us" ]	null	2025-08-21T01:48:53Z	--- title: Test Hugsim Web Server emoji: 📈 colorFrom: purple colorTo: yellow sdk: docker pinned: false ---
roeker/blockassist-bc-quick_wiry_owl_1755743226	roeker	2025-08-21T02:28:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:27:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # 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_1755741725	hakimjustbao	2025-08-21T02:27:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:27:51Z	--- 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).
Team-ACE/ToolACE-2.5-Llama-3.1-8B	Team-ACE	2025-08-21T02:27:46Z	1	0	null	[ "safetensors", "llama", "code", "en", "dataset:Team-ACE/ToolACE", "arxiv:2409.00920", "arxiv:2504.01400", "arxiv:2505.07512", "arxiv:2508.12685", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-08-20T13:36:06Z	--- license: apache-2.0 datasets: - Team-ACE/ToolACE language: - en metrics: - accuracy base_model: meta-llama/Meta-Llama-3.1-8B-Instruct tags: - code --- # ToolACE-2.5-Llama-3.1-8B ToolACE-2.5-Llama-3.1-8B is a fine-tuned model of LLaMA-3.1-8B-Instruct with our dataset [ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE) tailored for tool usage. Compared with [ToolACE-2](https://huggingface.co/Team-ACE/ToolACE-2-Llama-3.1-8B), ToolACE-2.5-8B enhances the multi-turn tool-usage ability. ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data. ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs. Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process. To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks. More details can be found in our paper: [ToolACE: Winning the Points of LLM Function Calling](https://arxiv.org/abs/2409.00920) ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/66bf01f45bdd611f9a602087/WmyWOYtg_dbTgwQmvlqcz.jpeg) More techniques are applied to further improve tool-usage ability, including: - [ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning](https://arxiv.org/abs/2504.01400) - [ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution](https://arxiv.org/abs/2505.07512) - [ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction](https://arxiv.org/abs/2508.12685) ### Usage Here we provide a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate function calling with given functions. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Team-ACE/ToolACE-2.5-Llama-3.1-8B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype='auto', device_map='auto' ) # You can modify the prompt for your task system_prompt = """You are an expert in composing functions. You are given a question and a set of possible functions. Based on the question, you will need to make one or more function/tool calls to achieve the purpose. If none of the function can be used, point it out. If the given question lacks the parameters required by the function, also point it out. You should only return the function call in tools call sections. If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)] You SHOULD NOT include any other text in the response. Here is a list of functions in JSON format that you can invoke.\n{functions}\n """ # User query query = "Find me the sales growth rate for company XYZ for the last 3 years and also the interest coverage ratio for the same duration." # Availabel tools in JSON format (OpenAI-format) tools = [ { "name": "financial_ratios.interest_coverage", "description": "Calculate a company's interest coverage ratio given the company name and duration", "arguments": { "type": "dict", "properties": { "company_name": { "type": "string", "description": "The name of the company." }, "years": { "type": "integer", "description": "Number of past years to calculate the ratio." } }, "required": ["company_name", "years"] } }, { "name": "sales_growth.calculate", "description": "Calculate a company's sales growth rate given the company name and duration", "arguments": { "type": "dict", "properties": { "company": { "type": "string", "description": "The company that you want to get the sales growth rate for." }, "years": { "type": "integer", "description": "Number of past years for which to calculate the sales growth rate." } }, "required": ["company", "years"] } }, { "name": "weather_forecast", "description": "Retrieve a weather forecast for a specific location and time frame.", "arguments": { "type": "dict", "properties": { "location": { "type": "string", "description": "The city that you want to get the weather for." }, "days": { "type": "integer", "description": "Number of days for the forecast." } }, "required": ["location", "days"] } } ] messages = [ {'role': 'system', 'content': system_prompt.format(functions=tools)}, {'role': 'user', 'content': query} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` Then you should be able to see the following output functional calls: ``` [sales_growth.calculate(company="XYZ", years=3), financial_ratios.interest_coverage(company_name="XYZ", years=3)] ``` ### Citation If you think ToolACE is useful in your work, please cite our paper: ``` @inproceedings{ liu2025toolace, title={Tool{ACE}: Winning the Points of {LLM} Function Calling}, author={Weiwen Liu and Xu Huang and Xingshan Zeng and xinlong hao and Shuai Yu and Dexun Li and Shuai Wang and Weinan Gan and Zhengying Liu and Yuanqing Yu and Zezhong WANG and Yuxian Wang and Wu Ning and Yutai Hou and Bin Wang and Chuhan Wu and Wang Xinzhi and Yong Liu and Yasheng Wang and Duyu Tang and Dandan Tu and Lifeng Shang and Xin Jiang and Ruiming Tang and Defu Lian and Qun Liu and Enhong Chen}, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, url={https://openreview.net/forum?id=8EB8k6DdCU} } ``` Additionally, please check our other related works whose techniques are applied in ToolACE-2.5-8B: ``` @article{zeng2025toolacer, title={ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning}, author={Zeng, Xingshan and Liu, Weiwen and Huang, Xu and Wang, Zezhong and Wang, Lingzhi and Li, Liangyou and Wang, Yasheng and Shang, Lifeng and Jiang, Xin and Tang, Ruiming and Liu, Qun}, journal={arXiv preprint arXiv:2504.01400}, year={2025} } ``` ``` @article{huang2025toolace, title={ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution}, author={Huang, Xu and Liu, Weiwen and Zeng, Xingshan and Huang, Yuefeng and Hao, Xinlong and Wang, Yuxian and Zeng, Yirong and Wu, Chuhan and Wang, Yasheng and Tang, Ruiming and Lian, Defu}, journal={arXiv preprint arXiv:2505.07512}, year={2025} } ``` ``` @article{zeng2025toolacemt, title={ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction}, author={Zeng, Xingshan and Liu, Weiwen and Wang, Lingzhi and Li, Liangyou and Mi, Fei and Wang, Yasheng and Shang, Lifeng and Jiang, Xin and Liu, Qun}, journal={arXiv preprint arXiv:2508.12685}, year={2025} } ```
lightningpal/epi-derm2	lightningpal	2025-08-21T02:24:47Z	0	0	transformers	[ "transformers", "safetensors", "resnet", "image-classification", "vision", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-08-21T02:21:08Z	--- pipeline_tag: image-classification library_name: transformers tags: - image-classification - vision --- # 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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IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755742982	IvanJAjebu	2025-08-21T02:24:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:24:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yidingp/qwen2_coder_7b_apps_finetuned	yidingp	2025-08-21T02:23:56Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:Qwen/Qwen2.5-Coder-7B", "base_model:finetune:Qwen/Qwen2.5-Coder-7B", "endpoints_compatible", "region:us" ]	null	2025-08-21T02:23:50Z	--- base_model: Qwen/Qwen2.5-Coder-7B library_name: transformers model_name: qwen2_coder_7b_apps_finetuned tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for qwen2_coder_7b_apps_finetuned This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B). 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="yidingp/qwen2_coder_7b_apps_finetuned", 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+cu126 - 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}} } ```
roeker/blockassist-bc-quick_wiry_owl_1755742912	roeker	2025-08-21T02:22:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:22:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # 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_1755741387	helmutsukocok	2025-08-21T02:22:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:22:22Z	--- 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).
luiz826/MichaelScottGenFinal	luiz826	2025-08-21T02:22:17Z	26	0	transformers	[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-12-13T04:22:34Z	# This is a Michael Scott Generator ![Michael Scott](https://media.tenor.com/BFLvU0UB74AAAAAM/office-sentence.gif) We made this project for the NLP course on Federal University of Paraíba. Contact me 👋: https://luiz826.github.io/
afung/pika-pick-and-place-ee_absolute-fisheye	afung	2025-08-21T02:20:57Z	0	0	lerobot	[ "lerobot", "safetensors", "diffusion", "robotics", "dataset:afung/pika-pick-and-place-ee_delta_gripper-fisheye", "arxiv:2303.04137", "license:apache-2.0", "region:us" ]	robotics	2025-08-21T02:17:28Z	--- datasets: afung/pika-pick-and-place-ee_delta_gripper-fisheye library_name: lerobot license: apache-2.0 model_name: diffusion pipeline_tag: robotics tags: - diffusion - robotics - lerobot --- # Model Card for diffusion <!-- Provide a quick summary of what the model is/does. --> [Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. 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
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755741319	lisaozill03	2025-08-21T02:19:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:19:40Z	--- 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).
leo12757/llama3.2_3B_news_merged	leo12757	2025-08-21T02:18:53Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T02:18:53Z	--- license: apache-2.0 ---
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755742626	IvanJAjebu	2025-08-21T02:18:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:18:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755742603	roeker	2025-08-21T02:18:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:17:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
