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
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755575751	sampingkaca72	2025-08-19T04:21:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T04:21:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Bearrr310/ds-train-grpo-1.5B-0818-dsvllm	Bearrr310	2025-08-19T04:16:13Z	0	0	transformers	[ "transformers", "tensorboard", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "endpoints_compatible", "region:us" ]	null	2025-08-19T03:07:06Z	--- library_name: transformers model_name: ds_train_grpo_1.5B-0818-dsvllm tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for ds_train_grpo_1.5B-0818-dsvllm This model is a fine-tuned version of [None](https://huggingface.co/None). 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="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.7.1 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_16_4_all_37_0.001_8960_3	winnieyangwannan	2025-08-19T04:12:08Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-16T18:32:27Z	--- 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]
AnonymousCS/xlmr_english_immigration1	AnonymousCS	2025-08-19T03:58:57Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T03:55:22Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_english_immigration1 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. --> # xlmr_english_immigration1 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2919 - Accuracy: 0.9154 - 1-f1: 0.8706 - 1-recall: 0.8605 - 1-precision: 0.8810 - Balanced Acc: 0.9015 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - 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: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.2078 \| 1.0 \| 5 \| 0.2712 \| 0.9154 \| 0.8736 \| 0.8837 \| 0.8636 \| 0.9074 \| \| 0.1325 \| 2.0 \| 10 \| 0.2784 \| 0.9077 \| 0.8571 \| 0.8372 \| 0.8780 \| 0.8899 \| \| 0.1726 \| 3.0 \| 15 \| 0.2919 \| 0.9154 \| 0.8706 \| 0.8605 \| 0.8810 \| 0.9015 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
rockst4r4/Qwen3-0.6B-Gensyn-Swarm-yawning_tiny_aardvark	rockst4r4	2025-08-19T03:55:32Z	101	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am yawning_tiny_aardvark", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-14T00:29:24Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am yawning_tiny_aardvark --- # 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]
NexVeridian/Kimi-VL-A3B-Thinking-2506-5bit	NexVeridian	2025-08-19T03:30:29Z	0	0	mlx	[ "mlx", "safetensors", "kimi_vl", "text-generation", "conversational", "custom_code", "base_model:moonshotai/Kimi-VL-A3B-Thinking-2506", "base_model:quantized:moonshotai/Kimi-VL-A3B-Thinking-2506", "license:mit", "5-bit", "region:us" ]	text-generation	2025-08-19T03:25:12Z	--- base_model: moonshotai/Kimi-VL-A3B-Thinking-2506 license: mit pipeline_tag: text-generation library_name: mlx tags: - mlx --- # NexVeridian/Kimi-VL-A3B-Thinking-2506-5bit This model [NexVeridian/Kimi-VL-A3B-Thinking-2506-5bit](https://huggingface.co/NexVeridian/Kimi-VL-A3B-Thinking-2506-5bit) was converted to MLX format from [moonshotai/Kimi-VL-A3B-Thinking-2506](https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking-2506) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("NexVeridian/Kimi-VL-A3B-Thinking-2506-5bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
WenFengg/21_14l12_19_8	WenFengg	2025-08-19T03:07:33Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T03:02:14Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
samnemo/Qwen3-0.6B-Gensyn-Swarm-keen_fast_newt	samnemo	2025-08-19T01:54:41Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am keen_fast_newt", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T01:54:27Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am keen_fast_newt --- # 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]
indoempatnol/blockassist-bc-fishy_wary_swan_1755565663	indoempatnol	2025-08-19T01:33:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T01:33:11Z	--- 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).