manfred2015/UUU-Finetune-GPt2	manfred2015	2025-08-21T02:17:44Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T02:17:43Z	--- license: apache-2.0 ---
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755740928	katanyasekolah	2025-08-21T02:17:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:17:27Z	--- 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).
AlphaMine00/blockassist-bc-flapping_beaked_zebra_1755742537	AlphaMine00	2025-08-21T02:17:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping beaked zebra", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:16:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping beaked zebra --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
koloni/blockassist-bc-deadly_graceful_stingray_1755741080	koloni	2025-08-21T02:17:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:16:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
andy013567/gemma-3-1b-it-classifier-finetune	andy013567	2025-08-21T02:15:43Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "unsloth", "trl", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-08-20T00:42:53Z	--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit library_name: transformers model_name: gemma-3-1b-it-classifier-finetune tags: - generated_from_trainer - sft - unsloth - trl licence: license --- # Model Card for gemma-3-1b-it-classifier-finetune This model is a fine-tuned version of [unsloth/gemma-3-1b-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3-1b-it-unsloth-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="andy013567/gemma-3-1b-it-classifier-finetune", 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/anhbui5302/huggingface/runs/rd8egtwy) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0+cu126 - Datasets: 3.6.0 - 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}} } ```
iBush/llama3.2_3B_news_qlora	iBush	2025-08-21T02:15:20Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "region:us" ]	text-generation	2025-08-21T01:49:31Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Fattyfish/llama3.2_3B_news_qlora	Fattyfish	2025-08-21T02:14:36Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "region:us" ]	text-generation	2025-08-21T02:11:29Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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linlinlin000/Llama3.2_3B_news_qlora	linlinlin000	2025-08-21T02:13:17Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "region:us" ]	text-generation	2025-08-21T01:52:18Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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rriicckk/llama3.2_3B_news_qlora	rriicckk	2025-08-21T02:12:25Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "region:us" ]	text-generation	2025-08-21T01:52:22Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - 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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roeker/blockassist-bc-quick_wiry_owl_1755742299	roeker	2025-08-21T02:12:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:12:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
chen95427/llama3.2_3B_news_qlora	chen95427	2025-08-21T02:11:34Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T02:11:34Z	--- license: apache-2.0 ---
junyi080914/llama3.2_3B_news_merged	junyi080914	2025-08-21T02:11:10Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T02:11:10Z	--- license: apache-2.0 ---
lautan/blockassist-bc-gentle_patterned_goat_1755740594	lautan	2025-08-21T02:09:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:09:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
18-Clips-Sophie-Rain-Viral-video-original/New.full.videos.Sophie.Rain.Spiderman.Viral.Video.Official.Tutorial	18-Clips-Sophie-Rain-Viral-video-original	2025-08-21T02:08:36Z	0	0	null	[ "region:us" ]	null	2025-08-21T02:08:21Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Yanina007Caffetti/tutor-cognitivo-emociones-beto	Yanina007Caffetti	2025-08-21T02:06:13Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-21T02:05:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755741930	0xaoyama	2025-08-21T02:06:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:05:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755740275	manusiaperahu2012	2025-08-21T02:05:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:05:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Starsola/llama3.2_3B_news_merged	Starsola	2025-08-21T02:04:18Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T02:04:18Z	--- license: apache-2.0 ---
ccyuan/llama3.2_3B_news_qlora	ccyuan	2025-08-21T02:04:15Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T02:04:15Z	--- license: apache-2.0 ---
wiikoo/ComfyUI-Models-Backup-20250821	wiikoo	2025-08-21T02:03:53Z	0	0	diffusers	[ "diffusers", "onnx", "safetensors", "gguf", "comfyui", "stable-diffusion", "ai-models", "backup", "license:other", "region:us" ]	null	2025-08-20T18:58:57Z	--- license: other tags: - comfyui - stable-diffusion - ai-models - backup --- # ComfyUI 模型备份 - ComfyUI-Models-Backup-20250821 这是ComfyUI的完整模型和自定义节点备份仓库。 ## 📁 目录结构 ``` wiikoo/ComfyUI-Models-Backup-20250821/ ├── models/ # ComfyUI模型文件 │ ├── checkpoints/ # Stable Diffusion检查点 │ ├── loras/ # LoRA模型 │ ├── vae/ # VAE模型 │ ├── controlnet/ # ControlNet模型 │ ├── clip/ # CLIP模型 │ ├── unet/ # UNet模型 │ └── ... # 其他模型类型 └── custom_nodes/ # ComfyUI自定义节点 ├── ComfyUI-Manager/ # 节点管理器 ├── comfyui-easy-use/ # 易用性节点 └── ... # 其他自定义节点 ``` ## 🚀 使用方法 ### 方法1: 完整下载 ```bash git clone https://huggingface.co/wiikoo/ComfyUI-Models-Backup-20250821 ``` ### 方法2: 选择性下载 1. 浏览仓库文件 2. 下载需要的模型或节点 3. 将文件放置到ComfyUI对应目录 ### 方法3: 使用Git LFS ```bash git lfs clone https://huggingface.co/wiikoo/ComfyUI-Models-Backup-20250821 ``` ## 📊 备份信息 - 备份时间: Thu Aug 21 02:03:44 UTC 2025 - 仓库类型: ComfyUI模型备份 - 包含内容: 模型文件 + 自定义节点 ## ⚠️ 注意事项 - 此备份已过滤占位符文件和缓存文件 - 大文件使用Git LFS存储 - 请确保ComfyUI版本兼容性 - 部分模型可能需要特定的许可证 ## 🔧 兼容性 - ComfyUI版本: 最新稳定版 - Python版本: 3.8+ - 系统要求: 支持CUDA的GPU（推荐） ## 📝 更新日志 - 初始备份创建于 2025-08-21
Joecheng/llama3.2_3B_news_merged	Joecheng	2025-08-21T02:03:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T02:03:42Z	--- license: apache-2.0 ---
indoempatnol/blockassist-bc-fishy_wary_swan_1755740190	indoempatnol	2025-08-21T02:03:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:02:59Z	--- 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).
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755741744	0xaoyama	2025-08-21T02:02:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:02:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gregrqewgr/llama3.2_3B_news_merged	gregrqewgr	2025-08-21T02:02:40Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T02:02:40Z	--- license: apache-2.0 ---
g-assismoraes/Qwen3-4B-Base-assin2	g-assismoraes	2025-08-21T02:01:38Z	8	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-4B-Base", "base_model:finetune:Qwen/Qwen3-4B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T18:29:44Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-4B-Base tags: - generated_from_trainer model-index: - name: Qwen3-4B-Base-assin2 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. --> # Qwen3-4B-Base-assin2 This model is a fine-tuned version of [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3019 ## 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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.319 \| 1.0 \| 1625 \| 0.2994 \| \| 0.2082 \| 2.0 \| 3250 \| 0.2527 \| \| 0.1641 \| 3.0 \| 4875 \| 0.2473 \| \| 0.1395 \| 4.0 \| 6500 \| 0.2565 \| \| 0.1264 \| 5.0 \| 8125 \| 0.2711 \| \| 0.1203 \| 6.0 \| 9750 \| 0.2849 \| \| 0.1132 \| 7.0 \| 11375 \| 0.2965 \| \| 0.1109 \| 8.0 \| 13000 \| 0.3019 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
ArtBazh/Glass2Can_policy	ArtBazh	2025-08-21T02:00:20Z	0	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:ArtBazh/Glass2Can", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-21T02:00:00Z	--- datasets: ArtBazh/Glass2Can 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 lerobot-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 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
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755740019	ihsanridzi	2025-08-21T02:00:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T02:00:11Z	--- 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).
saber1209caoke/my_policy	saber1209caoke	2025-08-21T01:59:24Z	0	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:saber1209caoke/record-test0821", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-21T01:58:06Z	--- datasets: saber1209caoke/record-test0821 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - act - 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 lerobot-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 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
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755739876	rvipitkirubbe	2025-08-21T01:57:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mottled foraging ape", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:57:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mottled foraging ape --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
BBoDDoGood/slm-gguf	BBoDDoGood	2025-08-21T01:56:39Z	45	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-19T07:09:02Z	# Korean Security Chatbot GGUF Model 이 저장소는 한국어 보안 챗봇을 위한 GGUF 모델을 포함합니다. ## 모델 정보 - 모델 타입: Qwen2.5-1.5B 기반 한국어 보안 챗봇 - 파일 형식: GGUF (F16) - 용도: 보안 상황 인식 및 대응 안내 ## 사용 방법 ### llama.cpp 사용 ```bash ./main -m slm_model.gguf -p "input: [보안 상황] 장소: 사무실, 위험도: 높음" -n 100 ``` ### Python에서 사용 ```python from llama_cpp import Llama llm = Llama(model_path="slm_model.gguf") response = llm("input: [보안 상황]", max_tokens=100) ``` ## 모델 특징 - 한국어 보안 상황 인식 - 실시간 대응 안내 - 다양한 보안 시나리오 지원 ## 라이선스 이 모델은 교육 및 연구 목적으로만 사용되어야 합니다.
DanielJustin/llama3.2_3B_news_qlora	DanielJustin	2025-08-21T01:53:51Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T01:53:51Z	--- license: apache-2.0 ---
Joecheng/llama3.2_3B_news_qlora4	Joecheng	2025-08-21T01:52:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T01:52:42Z	--- license: apache-2.0 ---
roeker/blockassist-bc-quick_wiry_owl_1755741070	roeker	2025-08-21T01:52:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:51:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
SFWolf/llama3.2_3B_news_merged	SFWolf	2025-08-21T01:51:53Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T01:51:53Z	--- license: apache-2.0 ---
blackofdeath/llama3.2_3B_news_merged	blackofdeath	2025-08-21T01:50:46Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T01:50:46Z	--- license: apache-2.0 ---
PGFROG/llama3.2_3B_news_merged	PGFROG	2025-08-21T01:49:11Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T01:49:11Z	--- license: apache-2.0 ---
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755739370	helmutsukocok	2025-08-21T01:49:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:49:03Z	--- 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).
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755739345	lisaozill03	2025-08-21T01:49:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:48:59Z	--- 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).
New-tutorial-Jobz-Hunting-Hd-Viral-Videos/FULL.VIDEO.Jobz.Hunting.Sajal.Malik.Viral.Video.Tutorial.Official.link	New-tutorial-Jobz-Hunting-Hd-Viral-Videos	2025-08-21T01:48:20Z	0	0	null	[ "region:us" ]	null	2025-08-21T01:48:09Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755740789	IvanJAjebu	2025-08-21T01:47:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:47:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # 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_1755739135	vwzyrraz7l	2025-08-21T01:47:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:47:28Z	--- 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).
roeker/blockassist-bc-quick_wiry_owl_1755740776	roeker	2025-08-21T01:47:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:46:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Donks520/Ya	Donks520	2025-08-21T01:43:07Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-21T01:41:35Z	--- license: apache-2.0 ---
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755740395	IvanJAjebu	2025-08-21T01:41:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:41:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
minium/distance-aware-mobile-vla	minium	2025-08-21T01:40:59Z	0	0	null	[ "region:us" ]	null	2025-08-21T01:29:12Z	# Distance-Aware Mobile VLA Model ## Overview This is a distance-aware Vision-Language-Action (VLA) model for mobile robot navigation, built on top of Kosmos2 vision backbone. ## Model Architecture - Backbone: Kosmos2 Vision Model (microsoft/kosmos-2-patch14-224) - Action Head: LSTM + MLP - Distance Awareness: Distance embedding and fusion layers - Input: 8-frame image sequence - Output: 2-frame action prediction [linear_x, linear_y, angular_z] ## Performance - Overall MAE: 0.2602 - Success Rate: 88.7% - Distance-wise Performance: - Close: MAE 0.2617 (76.6% success) - Medium: MAE 0.2017 (81.9% success) ⭐ Best - Far: MAE 0.3373 (69.8% success) ## Usage ```python from transformers import AutoProcessor, AutoModel import torch # Load model processor = AutoProcessor.from_pretrained("your-username/distance-aware-mobile-vla") model = AutoModel.from_pretrained("your-username/distance-aware-mobile-vla") # Prepare input images = torch.randn(1, 8, 3, 224, 224) # 8-frame sequence distance_labels = torch.tensor([1]) # 0: close, 1: medium, 2: far # Predict actions with torch.no_grad(): predicted_actions = model(images, distance_labels) ``` ## Training Details - Dataset: 480 episodes (160 per distance) - Augmentation: Distance-aware specialized augmentation - Distance Factors: Close 8x, Medium 5x, Far 8x - Training Epochs: 15 ## Key Features - ✅ Distance-aware training and inference - ✅ Kosmos2 vision backbone - ✅ Temporal modeling with LSTM - ✅ Specialized data augmentation - ✅ Balanced performance across distances ## Limitations - Currently predicts 2 frames from 8 input frames - SPACE (stop) action accuracy needs improvement - Far distance performance can be enhanced ## Citation If you use this model, please cite: ``` @misc{distance_aware_mobile_vla_2024, title={Distance-Aware Mobile VLA Model}, author={Your Name}, year={2024} } ```
John6666/mess-illustrious-anime-mix-v10-sdxl	John6666	2025-08-21T01:39:29Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "flat anime", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-08-21T01:31:57Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - flat anime - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1884871/mess-illustrious-anime-mix?modelVersionId=2133466). This model created by [Mess1](https://civitai.com/user/Mess1).
LucasFMartins/gemma-3-1b-it-fine-tuned-demo-5	LucasFMartins	2025-08-21T01:39:00Z	0	0	null	[ "safetensors", "fine-tuned", "gemma", "lora", "gemma-garage", "text-generation", "conversational", "en", "base_model:google/gemma-3-1b-it", "base_model:adapter:google/gemma-3-1b-it", "license:apache-2.0", "region:us" ]	text-generation	2025-08-21T01:38:55Z	--- language: en license: apache-2.0 tags: - fine-tuned - gemma - lora - gemma-garage base_model: google/gemma-3-1b-it pipeline_tag: text-generation --- # gemma-3-1b-it-fine-tuned-demo-5 Fine-tuned google/gemma-3-1b-it model from Gemma Garage This model was fine-tuned using [Gemma Garage](https://github.com/your-repo/gemma-garage), a platform for fine-tuning Gemma models with LoRA. ## Model Details - Base Model: google/gemma-3-1b-it - Fine-tuning Method: LoRA (Low-Rank Adaptation) - Training Platform: Gemma Garage - Fine-tuned on: 2025-08-21 ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LucasFMartins/gemma-3-1b-it-fine-tuned-demo-5") model = AutoModelForCausalLM.from_pretrained("LucasFMartins/gemma-3-1b-it-fine-tuned-demo-5") # Generate text inputs = tokenizer("Your prompt here", return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=100) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## Training Details This model was fine-tuned using the Gemma Garage platform with the following configuration: - Request ID: 7c24eaa3-6289-41e4-b09a-c7e963eb5ed2 - Training completed on: 2025-08-21 01:38:57 UTC For more information about Gemma Garage, visit [our GitHub repository](https://github.com/your-repo/gemma-garage).
lautan/blockassist-bc-gentle_patterned_goat_1755738642	lautan	2025-08-21T01:37:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:36:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
haihp02/256459dc-f005-4fc1-8241-b653e32be26f	haihp02	2025-08-21T01:36:24Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-21T01:36:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755740076	IvanJAjebu	2025-08-21T01:35:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:35:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755738254	coelacanthxyz	2025-08-21T01:32:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:32:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755739852	roeker	2025-08-21T01:32:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:31:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755739783	IvanJAjebu	2025-08-21T01:30:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:30:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # 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_1755738274	indoempatnol	2025-08-21T01:30:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:30:50Z	--- 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).
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755738236	manusiaperahu2012	2025-08-21T01:30:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:30:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hobson123/blockassist-bc-mammalian_dense_gibbon_1755739373	hobson123	2025-08-21T01:28:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mammalian dense gibbon", "arxiv:2504.07091", "region:us" ]	null	2025-08-21T01:28:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mammalian dense gibbon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
John6666/bismuth-illustrious-mix-v40-sdxl	John6666	2025-08-21T01:27:03Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "details", "flexibility", "contrast", "vivid colors", "lighting", "facial structure and detail", "composition", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-08-21T01:22:06Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - details - flexibility - contrast - vivid colors - details - lighting - facial structure and detail - composition - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1359028/bismuth-illustrious-mix?modelVersionId=2131283). This model created by [Axelros](https://civitai.com/user/Axelros).
Mostefa-Terbeche/diabetic-retinopathy-aptos-efficientnet_b3-original-20250720-012032	Mostefa-Terbeche	2025-08-21T01:24:39Z	0	0	null	[ "diabetic-retinopathy", "medical-imaging", "pytorch", "computer-vision", "retinal-imaging", "dataset:aptos", "license:apache-2.0", "model-index", "region:us" ]	null	2025-08-21T00:58:37Z	--- license: apache-2.0 tags: - diabetic-retinopathy - medical-imaging - pytorch - computer-vision - retinal-imaging datasets: - aptos metrics: - accuracy - quadratic-kappa - auc model-index: - name: aptos_efficientnet_b3_original results: - task: type: image-classification name: Diabetic Retinopathy Classification dataset: type: aptos name: APTOS metrics: - type: accuracy value: 0.7704918032786885 - type: quadratic-kappa value: 0.8974660347551343 --- # Diabetic Retinopathy Classification Model ## Model Description This model is trained for diabetic retinopathy classification using the efficientnet_b3 architecture on the aptos dataset with original preprocessing. ## Model Details - Architecture: efficientnet_b3 - Dataset: aptos - Preprocessing: original - Training Date: 20250720-012032 - Task: 5-class diabetic retinopathy grading (0-4) - Directory: aptos_efficientnet_b3_20250720-012032_new ## Performance - Test Accuracy: 0.7704918032786885 - Test Quadratic Kappa: 0.8974660347551343 - Validation Kappa: 0.8974660347551343 ## Usage ```python import torch from huggingface_hub import hf_hub_download # Download model model_path = hf_hub_download( repo_id="your-username/diabetic-retinopathy-aptos-efficientnet_b3-original", filename="model_best.pt" ) # Load model model = torch.load(model_path, map_location='cpu') ``` ## Classes - 0: No DR (No diabetic retinopathy) - 1: Mild DR (Mild non-proliferative diabetic retinopathy) - 2: Moderate DR (Moderate non-proliferative diabetic retinopathy) - 3: Severe DR (Severe non-proliferative diabetic retinopathy) - 4: Proliferative DR (Proliferative diabetic retinopathy) ## Citation If you use this model, please cite your research paper/thesis.