thanobidex/blockassist-bc-colorful_shiny_hare_1755565293	thanobidex	2025-08-19T01:26:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T01:26:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
teapotai/teapotllm	teapotai	2025-08-19T01:26:28Z	61	179	transformers	[ "transformers", "onnx", "safetensors", "t5", "text2text-generation", "text-generation", "transformers.js", "en", "fr", "ro", "de", "multilingual", "dataset:teapotai/synthqa", "dataset:teapotai/teapot-chat", "base_model:google/flan-t5-large", "base_model:quantized:google/flan-t5-large", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-19T02:29:11Z	--- license: mit datasets: - teapotai/synthqa - teapotai/teapot-chat language: - en - fr - ro - de - multilingual library_name: transformers tags: - text-generation - transformers.js widget: - text: >- Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built by and for the community. What devices can teapot run on? example_title: Question Answering - text: >- Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built by and for the community. Tell me about teapotllm example_title: Summarization Answering - text: >- Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built by and for the community. Extract the number of parameters example_title: Information Extraction - text: >- Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built by and for the community. How many parameters is Deepseek? example_title: Hallucination Resistance base_model: - google/flan-t5-large pipeline_tag: text2text-generation --- # Teapot LLM [Website](https://teapotai.com/) \| [Try out our demo on Discord](https://discord.gg/hPxGSn5dST) Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. Teapot is a model built by and for the community. ![https://teapotai.com/assets/teapotevalbanner.jpg](https://teapotai.com/assets/teapot_banner.png) [Evaluation Details](https://huggingface.co/teapotai/teapotllm#model-evaluation) ### Conversational Question Answering Teapot is fine-tuned to provide friendly, conversational answers using context and documents provided as references. ### Hallucination Resistance Teapot is trained to only output answers that can be derived from the provided context, ensuring that even though it is a small model, it performs demonstrably better by refusing to answer questions when there is insufficient data. ### Retrieval Augmented Generation Teapot is further fine-tuned on the task of retrieval augmented generation by utilizing a custom [embedding model](https://huggingface.co/teapotai/teapotembedding). We perform RAG across multiple documents from our training data and the model is able to learn to extract relevant details for question answering. ### Information Extraction Teapot has been trained to extract succint answers in a variety of format enabling efficient document parsing. Teapot is trained natively to output standard data types such as numbers, strings, and even json. --- ## Getting Started We recommend using our library [teapotai](https://pypi.org/project/teapotai/) to quickly integrate our models into production environments, as it handles the overhead of model configuration, document embeddings, error handling and prompt formatting. However, you can directly use the model from the transformers library on huggingface. ### Installation ```bash ! pip install teapotai ``` --- ### 1. General Question Answering (QnA) Teapot can be used for general question answering based on a provided context. The model is optimized to respond conversationally and is trained to avoid answering questions that can't be answered from the given context, reducing hallucinations. #### Example: ```python from teapotai import TeapotAI # Sample context context = """ The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889. It stands at a height of 330 meters and is one of the most recognizable structures in the world. """ teapot_ai = TeapotAI() answer = teapot_ai.query( query="What is the height of the Eiffel Tower?", context=context ) print(answer) # => "The Eiffel Tower stands at a height of 330 meters. " ``` #### Hallucination Example: ```python from teapotai import TeapotAI # Sample context without height information context = """ The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889. """ teapot_ai = TeapotAI() answer = teapot_ai.query( query="What is the height of the Eiffel Tower?", context=context ) print(answer) # => "I don't have information on the height of the Eiffel Tower." ``` --- ### 2. Chat with Retrieval Augmented Generation (RAG) Teapot can also use Retrieval-Augmented Generation (RAG) to determine which documents are relevant before answering a question. This is useful when you have many documents you want to use as context, ensuring the model answers based on the most relevant ones. #### Example: ```python from teapotai import TeapotAI # Sample documents (in practice, these could be articles or longer documents) documents = [ "The Eiffel Tower is located in Paris, France. It was built in 1889 and stands 330 meters tall.", "The Great Wall of China is a historic fortification that stretches over 13,000 miles.", "The Amazon Rainforest is the largest tropical rainforest in the world, covering over 5.5 million square kilometers.", "The Grand Canyon is a natural landmark located in Arizona, USA, carved by the Colorado River.", "Mount Everest is the tallest mountain on Earth, located in the Himalayas along the border between Nepal and China.", "The Colosseum in Rome, Italy, is an ancient amphitheater known for its gladiator battles.", "The Sahara Desert is the largest hot desert in the world, located in North Africa.", "The Nile River is the longest river in the world, flowing through northeastern Africa.", "The Empire State Building is an iconic skyscraper in New York City that was completed in 1931 and stands at 1454 feet tall." ] # Initialize TeapotAI with documents for RAG teapot_ai = TeapotAI(documents=documents) # Get the answer using RAG answer = teapot_ai.chat([ { "role":"system", "content": "You are an agent designed to answer facts about famous landmarks." }, { "role":"user", "content": "What landmark was constructed in the 1800s?" } ]) print(answer) # => The Eiffel Tower was constructed in the 1800s. ``` #### Loading RAG Model: You can save a model with pre-computed embeddings to reduce loading times. TeapotAI is pickle-compatible and can be saved and loaded as shown below. ```python import pickle # Pickle the TeapotAI model to a file with pre-computed embeddings with open("teapot_ai.pkl", "wb") as f: pickle.dump(teapot_ai, f) # Load the pickled model with open("teapot_ai.pkl", "rb") as f: loaded_teapot_ai = pickle.load(f) # You can now use the loaded instance as you would normally print(len(loaded_teapot_ai.documents)) # => 10 Documents with precomputed embeddings loaded_teapot_ai.query("What city is the Eiffel Tower in?") # => "The Eiffel Tower is located in Paris, France." ``` --- ### 3. Information Extraction Teapot can be used to extract structured information from context using pre-defined JSON structures. The extract method takes a Pydantic model to ensure Teapot extracts the correct types. Teapot can infer fields based on names and will also leverage descriptions if available. This method can also be used with RAG and query functionalities natively. #### Example: ```python from teapotai import TeapotAI from pydantic import BaseModel # Sample text containing apartment details apartment_description = """ This spacious 2-bedroom apartment is available for rent in downtown New York. The monthly rent is $2500. It includes 1 bathrooms and a fully equipped kitchen with modern appliances. Pets are welcome! Please reach out to us at 555-123-4567 or john@realty.com """ # Define a Pydantic model for the data you want to extract class ApartmentInfo(BaseModel): rent: float = Field(..., description="the monthly rent in dollars") bedrooms: int = Field(..., description="the number of bedrooms") bathrooms: int = Field(..., description="the number of bathrooms") phone_number: str # Initialize TeapotAI teapot_ai = TeapotAI() # Extract the apartment details extracted_info = teapot_ai.extract( ApartmentInfo, context=apartment_description ) print(extracted_info) # => ApartmentInfo(rent=2500.0 bedrooms=2 bathrooms=1 phone_number='555-123-4567') ``` ### Native Transformer Support While we recommend using TeapotAI's library, you can load the base model directly with Hugging Face's Transformers library as follows: ```python from transformers import pipeline # Load the model teapot_ai = pipeline("text2text-generation", "teapotai/teapotllm") context = """ The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889. It stands at a height of 330 meters and is one of the most recognizable structures in the world. """ question = "What is the height of the Eiffel Tower?" answer = teapot_ai(context+"\n"+question) print(answer[0].get('generated_text')) # => The Eiffel Tower stands at a height of 330 meters. ``` ### Transformers.js Support You can even run the model in-browser (or any other JavaScript environment) with [Transformers.js](https://huggingface.co/docs/transformers.js) as follows: ```js // npm i @huggingface/transformers import { pipeline } from "@huggingface/transformers"; const teapot_ai = await pipeline("text2text-generation", "teapotai/teapotllm"); const context = ` The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889. It stands at a height of 330 meters and is one of the most recognizable structures in the world. `; const question = "What is the height of the Eiffel Tower?"; const answer = await teapot_ai(context + "\n" + question); console.log(answer[0].generated_text); // => " The Eiffel Tower stands at a height of 330 meters." ``` --- ## Model Details Teapot LLM is fine-tuned from [flan-t5-large](https://huggingface.co/google/flan-t5-large) on a [synthetic dataset](https://huggingface.co/datasets/teapotai/synthqa) of LLM tasks generated using [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3). ### Training Details - [Dataset] ~10mb synthetic dataset consisting of QnA pairs with a variety of task specific formats. - [Methodology] The model is trained to mimic task specific output formats, and is scored based on its ability to output relevant, succint and verifiable answers in the requested format. - [Hardware] Teapot was trained for ~10hr on an A100 provided by Google Colab. - [Hyperparameters] The model was trained with various learning rates and monitored to ensure task specific performance was learned without catastrophic forgetting. ### Model Evaluation TeapotLLM is focused on in-context reasoning tasks, and therefore most benchmarks are not suitable for evaluation. We want TeapotLLM to be a practical tool for QnA and information extraction, so we have developed custom datasets to benchmark performance. [Evaluation Notebook Here](https://github.com/zakerytclarke/teapot/blob/main/docs/evals/TeapotLLM_Benchmark.ipynb) #### Synthqa Evaluation [Synthqa](https://huggingface.co/datasets/teapotai/synthqa) is a dataset focused on in-context QnA and information extraction tasks. We use the validation set to benchmark TeapotLLM against other models of similar size. All benchmarks were run using a Google Colab Notebook running on CPU with High Ram. Teapot significantly outperforms models of similar size, with low latency CPU inference and improved accuracy. ![https://teapotai.com/assets/synthqa_eval.jpg](https://teapotai.com/assets/synthqa_eval.jpg) ![https://teapotai.com/assets/synthqa_eval_split.jpg](https://teapotai.com/assets/synthqa_eval_split.jpg) We also manually annotated hallucination refusals from models. All models were asked to not answer if the answer could not be derived from the provided context. TeapotLLM exhibits significantly stronger hallucination resistant behavior, without compromising on incorrect refusals. ![https://teapotai.com/assets/hallucination_metrics.png](https://teapotai.com/assets/hallucination_metrics.png) ### Limitations and Risks Teapot is trained specifically for question answering use cases and is not intended to be used for code generation, creative writing or critical decision applications. Teapot has only been trained on specific languages supported by flan-t5 and has not been evaluated for performance in languages other than English. ### License This model, the embedding model and the synthetic dataset are all provided open source under the MIT LICENSE. ## Questions, Feature Requests? We hope you find TeapotAI useful and are continuosuly working to improve our models. Please reach out to us on our [Discord](https://discord.gg/hPxGSn5dST) for any technical help or feature requrests. We look forwarding to seeing what our community can build!
mizutoukotori/pi0_so101_next	mizutoukotori	2025-08-19T00:34:58Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pi0", "dataset:mizutoukotori/pick_the_pink_block", "arxiv:2410.24164", "license:apache-2.0", "region:us" ]	robotics	2025-08-19T00:24:31Z	--- datasets: mizutoukotori/pick_the_pink_block library_name: lerobot license: apache-2.0 model_name: pi0 pipeline_tag: robotics tags: - robotics - lerobot - pi0 --- # Model Card for pi0 <!-- Provide a quick summary of what the model is/does. --> [Pi0](https://huggingface.co/papers/2410.24164) is a generalist vision-language-action transformer that converts multimodal observations and text instructions into robot actions for zero-shot task transfer. 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
dashawn888/MyGemmaNPC	dashawn888	2025-08-19T00:29:30Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T00:25:41Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: MyGemmaNPC tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for MyGemmaNPC This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dashawn888/MyGemmaNPC", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
sameddallaa/q-Learning-taxi-v3	sameddallaa	2025-08-19T00:22:26Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-08-19T00:09:06Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Learning-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage model = load_from_hub(repo_id="sameddallaa/q-Learning-taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755561277	vwzyrraz7l	2025-08-19T00:19:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T00:19:09Z	--- 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).