AnerYubo/blockassist-bc-reptilian_bellowing_cockroach_1755745453

AnerYubo

2025-08-21T03:04:16Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reptilian bellowing cockroach", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T03:04:14Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reptilian bellowing cockroach --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

yk0/forge-e48

yk0

2025-08-21T03:03:46Z

0

null

[ "pytorch", "region:us" ]

null

2025-08-21T03:02:33Z

# forge-v1 Model Private testing version.

liukevin666/blockassist-bc-yawning_striped_cassowary_1755745023

liukevin666

2025-08-21T03:02:05Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:58:07Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755743784

rvipitkirubbe

2025-08-21T03:01:46Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mottled foraging ape", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T03:01:42Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mottled foraging ape --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755745209

IvanJAjebu

2025-08-21T03:01:28Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T03:01:19Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

thalhathai/distilbert-base-uncased-finetuned-emotion

thalhathai

2025-08-21T03:00:41Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-21T02:32:06Z

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

hakimjustbao/blockassist-bc-raging_subtle_wasp_1755743635

hakimjustbao

2025-08-21T03:00:14Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T03:00:10Z

--- 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).

g-assismoraes/Qwen3-4B-Base-faquad

g-assismoraes

2025-08-21T03:00:04Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-4B-Base", "base_model:finetune:Qwen/Qwen3-4B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-21T02:02:09Z

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-4B-Base tags: - generated_from_trainer model-index: - name: Qwen3-4B-Base-faquad results: [] ---  # Qwen3-4B-Base-faquad This model is a fine-tuned version of [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9271 ## 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2827 | 1.0 | 782 | 1.3027 | | 0.142 | 2.0 | 1564 | 1.5038 | | 0.0994 | 3.0 | 2346 | 1.6675 | | 0.087 | 4.0 | 3128 | 1.7409 | | 0.0803 | 5.0 | 3910 | 1.8365 | | 0.0807 | 6.0 | 4692 | 1.8826 | | 0.0763 | 7.0 | 5474 | 1.9170 | | 0.0764 | 8.0 | 6256 | 1.9271 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0

roeker/blockassist-bc-quick_wiry_owl_1755745059

roeker

2025-08-21T02:58:24Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:58:17Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

rourkerhotmail1/blockassist-bc-stalking_scruffy_walrus_1755742822

rourkerhotmail1

2025-08-21T02:54:19Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stalking scruffy walrus", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:54:16Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stalking scruffy walrus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

germanlunichh/blockassist-bc-mute_shaggy_alligator_1755742698

germanlunichh

2025-08-21T02:51:01Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute shaggy alligator", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:50:58Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute shaggy alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755743073

lisaozill03

2025-08-21T02:49:53Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:49:49Z

--- 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).

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755742849

kojeklollipop

2025-08-21T02:48:34Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:48:30Z

--- 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).

jakehsv/blockassist-bc-flexible_waddling_peacock_1755742765

jakehsv

2025-08-21T02:48:14Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flexible waddling peacock", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:48:04Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flexible waddling peacock --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

liukevin666/blockassist-bc-yawning_striped_cassowary_1755744350

liukevin666

2025-08-21T02:47:15Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:46:44Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # 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_1755742548

chainway9

2025-08-21T02:43:13Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:43:07Z

--- 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).

lqpl/blockassist-bc-hairy_insectivorous_antelope_1755744113

lqpl

2025-08-21T02:43:12Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:42:50Z

--- 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).

coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755742440

coelacanthxyz

2025-08-21T02:42:26Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:42:21Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

kevinshin/hunyuan-1.8b-it-dpo-lr-1e-6-batch-16-epoch-1-wildchat-cw-3k

kevinshin

2025-08-21T02:40:52Z

0

transformers

[ "transformers", "safetensors", "hunyuan_v1_dense", "text-generation", "generated_from_trainer", "dpo", "trl", "conversational", "dataset:kevinshin/wildchat-creative-writing-3k-pref", "arxiv:2305.18290", "base_model:tencent/Hunyuan-1.8B-Instruct", "base_model:finetune:tencent/Hunyuan-1.8B-Instruct", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-20T22:30:37Z

--- base_model: tencent/Hunyuan-1.8B-Instruct datasets: kevinshin/wildchat-creative-writing-3k-pref library_name: transformers model_name: hunyuan-1.8b-it-dpo-lr-1e-6-batch-16-epoch-1-wildchat-cw-3k tags: - generated_from_trainer - dpo - trl licence: license --- # Model Card for hunyuan-1.8b-it-dpo-lr-1e-6-batch-16-epoch-1-wildchat-cw-3k This model is a fine-tuned version of [tencent/Hunyuan-1.8B-Instruct](https://huggingface.co/tencent/Hunyuan-1.8B-Instruct) on the [kevinshin/wildchat-creative-writing-3k-pref](https://huggingface.co/datasets/kevinshin/wildchat-creative-writing-3k-pref) 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="kevinshin/hunyuan-1.8b-it-dpo-lr-1e-6-batch-16-epoch-1-wildchat-cw-3k", 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/myungjune-sogang-university/general_remo_train/runs/qpgof8ie) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.19.1 - Transformers: 4.55.0.dev0 - Pytorch: 2.6.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.2 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

calegpedia/blockassist-bc-stealthy_slimy_rooster_1755742244

calegpedia

2025-08-21T02:39:02Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:38:56Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

lqpl/blockassist-bc-hairy_insectivorous_antelope_1755743751

lqpl

2025-08-21T02:39:01Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:36:47Z

--- 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).

JihadSaoudQMUL/deberta-bias-detection

JihadSaoudQMUL

2025-08-21T02:38:34Z

0

peft

[ "peft", "safetensors", "base_model:adapter:microsoft/deberta-base", "lora", "transformers", "arxiv:1910.09700", "base_model:microsoft/deberta-base", "region:us" ]

null

2025-08-21T02:35:35Z

--- base_model: microsoft/deberta-base library_name: peft tags: - base_model:adapter:microsoft/deberta-base - lora - transformers --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0

roeker/blockassist-bc-quick_wiry_owl_1755743831

roeker

2025-08-21T02:37:55Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:37:49Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755743632

0xaoyama

2025-08-21T02:34:27Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:34:14Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

pkshatech/GLuCoSE-base-ja

pkshatech

2025-08-21T02:34:17Z

50,548

32

sentence-transformers

[ "sentence-transformers", "pytorch", "luke", "feature-extraction", "transformers", "sentence-similarity", "ja", "dataset:mc4", "dataset:clips/mqa", "dataset:shunk031/JGLUE", "dataset:paws-x", "dataset:MoritzLaurer/multilingual-NLI-26lang-2mil7", "dataset:castorini/mr-tydi", "dataset:hpprc/jsick", "arxiv:2104.07179", "arxiv:2004.04906", "base_model:studio-ousia/luke-japanese-base-lite", "base_model:finetune:studio-ousia/luke-japanese-base-lite", "license:apache-2.0", "autotrain_compatible", "region:us" ]

sentence-similarity

2023-07-16T07:28:46Z

--- pipeline_tag: sentence-similarity language: ja license: apache-2.0 tags: - transformers - sentence-similarity - feature-extraction - sentence-transformers inference: false datasets: - mc4 - clips/mqa - shunk031/JGLUE - paws-x - MoritzLaurer/multilingual-NLI-26lang-2mil7 - castorini/mr-tydi - hpprc/jsick base_model: - studio-ousia/luke-japanese-base-lite --- # GLuCoSE (General Luke-based Contrastive Sentence Embedding)-base-Japanese [日本語のREADME/Japanese README](https://huggingface.co/pkshatech/GLuCoSE-base-ja/blob/main/README_JA.md) GLuCoSE (General LUke-based COntrastive Sentence Embedding, "glucose") is a Japanese text embedding model based on [LUKE](https://github.com/studio-ousia/luke). In order to create a general-purpose, user-friendly Japanese text embedding model, GLuCoSE has been trained on a mix of web data and various datasets associated with natural language inference and search. This model is not only suitable for sentence vector similarity tasks but also for semantic search tasks. - Maximum token count: 512 - Output dimension: 768 - Pooling: mean pooling - Supported language: Japanese ## Usage You can use this model easily with [sentence-transformers](https://www.SBERT.net). First, install sentence-transformers with pip as follows: ``` pip install -U sentence-transformers ``` You can load the model and convert sentences into dense vectors as shown below: ```python from sentence_transformers import SentenceTransformer sentences = [ "PKSHA Technologyは機械学習/深層学習技術に関わるアルゴリズムソリューションを展開している。", "この深層学習モデルはPKSHA Technologyによって学習され、公開された。", "広目天は、仏教における四天王の一尊であり、サンスクリット語の「種々の眼をした者」を名前の由来とする。", ] model = SentenceTransformer('pkshatech/GLuCoSE-base-ja') embeddings = model.encode(sentences) print(embeddings) ``` Since the loss function used during training is cosine similarity, we recommend using cosine similarity for downstream tasks. This text embedding model can also be used in LangChain. Please refer to [this page](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers) for more information. ## Resources Used The following resources were used to train this model. ### Pre-trained model - [studio-ousia/luke-japanese-base-lite](https://huggingface.co/studio-ousia/luke-japanese-base-lite) ### Datasets - [mC4](https://huggingface.co/datasets/mc4) - [MQA](https://huggingface.co/datasets/clips/mqa) - [JNLI](https://github.com/yahoojapan/JGLUE) - [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [JSeM](https://github.com/DaisukeBekki/JSeM) - [MoritzLaurer/multilingual-NLI-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) - [MultiNLI](https://huggingface.co/datasets/multi_nli) - [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) - [FeverNLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md) - [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) - [JSICK](https://github.com/verypluming/JSICK) - [Mr.Tidy](https://huggingface.co/datasets/castorini/mr-tydi) - [JSTS](https://github.com/yahoojapan/JGLUE) (used for validation) [^1] ## Benchmarks ### Semantic Similarity Calculation ([JSTS](https://github.com/yahoojapan/JGLUE) dev set) Evaluation by Spearman's correlation coefficient and Pearson's correlation coefficient. | Model | Spearman | Pearson | | --- | --- | --- | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) |0.837[^2] | 0.790[^2] | | [pkshatech/simcse-ja-bert-base-clcmlp](https://huggingface.co/pkshatech/simcse-ja-bert-base-clcmlp)[^3] | 0.850 | 0.801 | | pkshatech/GLuCoSE-base-ja | **0.864** | **0.818** | ### Zero-shot Search ([AIO3](https://sites.google.com/view/project-aio/competition3?authuser=0) dev set) Evaluation by top-k retrieval accuracy[^4] (the fraction of questions that have a correct answer in the top-k retrieved documents at least once.) | Model | Top-1 | Top-5 | Top-10 | Top-50 | | --- | --- | --- | --- | --- | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 33.50 | 57.80 | 65.10 | 76.60 | | [pkshatech/simcse-ja-bert-base-clcmlp](https://huggingface.co/pkshatech/simcse-ja-bert-base-clcmlp)[^3] | 30.60 | 54.50 | 62.50 | 76.70 | | pkshatech/GLuCoSE-base-ja | **36.10** | **59.40** | **66.40** | **78.30** | # Authors [Akihiko Fukuchi](https://huggingface.co/akiFQC), [Yuichiro Hoshino](https://huggingface.co/Yuichiroh), [Yotarow Watanabe](https://huggingface.co/yotarow) ## Citation ``` @misc{pkshatech-GLuCoSE-base-ja, title={pkshatech/GLuCoSE-base-ja}, url={https://huggingface.co/pkshatech/GLuCoSE-base-ja}, author={Akihiko Fukuchi, Yuichiro Hoshino, Yotarow Watanabe}, year={2023}, } ``` ## License This model is published under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). [^1]: When we trained this model, the test data of JGLUE was not released, so we used the dev set of JGLUE as a private evaluation data. Therefore, we selected the checkpoint on the train set of JGLUE insted of its dev set. [^2]: https://qiita.com/akeyhero/items/ce371bfed64399027c23 [^3]: This is the model we have released before. [^4]: For more details, please refer to https://arxiv.org/pdf/2004.04906.pdf.

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755743552

IvanJAjebu

2025-08-21T02:33:39Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:33:30Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

ihsanridzi/blockassist-bc-wiry_flexible_owl_1755741980

ihsanridzi

2025-08-21T02:31:33Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:31:28Z

--- 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).

wrl2003/test-ltf

wrl2003

2025-08-21T02:29:12Z

0

null

[ "region:us" ]

null

2025-08-21T01:48:53Z

--- title: Test Hugsim Web Server emoji: 📈 colorFrom: purple colorTo: yellow sdk: docker pinned: false ---

roeker/blockassist-bc-quick_wiry_owl_1755743226

roeker

2025-08-21T02:28:27Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:27:51Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # 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_1755741725

hakimjustbao

2025-08-21T02:27:55Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:27:51Z

--- 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).

Team-ACE/ToolACE-2.5-Llama-3.1-8B

Team-ACE

2025-08-21T02:27:46Z

1

0

null

[ "safetensors", "llama", "code", "en", "dataset:Team-ACE/ToolACE", "arxiv:2409.00920", "arxiv:2504.01400", "arxiv:2505.07512", "arxiv:2508.12685", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:apache-2.0", "region:us" ]