thanobidex/blockassist-bc-colorful_shiny_hare_1755560145	thanobidex	2025-08-19T00:01:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T00:01:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
donoway/ARC-Challenge_Llama-3.2-1B-kxmkyfib	donoway	2025-08-19T00:01:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T23:37:41Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Challenge_Llama-3.2-1B-kxmkyfib 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. --> # ARC-Challenge_Llama-3.2-1B-kxmkyfib This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.6704 - Model Preparation Time: 0.0058 - Mdl: 3740.1267 - Accumulated Loss: 2592.4583 - Correct Preds: 110.0 - Total Preds: 299.0 - Accuracy: 0.3679 - Correct Gen Preds: 105.0 - Gen Accuracy: 0.3512 - Correct Gen Preds 32: 4.0 - Correct Preds 32: 5.0 - Total Labels 32: 64.0 - Accuracy 32: 0.0781 - Gen Accuracy 32: 0.0625 - Correct Gen Preds 33: 18.0 - Correct Preds 33: 19.0 - Total Labels 33: 73.0 - Accuracy 33: 0.2603 - Gen Accuracy 33: 0.2466 - Correct Gen Preds 34: 61.0 - Correct Preds 34: 61.0 - Total Labels 34: 78.0 - Accuracy 34: 0.7821 - Gen Accuracy 34: 0.7821 - Correct Gen Preds 35: 21.0 - Correct Preds 35: 24.0 - Total Labels 35: 83.0 - Accuracy 35: 0.2892 - Gen Accuracy 35: 0.2530 - Correct Gen Preds 36: 1.0 - Correct Preds 36: 1.0 - Total Labels 36: 1.0 - Accuracy 36: 1.0 - Gen Accuracy 36: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Mdl \| Accumulated Loss \| Correct Preds \| Total Preds \| Accuracy \| Correct Gen Preds \| Gen Accuracy \| Correct Gen Preds 32 \| Correct Preds 32 \| Total Labels 32 \| Accuracy 32 \| Gen Accuracy 32 \| Correct Gen Preds 33 \| Correct Preds 33 \| Total Labels 33 \| Accuracy 33 \| Gen Accuracy 33 \| Correct Gen Preds 34 \| Correct Preds 34 \| Total Labels 34 \| Accuracy 34 \| Gen Accuracy 34 \| Correct Gen Preds 35 \| Correct Preds 35 \| Total Labels 35 \| Accuracy 35 \| Gen Accuracy 35 \| Correct Gen Preds 36 \| Correct Preds 36 \| Total Labels 36 \| Accuracy 36 \| Gen Accuracy 36 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:---------:\|:----------------:\|:-------------:\|:-----------:\|:--------:\|:-----------------:\|:------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\| \| No log \| 0 \| 0 \| 1.6389 \| 0.0058 \| 706.9523 \| 490.0220 \| 66.0 \| 299.0 \| 0.2207 \| 66.0 \| 0.2207 \| 62.0 \| 62.0 \| 64.0 \| 0.9688 \| 0.9688 \| 0.0 \| 0.0 \| 73.0 \| 0.0 \| 0.0 \| 4.0 \| 4.0 \| 78.0 \| 0.0513 \| 0.0513 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.6477 \| 1.0 \| 1 \| 1.6389 \| 0.0058 \| 706.9523 \| 490.0220 \| 66.0 \| 299.0 \| 0.2207 \| 66.0 \| 0.2207 \| 62.0 \| 62.0 \| 64.0 \| 0.9688 \| 0.9688 \| 0.0 \| 0.0 \| 73.0 \| 0.0 \| 0.0 \| 4.0 \| 4.0 \| 78.0 \| 0.0513 \| 0.0513 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.6477 \| 2.0 \| 2 \| 2.0454 \| 0.0058 \| 882.3094 \| 611.5703 \| 76.0 \| 299.0 \| 0.2542 \| 76.0 \| 0.2542 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 72.0 \| 72.0 \| 73.0 \| 0.9863 \| 0.9863 \| 4.0 \| 4.0 \| 78.0 \| 0.0513 \| 0.0513 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 2.0979 \| 3.0 \| 3 \| 1.3844 \| 0.0058 \| 597.1828 \| 413.9355 \| 87.0 \| 299.0 \| 0.2910 \| 87.0 \| 0.2910 \| 0.0 \| 0.0 \| 64.0 \| 0.0 \| 0.0 \| 60.0 \| 60.0 \| 73.0 \| 0.8219 \| 0.8219 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 27.0 \| 27.0 \| 83.0 \| 0.3253 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.3205 \| 4.0 \| 4 \| 1.8474 \| 0.0058 \| 796.8976 \| 552.3673 \| 64.0 \| 299.0 \| 0.2140 \| 64.0 \| 0.2140 \| 64.0 \| 64.0 \| 64.0 \| 1.0 \| 1.0 \| 0.0 \| 0.0 \| 73.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.2752 \| 5.0 \| 5 \| 1.5099 \| 0.0058 \| 651.3276 \| 451.4659 \| 78.0 \| 299.0 \| 0.2609 \| 78.0 \| 0.2609 \| 47.0 \| 47.0 \| 64.0 \| 0.7344 \| 0.7344 \| 7.0 \| 7.0 \| 73.0 \| 0.0959 \| 0.0959 \| 21.0 \| 21.0 \| 78.0 \| 0.2692 \| 0.2692 \| 3.0 \| 3.0 \| 83.0 \| 0.0361 \| 0.0361 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.8745 \| 6.0 \| 6 \| 1.9551 \| 0.0058 \| 843.3534 \| 584.5680 \| 75.0 \| 299.0 \| 0.2508 \| 58.0 \| 0.1940 \| 24.0 \| 35.0 \| 64.0 \| 0.5469 \| 0.375 \| 10.0 \| 15.0 \| 73.0 \| 0.2055 \| 0.1370 \| 8.0 \| 8.0 \| 78.0 \| 0.1026 \| 0.1026 \| 16.0 \| 17.0 \| 83.0 \| 0.2048 \| 0.1928 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.3891 \| 7.0 \| 7 \| 2.2535 \| 0.0058 \| 972.0730 \| 673.7897 \| 81.0 \| 299.0 \| 0.2709 \| 48.0 \| 0.1605 \| 2.0 \| 12.0 \| 64.0 \| 0.1875 \| 0.0312 \| 20.0 \| 33.0 \| 73.0 \| 0.4521 \| 0.2740 \| 13.0 \| 18.0 \| 78.0 \| 0.2308 \| 0.1667 \| 13.0 \| 18.0 \| 83.0 \| 0.2169 \| 0.1566 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0723 \| 8.0 \| 8 \| 3.3987 \| 0.0058 \| 1466.0698 \| 1016.2021 \| 88.0 \| 299.0 \| 0.2943 \| 37.0 \| 0.1237 \| 3.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.0469 \| 7.0 \| 16.0 \| 73.0 \| 0.2192 \| 0.0959 \| 17.0 \| 33.0 \| 78.0 \| 0.4231 \| 0.2179 \| 10.0 \| 17.0 \| 83.0 \| 0.2048 \| 0.1205 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0151 \| 9.0 \| 9 \| 4.3882 \| 0.0058 \| 1892.9204 \| 1312.0724 \| 106.0 \| 299.0 \| 0.3545 \| 51.0 \| 0.1706 \| 2.0 \| 11.0 \| 64.0 \| 0.1719 \| 0.0312 \| 7.0 \| 21.0 \| 73.0 \| 0.2877 \| 0.0959 \| 29.0 \| 47.0 \| 78.0 \| 0.6026 \| 0.3718 \| 13.0 \| 27.0 \| 83.0 \| 0.3253 \| 0.1566 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0005 \| 10.0 \| 10 \| 5.3274 \| 0.0058 \| 2298.0553 \| 1592.8905 \| 105.0 \| 299.0 \| 0.3512 \| 78.0 \| 0.2609 \| 3.0 \| 9.0 \| 64.0 \| 0.1406 \| 0.0469 \| 14.0 \| 21.0 \| 73.0 \| 0.2877 \| 0.1918 \| 45.0 \| 50.0 \| 78.0 \| 0.6410 \| 0.5769 \| 16.