null

2025-08-20T13:36:06Z

--- license: apache-2.0 datasets: - Team-ACE/ToolACE language: - en metrics: - accuracy base_model: meta-llama/Meta-Llama-3.1-8B-Instruct tags: - code --- # ToolACE-2.5-Llama-3.1-8B ToolACE-2.5-Llama-3.1-8B is a fine-tuned model of LLaMA-3.1-8B-Instruct with our dataset [ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE) tailored for tool usage. Compared with [ToolACE-2](https://huggingface.co/Team-ACE/ToolACE-2-Llama-3.1-8B), ToolACE-2.5-8B enhances the multi-turn tool-usage ability. ToolACE is an automatic agentic pipeline designed to generate **A**ccurate, **C**omplex, and div**E**rse tool-learning data. ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs. Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process. To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks. More details can be found in our paper: [*ToolACE: Winning the Points of LLM Function Calling*](https://arxiv.org/abs/2409.00920) ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/66bf01f45bdd611f9a602087/WmyWOYtg_dbTgwQmvlqcz.jpeg) More techniques are applied to further improve tool-usage ability, including: - [*ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning*](https://arxiv.org/abs/2504.01400) - [*ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution*](https://arxiv.org/abs/2505.07512) - [*ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction*](https://arxiv.org/abs/2508.12685) ### Usage Here we provide a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate function calling with given functions. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Team-ACE/ToolACE-2.5-Llama-3.1-8B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype='auto', device_map='auto' ) # You can modify the prompt for your task system_prompt = """You are an expert in composing functions. You are given a question and a set of possible functions. Based on the question, you will need to make one or more function/tool calls to achieve the purpose. If none of the function can be used, point it out. If the given question lacks the parameters required by the function, also point it out. You should only return the function call in tools call sections. If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)] You SHOULD NOT include any other text in the response. Here is a list of functions in JSON format that you can invoke.\n{functions}\n """ # User query query = "Find me the sales growth rate for company XYZ for the last 3 years and also the interest coverage ratio for the same duration." # Availabel tools in JSON format (OpenAI-format) tools = [ { "name": "financial_ratios.interest_coverage", "description": "Calculate a company's interest coverage ratio given the company name and duration", "arguments": { "type": "dict", "properties": { "company_name": { "type": "string", "description": "The name of the company." }, "years": { "type": "integer", "description": "Number of past years to calculate the ratio." } }, "required": ["company_name", "years"] } }, { "name": "sales_growth.calculate", "description": "Calculate a company's sales growth rate given the company name and duration", "arguments": { "type": "dict", "properties": { "company": { "type": "string", "description": "The company that you want to get the sales growth rate for." }, "years": { "type": "integer", "description": "Number of past years for which to calculate the sales growth rate." } }, "required": ["company", "years"] } }, { "name": "weather_forecast", "description": "Retrieve a weather forecast for a specific location and time frame.", "arguments": { "type": "dict", "properties": { "location": { "type": "string", "description": "The city that you want to get the weather for." }, "days": { "type": "integer", "description": "Number of days for the forecast." } }, "required": ["location", "days"] } } ] messages = [ {'role': 'system', 'content': system_prompt.format(functions=tools)}, {'role': 'user', 'content': query} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` Then you should be able to see the following output functional calls: ``` [sales_growth.calculate(company="XYZ", years=3), financial_ratios.interest_coverage(company_name="XYZ", years=3)] ``` ### Citation If you think ToolACE is useful in your work, please cite our paper: ``` @inproceedings{ liu2025toolace, title={Tool{ACE}: Winning the Points of {LLM} Function Calling}, author={Weiwen Liu and Xu Huang and Xingshan Zeng and xinlong hao and Shuai Yu and Dexun Li and Shuai Wang and Weinan Gan and Zhengying Liu and Yuanqing Yu and Zezhong WANG and Yuxian Wang and Wu Ning and Yutai Hou and Bin Wang and Chuhan Wu and Wang Xinzhi and Yong Liu and Yasheng Wang and Duyu Tang and Dandan Tu and Lifeng Shang and Xin Jiang and Ruiming Tang and Defu Lian and Qun Liu and Enhong Chen}, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, url={https://openreview.net/forum?id=8EB8k6DdCU} } ``` Additionally, please check our other related works whose techniques are applied in ToolACE-2.5-8B: ``` @article{zeng2025toolacer, title={ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning}, author={Zeng, Xingshan and Liu, Weiwen and Huang, Xu and Wang, Zezhong and Wang, Lingzhi and Li, Liangyou and Wang, Yasheng and Shang, Lifeng and Jiang, Xin and Tang, Ruiming and Liu, Qun}, journal={arXiv preprint arXiv:2504.01400}, year={2025} } ``` ``` @article{huang2025toolace, title={ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution}, author={Huang, Xu and Liu, Weiwen and Zeng, Xingshan and Huang, Yuefeng and Hao, Xinlong and Wang, Yuxian and Zeng, Yirong and Wu, Chuhan and Wang, Yasheng and Tang, Ruiming and Lian, Defu}, journal={arXiv preprint arXiv:2505.07512}, year={2025} } ``` ``` @article{zeng2025toolacemt, title={ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction}, author={Zeng, Xingshan and Liu, Weiwen and Wang, Lingzhi and Li, Liangyou and Mi, Fei and Wang, Yasheng and Shang, Lifeng and Jiang, Xin and Liu, Qun}, journal={arXiv preprint arXiv:2508.12685}, year={2025} } ```

lightningpal/epi-derm2

lightningpal

2025-08-21T02:24:47Z

0

transformers

[ "transformers", "safetensors", "resnet", "image-classification", "vision", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2025-08-21T02:21:08Z

--- pipeline_tag: image-classification library_name: transformers tags: - image-classification - vision --- # Model Card for Model ID  ## Model Details ### Model Description  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:** [Fernando Hidalgo Lecaros] - **Model type:** [ImageClassification] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model:** [ResNet50] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755742982

IvanJAjebu

2025-08-21T02:24:17Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:24:08Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

yidingp/qwen2_coder_7b_apps_finetuned

yidingp

2025-08-21T02:23:56Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:Qwen/Qwen2.5-Coder-7B", "base_model:finetune:Qwen/Qwen2.5-Coder-7B", "endpoints_compatible", "region:us" ]

null

2025-08-21T02:23:50Z

--- base_model: Qwen/Qwen2.5-Coder-7B library_name: transformers model_name: qwen2_coder_7b_apps_finetuned tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for qwen2_coder_7b_apps_finetuned This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B). 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="yidingp/qwen2_coder_7b_apps_finetuned", 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+cu126 - 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}} } ```

roeker/blockassist-bc-quick_wiry_owl_1755742912

roeker

2025-08-21T02:22:38Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:22:31Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # 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_1755741387

helmutsukocok

2025-08-21T02:22:25Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:22:22Z

--- 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).

luiz826/MichaelScottGenFinal

luiz826

2025-08-21T02:22:17Z

26

0

transformers

[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-12-13T04:22:34Z

# This is a Michael Scott Generator ![Michael Scott](https://media.tenor.com/BFLvU0UB74AAAAAM/office-sentence.gif) We made this project for the NLP course on Federal University of Paraíba. Contact me 👋: https://luiz826.github.io/

afung/pika-pick-and-place-ee_absolute-fisheye

afung

2025-08-21T02:20:57Z

0

lerobot

[ "lerobot", "safetensors", "diffusion", "robotics", "dataset:afung/pika-pick-and-place-ee_delta_gripper-fisheye", "arxiv:2303.04137", "license:apache-2.0", "region:us" ]

robotics

2025-08-21T02:17:28Z

--- datasets: afung/pika-pick-and-place-ee_delta_gripper-fisheye library_name: lerobot license: apache-2.0 model_name: diffusion pipeline_tag: robotics tags: - diffusion - robotics - lerobot --- # Model Card for diffusion  [Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. 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

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755741319

lisaozill03

2025-08-21T02:19:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:19:40Z

--- 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).

leo12757/llama3.2_3B_news_merged

leo12757

2025-08-21T02:18:53Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T02:18:53Z

--- license: apache-2.0 ---

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755742626

IvanJAjebu

2025-08-21T02:18:22Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:18:13Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

roeker/blockassist-bc-quick_wiry_owl_1755742603

roeker

2025-08-21T02:18:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:17:23Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

manfred2015/UUU-Finetune-GPt2

manfred2015

2025-08-21T02:17:44Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T02:17:43Z

--- license: apache-2.0 ---

katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755740928

katanyasekolah

2025-08-21T02:17:31Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:17:27Z

--- 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).

AlphaMine00/blockassist-bc-flapping_beaked_zebra_1755742537

AlphaMine00

2025-08-21T02:17:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping beaked zebra", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:16:59Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping beaked zebra --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

koloni/blockassist-bc-deadly_graceful_stingray_1755741080

koloni

2025-08-21T02:17:01Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:16:58Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

andy013567/gemma-3-1b-it-classifier-finetune

andy013567

2025-08-21T02:15:43Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "unsloth", "trl", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "endpoints_compatible", "region:us" ]

null

2025-08-20T00:42:53Z

--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit library_name: transformers model_name: gemma-3-1b-it-classifier-finetune tags: - generated_from_trainer - sft - unsloth - trl licence: license --- # Model Card for gemma-3-1b-it-classifier-finetune This model is a fine-tuned version of [unsloth/gemma-3-1b-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3-1b-it-unsloth-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="andy013567/gemma-3-1b-it-classifier-finetune", 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/anhbui5302/huggingface/runs/rd8egtwy) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0+cu126 - Datasets: 3.6.0 - 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}} } ```

iBush/llama3.2_3B_news_qlora

iBush

2025-08-21T02:15:20Z

0

peft

[ "peft", "safetensors", "base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "region:us" ]

text-generation

2025-08-21T01:49:31Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0

Fattyfish/llama3.2_3B_news_qlora

Fattyfish

2025-08-21T02:14:36Z

0

peft

[ "peft", "safetensors", "base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "region:us" ]

text-generation

2025-08-21T02:11:29Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0

linlinlin000/Llama3.2_3B_news_qlora

linlinlin000

2025-08-21T02:13:17Z

0

peft

[ "peft", "safetensors", "base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "region:us" ]

text-generation

2025-08-21T01:52:18Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0

rriicckk/llama3.2_3B_news_qlora

rriicckk

2025-08-21T02:12:25Z

0

peft

[ "peft", "safetensors", "base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "region:us" ]

text-generation

2025-08-21T01:52:22Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0

roeker/blockassist-bc-quick_wiry_owl_1755742299

roeker

2025-08-21T02:12:23Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:12:17Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

chen95427/llama3.2_3B_news_qlora

chen95427

2025-08-21T02:11:34Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T02:11:34Z

--- license: apache-2.0 ---

junyi080914/llama3.2_3B_news_merged

junyi080914

2025-08-21T02:11:10Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T02:11:10Z

--- license: apache-2.0 ---

lautan/blockassist-bc-gentle_patterned_goat_1755740594

lautan

2025-08-21T02:09:37Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:09:34Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

18-Clips-Sophie-Rain-Viral-video-original/New.full.videos.Sophie.Rain.Spiderman.Viral.Video.Official.Tutorial

18-Clips-Sophie-Rain-Viral-video-original

2025-08-21T02:08:36Z

0

null

[ "region:us" ]

null

2025-08-21T02:08:21Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

Yanina007Caffetti/tutor-cognitivo-emociones-beto

Yanina007Caffetti

2025-08-21T02:06:13Z

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-21T02:05:46Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755741930

0xaoyama

2025-08-21T02:06:04Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:05:52Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755740275

manusiaperahu2012

2025-08-21T02:05:08Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:05:05Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Starsola/llama3.2_3B_news_merged

Starsola

2025-08-21T02:04:18Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T02:04:18Z

--- license: apache-2.0 ---

ccyuan/llama3.2_3B_news_qlora

ccyuan

2025-08-21T02:04:15Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T02:04:15Z

--- license: apache-2.0 ---

wiikoo/ComfyUI-Models-Backup-20250821

wiikoo

2025-08-21T02:03:53Z

0

diffusers

[ "diffusers", "onnx", "safetensors", "gguf", "comfyui", "stable-diffusion", "ai-models", "backup", "license:other", "region:us" ]

null

2025-08-20T18:58:57Z

--- license: other tags: - comfyui - stable-diffusion - ai-models - backup --- # ComfyUI 模型备份 - ComfyUI-Models-Backup-20250821 这是ComfyUI的完整模型和自定义节点备份仓库。 ## 📁 目录结构 ``` wiikoo/ComfyUI-Models-Backup-20250821/ ├── models/ # ComfyUI模型文件 │ ├── checkpoints/ # Stable Diffusion检查点 │ ├── loras/ # LoRA模型 │ ├── vae/ # VAE模型 │ ├── controlnet/ # ControlNet模型 │ ├── clip/ # CLIP模型 │ ├── unet/ # UNet模型 │ └── ... # 其他模型类型 └── custom_nodes/ # ComfyUI自定义节点 ├── ComfyUI-Manager/ # 节点管理器 ├── comfyui-easy-use/ # 易用性节点 └── ... # 其他自定义节点 ``` ## 🚀 使用方法 ### 方法1: 完整下载 ```bash git clone https://huggingface.co/wiikoo/ComfyUI-Models-Backup-20250821 ``` ### 方法2: 选择性下载 1. 浏览仓库文件 2. 下载需要的模型或节点 3. 将文件放置到ComfyUI对应目录 ### 方法3: 使用Git LFS ```bash git lfs clone https://huggingface.co/wiikoo/ComfyUI-Models-Backup-20250821 ``` ## 📊 备份信息 - **备份时间**: Thu Aug 21 02:03:44 UTC 2025 - **仓库类型**: ComfyUI模型备份 - **包含内容**: 模型文件 + 自定义节点 ## ⚠️ 注意事项 - 此备份已过滤占位符文件和缓存文件 - 大文件使用Git LFS存储 - 请确保ComfyUI版本兼容性 - 部分模型可能需要特定的许可证 ## 🔧 兼容性 - **ComfyUI版本**: 最新稳定版 - **Python版本**: 3.8+ - **系统要求**: 支持CUDA的GPU（推荐） ## 📝 更新日志 - 初始备份创建于 2025-08-21

Joecheng/llama3.2_3B_news_merged

Joecheng

2025-08-21T02:03:42Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T02:03:42Z

--- license: apache-2.0 ---

indoempatnol/blockassist-bc-fishy_wary_swan_1755740190

indoempatnol

2025-08-21T02:03:02Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:02:59Z

--- 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).

0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755741744

0xaoyama

2025-08-21T02:02:58Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:02:46Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

gregrqewgr/llama3.2_3B_news_merged

gregrqewgr

2025-08-21T02:02:40Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T02:02:40Z

--- license: apache-2.0 ---

g-assismoraes/Qwen3-4B-Base-assin2

g-assismoraes

2025-08-21T02:01:38Z

8

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-4B-Base", "base_model:finetune:Qwen/Qwen3-4B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-12T18:29:44Z

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-4B-Base tags: - generated_from_trainer model-index: - name: Qwen3-4B-Base-assin2 results: [] ---  # Qwen3-4B-Base-assin2 This model is a fine-tuned version of [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3019 ## 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.319 | 1.0 | 1625 | 0.2994 | | 0.2082 | 2.0 | 3250 | 0.2527 | | 0.1641 | 3.0 | 4875 | 0.2473 | | 0.1395 | 4.0 | 6500 | 0.2565 | | 0.1264 | 5.0 | 8125 | 0.2711 | | 0.1203 | 6.0 | 9750 | 0.2849 | | 0.1132 | 7.0 | 11375 | 0.2965 | | 0.1109 | 8.0 | 13000 | 0.3019 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0

ArtBazh/Glass2Can_policy

ArtBazh

2025-08-21T02:00:20Z

0

lerobot

[ "lerobot", "safetensors", "act", "robotics", "dataset:ArtBazh/Glass2Can", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]

robotics

2025-08-21T02:00:00Z

--- datasets: ArtBazh/Glass2Can library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - act - lerobot - robotics --- # Model Card for act  [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 lerobot-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 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

ihsanridzi/blockassist-bc-wiry_flexible_owl_1755740019

ihsanridzi

2025-08-21T02:00:15Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T02:00:11Z

--- 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).

saber1209caoke/my_policy

saber1209caoke

2025-08-21T01:59:24Z

0

lerobot

[ "lerobot", "safetensors", "act", "robotics", "dataset:saber1209caoke/record-test0821", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]

robotics

2025-08-21T01:58:06Z

--- datasets: saber1209caoke/record-test0821 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - act - robotics --- # Model Card for act  [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 lerobot-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 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

rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755739876

rvipitkirubbe

2025-08-21T01:57:47Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mottled foraging ape", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:57:44Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mottled foraging ape --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

BBoDDoGood/slm-gguf

BBoDDoGood

2025-08-21T01:56:39Z

45

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-19T07:09:02Z

# Korean Security Chatbot GGUF Model 이 저장소는 한국어 보안 챗봇을 위한 GGUF 모델을 포함합니다. ## 모델 정보 - **모델 타입**: Qwen2.5-1.5B 기반 한국어 보안 챗봇 - **파일 형식**: GGUF (F16) - **용도**: 보안 상황 인식 및 대응 안내 ## 사용 방법 ### llama.cpp 사용 ```bash ./main -m slm_model.gguf -p "input: [보안 상황] 장소: 사무실, 위험도: 높음" -n 100 ``` ### Python에서 사용 ```python from llama_cpp import Llama llm = Llama(model_path="slm_model.gguf") response = llm("input: [보안 상황]", max_tokens=100) ``` ## 모델 특징 - 한국어 보안 상황 인식 - 실시간 대응 안내 - 다양한 보안 시나리오 지원 ## 라이선스 이 모델은 교육 및 연구 목적으로만 사용되어야 합니다.

DanielJustin/llama3.2_3B_news_qlora

DanielJustin

2025-08-21T01:53:51Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T01:53:51Z

--- license: apache-2.0 ---

Joecheng/llama3.2_3B_news_qlora4

Joecheng

2025-08-21T01:52:42Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T01:52:42Z

--- license: apache-2.0 ---

roeker/blockassist-bc-quick_wiry_owl_1755741070

roeker

2025-08-21T01:52:22Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:51:50Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

SFWolf/llama3.2_3B_news_merged

SFWolf

2025-08-21T01:51:53Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T01:51:53Z

--- license: apache-2.0 ---

blackofdeath/llama3.2_3B_news_merged

blackofdeath

2025-08-21T01:50:46Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T01:50:46Z

--- license: apache-2.0 ---

PGFROG/llama3.2_3B_news_merged

PGFROG

2025-08-21T01:49:11Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T01:49:11Z

--- license: apache-2.0 ---

helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755739370

helmutsukocok

2025-08-21T01:49:06Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:49:03Z

--- 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).

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755739345

lisaozill03

2025-08-21T01:49:02Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:48:59Z

--- 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).