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.1928 \| 0.0 \| 1.0 \| 1.0 \| 1.0 \| 0.0 \| \| 0.0 \| 11.0 \| 11 \| 6.1034 \| 0.0058 \| 2632.8159 \| 1824.9289 \| 106.0 \| 299.0 \| 0.3545 \| 91.0 \| 0.3043 \| 3.0 \| 7.0 \| 64.0 \| 0.1094 \| 0.0469 \| 19.0 \| 22.0 \| 73.0 \| 0.3014 \| 0.2603 \| 51.0 \| 52.0 \| 78.0 \| 0.6667 \| 0.6538 \| 17.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2048 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 12.0 \| 12 \| 6.7276 \| 0.0058 \| 2902.0719 \| 2011.5630 \| 107.0 \| 299.0 \| 0.3579 \| 94.0 \| 0.3144 \| 2.0 \| 7.0 \| 64.0 \| 0.1094 \| 0.0312 \| 18.0 \| 20.0 \| 73.0 \| 0.2740 \| 0.2466 \| 54.0 \| 55.0 \| 78.0 \| 0.7051 \| 0.6923 \| 19.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2289 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 13.0 \| 13 \| 7.1073 \| 0.0058 \| 3065.8497 \| 2125.0851 \| 107.0 \| 299.0 \| 0.3579 \| 99.0 \| 0.3311 \| 2.0 \| 4.0 \| 64.0 \| 0.0625 \| 0.0312 \| 19.0 \| 22.0 \| 73.0 \| 0.3014 \| 0.2603 \| 56.0 \| 56.0 \| 78.0 \| 0.7179 \| 0.7179 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 14.0 \| 14 \| 7.4064 \| 0.0058 \| 3194.8512 \| 2214.5021 \| 106.0 \| 299.0 \| 0.3545 \| 97.0 \| 0.3244 \| 3.0 \| 4.0 \| 64.0 \| 0.0625 \| 0.0469 \| 17.0 \| 20.0 \| 73.0 \| 0.2740 \| 0.2329 \| 55.0 \| 56.0 \| 78.0 \| 0.7179 \| 0.7051 \| 21.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 15.0 \| 15 \| 7.6826 \| 0.0058 \| 3314.0289 \| 2297.1098 \| 106.0 \| 299.0 \| 0.3545 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 56.0 \| 56.0 \| 78.0 \| 0.7179 \| 0.7179 \| 21.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 16.0 \| 16 \| 7.8656 \| 0.0058 \| 3392.9475 \| 2351.8120 \| 101.0 \| 299.0 \| 0.3378 \| 94.0 \| 0.3144 \| 2.0 \| 4.0 \| 64.0 \| 0.0625 \| 0.0312 \| 15.0 \| 16.0 \| 73.0 \| 0.2192 \| 0.2055 \| 56.0 \| 56.0 \| 78.0 \| 0.7179 \| 0.7179 \| 20.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2410 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 17.0 \| 17 \| 8.0201 \| 0.0058 \| 3459.6157 \| 2398.0229 \| 101.0 \| 299.0 \| 0.3378 \| 95.0 \| 0.3177 \| 2.0 \| 3.0 \| 64.0 \| 0.0469 \| 0.0312 \| 16.0 \| 17.0 \| 73.0 \| 0.2329 \| 0.2192 \| 56.0 \| 56.0 \| 78.0 \| 0.7179 \| 0.7179 \| 20.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2410 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 18.0 \| 18 \| 8.1050 \| 0.0058 \| 3496.2268 \| 2423.3998 \| 106.0 \| 299.0 \| 0.3545 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 56.0 \| 56.0 \| 78.0 \| 0.7179 \| 0.7179 \| 22.0 \| 26.0 \| 83.0 \| 0.3133 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 19.0 \| 19 \| 8.2369 \| 0.0058 \| 3553.1220 \| 2462.8365 \| 103.0 \| 299.0 \| 0.3445 \| 97.0 \| 0.3244 \| 3.0 \| 4.0 \| 64.0 \| 0.0625 \| 0.0469 \| 16.0 \| 17.0 \| 73.0 \| 0.2329 \| 0.2192 \| 56.0 \| 56.0 \| 78.0 \| 0.7179 \| 0.7179 \| 21.