New-tutorial-Jobz-Hunting-Hd-Viral-Videos/FULL.VIDEO.Jobz.Hunting.Sajal.Malik.Viral.Video.Tutorial.Official.link

New-tutorial-Jobz-Hunting-Hd-Viral-Videos

2025-08-21T01:48:20Z

0

null

[ "region:us" ]

null

2025-08-21T01:48:09Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755740789

IvanJAjebu

2025-08-21T01:47:50Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:47:41Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # 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_1755739135

vwzyrraz7l

2025-08-21T01:47:32Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:47:28Z

--- 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).

roeker/blockassist-bc-quick_wiry_owl_1755740776

roeker

2025-08-21T01:47:05Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:46:59Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Donks520/Ya

Donks520

2025-08-21T01:43:07Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-21T01:41:35Z

--- license: apache-2.0 ---

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755740395

IvanJAjebu

2025-08-21T01:41:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:41:00Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

minium/distance-aware-mobile-vla

minium

2025-08-21T01:40:59Z

0

null

[ "region:us" ]

null

2025-08-21T01:29:12Z

# Distance-Aware Mobile VLA Model ## Overview This is a distance-aware Vision-Language-Action (VLA) model for mobile robot navigation, built on top of Kosmos2 vision backbone. ## Model Architecture - **Backbone**: Kosmos2 Vision Model (microsoft/kosmos-2-patch14-224) - **Action Head**: LSTM + MLP - **Distance Awareness**: Distance embedding and fusion layers - **Input**: 8-frame image sequence - **Output**: 2-frame action prediction [linear_x, linear_y, angular_z] ## Performance - **Overall MAE**: 0.2602 - **Success Rate**: 88.7% - **Distance-wise Performance**: - Close: MAE 0.2617 (76.6% success) - Medium: MAE 0.2017 (81.9% success) ⭐ Best - Far: MAE 0.3373 (69.8% success) ## Usage ```python from transformers import AutoProcessor, AutoModel import torch # Load model processor = AutoProcessor.from_pretrained("your-username/distance-aware-mobile-vla") model = AutoModel.from_pretrained("your-username/distance-aware-mobile-vla") # Prepare input images = torch.randn(1, 8, 3, 224, 224) # 8-frame sequence distance_labels = torch.tensor([1]) # 0: close, 1: medium, 2: far # Predict actions with torch.no_grad(): predicted_actions = model(images, distance_labels) ``` ## Training Details - **Dataset**: 480 episodes (160 per distance) - **Augmentation**: Distance-aware specialized augmentation - **Distance Factors**: Close 8x, Medium 5x, Far 8x - **Training Epochs**: 15 ## Key Features - ✅ Distance-aware training and inference - ✅ Kosmos2 vision backbone - ✅ Temporal modeling with LSTM - ✅ Specialized data augmentation - ✅ Balanced performance across distances ## Limitations - Currently predicts 2 frames from 8 input frames - SPACE (stop) action accuracy needs improvement - Far distance performance can be enhanced ## Citation If you use this model, please cite: ``` @misc{distance_aware_mobile_vla_2024, title={Distance-Aware Mobile VLA Model}, author={Your Name}, year={2024} } ```

John6666/mess-illustrious-anime-mix-v10-sdxl

John6666

2025-08-21T01:39:29Z

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "flat anime", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2025-08-21T01:31:57Z

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - flat anime - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1884871/mess-illustrious-anime-mix?modelVersionId=2133466). This model created by [Mess1](https://civitai.com/user/Mess1).

LucasFMartins/gemma-3-1b-it-fine-tuned-demo-5

LucasFMartins

2025-08-21T01:39:00Z

0

null

[ "safetensors", "fine-tuned", "gemma", "lora", "gemma-garage", "text-generation", "conversational", "en", "base_model:google/gemma-3-1b-it", "base_model:adapter:google/gemma-3-1b-it", "license:apache-2.0", "region:us" ]

text-generation

2025-08-21T01:38:55Z

--- language: en license: apache-2.0 tags: - fine-tuned - gemma - lora - gemma-garage base_model: google/gemma-3-1b-it pipeline_tag: text-generation --- # gemma-3-1b-it-fine-tuned-demo-5 Fine-tuned google/gemma-3-1b-it model from Gemma Garage This model was fine-tuned using [Gemma Garage](https://github.com/your-repo/gemma-garage), a platform for fine-tuning Gemma models with LoRA. ## Model Details - **Base Model**: google/gemma-3-1b-it - **Fine-tuning Method**: LoRA (Low-Rank Adaptation) - **Training Platform**: Gemma Garage - **Fine-tuned on**: 2025-08-21 ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LucasFMartins/gemma-3-1b-it-fine-tuned-demo-5") model = AutoModelForCausalLM.from_pretrained("LucasFMartins/gemma-3-1b-it-fine-tuned-demo-5") # Generate text inputs = tokenizer("Your prompt here", return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=100) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## Training Details This model was fine-tuned using the Gemma Garage platform with the following configuration: - Request ID: 7c24eaa3-6289-41e4-b09a-c7e963eb5ed2 - Training completed on: 2025-08-21 01:38:57 UTC For more information about Gemma Garage, visit [our GitHub repository](https://github.com/your-repo/gemma-garage).

lautan/blockassist-bc-gentle_patterned_goat_1755738642

lautan

2025-08-21T01:37:02Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:36:59Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

haihp02/256459dc-f005-4fc1-8241-b653e32be26f

haihp02

2025-08-21T01:36:24Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-21T01:36:12Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755740076

IvanJAjebu

2025-08-21T01:35:48Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:35:39Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755738254

coelacanthxyz

2025-08-21T01:32:35Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:32:28Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

roeker/blockassist-bc-quick_wiry_owl_1755739852

roeker

2025-08-21T01:32:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:31:27Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755739783

IvanJAjebu

2025-08-21T01:30:57Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:30:49Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # 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_1755738274

indoempatnol

2025-08-21T01:30:53Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:30:50Z

--- 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).

manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755738236

manusiaperahu2012

2025-08-21T01:30:24Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:30:21Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

hobson123/blockassist-bc-mammalian_dense_gibbon_1755739373

hobson123

2025-08-21T01:28:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mammalian dense gibbon", "arxiv:2504.07091", "region:us" ]

null

2025-08-21T01:28:29Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mammalian dense gibbon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

John6666/bismuth-illustrious-mix-v40-sdxl

John6666

2025-08-21T01:27:03Z

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "details", "flexibility", "contrast", "vivid colors", "lighting", "facial structure and detail", "composition", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2025-08-21T01:22:06Z

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - details - flexibility - contrast - vivid colors - details - lighting - facial structure and detail - composition - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1359028/bismuth-illustrious-mix?modelVersionId=2131283). This model created by [Axelros](https://civitai.com/user/Axelros).

Mostefa-Terbeche/diabetic-retinopathy-aptos-efficientnet_b3-original-20250720-012032

Mostefa-Terbeche

2025-08-21T01:24:39Z

0

null

[ "diabetic-retinopathy", "medical-imaging", "pytorch", "computer-vision", "retinal-imaging", "dataset:aptos", "license:apache-2.0", "model-index", "region:us" ]

null

2025-08-21T00:58:37Z

--- license: apache-2.0 tags: - diabetic-retinopathy - medical-imaging - pytorch - computer-vision - retinal-imaging datasets: - aptos metrics: - accuracy - quadratic-kappa - auc model-index: - name: aptos_efficientnet_b3_original results: - task: type: image-classification name: Diabetic Retinopathy Classification dataset: type: aptos name: APTOS metrics: - type: accuracy value: 0.7704918032786885 - type: quadratic-kappa value: 0.8974660347551343 --- # Diabetic Retinopathy Classification Model ## Model Description This model is trained for diabetic retinopathy classification using the efficientnet_b3 architecture on the aptos dataset with original preprocessing. ## Model Details - **Architecture**: efficientnet_b3 - **Dataset**: aptos - **Preprocessing**: original - **Training Date**: 20250720-012032 - **Task**: 5-class diabetic retinopathy grading (0-4) - **Directory**: aptos_efficientnet_b3_20250720-012032_new ## Performance - **Test Accuracy**: 0.7704918032786885 - **Test Quadratic Kappa**: 0.8974660347551343 - **Validation Kappa**: 0.8974660347551343 ## Usage ```python import torch from huggingface_hub import hf_hub_download # Download model model_path = hf_hub_download( repo_id="your-username/diabetic-retinopathy-aptos-efficientnet_b3-original", filename="model_best.pt" ) # Load model model = torch.load(model_path, map_location='cpu') ``` ## Classes - 0: No DR (No diabetic retinopathy) - 1: Mild DR (Mild non-proliferative diabetic retinopathy) - 2: Moderate DR (Moderate non-proliferative diabetic retinopathy) - 3: Severe DR (Severe non-proliferative diabetic retinopathy) - 4: Proliferative DR (Proliferative diabetic retinopathy) ## Citation If you use this model, please cite your research paper/thesis.