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 20.0 \| 20 \| 8.3117 \| 0.0058 \| 3585.3793 \| 2485.1956 \| 103.0 \| 299.0 \| 0.3445 \| 97.0 \| 0.3244 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 55.0 \| 55.0 \| 78.0 \| 0.7051 \| 0.7051 \| 20.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2410 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 21.0 \| 21 \| 8.3686 \| 0.0058 \| 3609.9493 \| 2502.2262 \| 104.0 \| 299.0 \| 0.3478 \| 98.0 \| 0.3278 \| 3.0 \| 4.0 \| 64.0 \| 0.0625 \| 0.0469 \| 16.0 \| 17.0 \| 73.0 \| 0.2329 \| 0.2192 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 21.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 22.0 \| 22 \| 8.4223 \| 0.0058 \| 3633.1012 \| 2518.2739 \| 107.0 \| 299.0 \| 0.3579 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 16.0 \| 17.0 \| 73.0 \| 0.2329 \| 0.2192 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 23.0 \| 26.0 \| 83.0 \| 0.3133 \| 0.2771 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 23.0 \| 23 \| 8.4732 \| 0.0058 \| 3655.0572 \| 2533.4926 \| 105.0 \| 299.0 \| 0.3512 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 16.0 \| 17.0 \| 73.0 \| 0.2329 \| 0.2192 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 24.0 \| 24 \| 8.5141 \| 0.0058 \| 3672.7066 \| 2545.7263 \| 105.0 \| 299.0 \| 0.3512 \| 101.0 \| 0.3378 \| 3.0 \| 4.0 \| 64.0 \| 0.0625 \| 0.0469 \| 16.0 \| 17.0 \| 73.0 \| 0.2329 \| 0.2192 \| 59.0 \| 59.0 \| 78.0 \| 0.7564 \| 0.7564 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 25.0 \| 25 \| 8.5522 \| 0.0058 \| 3689.1067 \| 2557.0939 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 3.0 \| 4.0 \| 64.0 \| 0.0625 \| 0.0469 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 23.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2771 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 26.0 \| 26 \| 8.5757 \| 0.0058 \| 3699.2461 \| 2564.1220 \| 107.0 \| 299.0 \| 0.3579 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 27.0 \| 27 \| 8.5743 \| 0.0058 \| 3698.6687 \| 2563.7218 \| 109.0 \| 299.0 \| 0.3645 \| 104.0 \| 0.3478 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 60.0 \| 60.0 \| 78.0 \| 0.7692 \| 0.7692 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 28.0 \| 28 \| 8.5966 \| 0.0058 \| 3708.2991 \| 2570.3970 \| 107.0 \| 299.0 \| 0.3579 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 29.0 \| 29 \| 8.6279 \| 0.0058 \| 3721.7709 \| 2579.7350 \| 107.0 \| 299.0 \| 0.3579 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 30.0 \| 30 \| 8.6494 \| 0.0058 \| 3731.0587 \| 2586.1728 \| 106.0 \| 299.0 \| 0.3545 \| 101.0 \| 0.3378 \| 3.0 \| 4.0 \| 64.0 \| 0.0625 \| 0.0469 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 59.0 \| 59.0 \| 78.0 \| 0.7564 \| 0.7564 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 31.0 \| 31 \| 8.6186 \| 0.0058 \| 3717.7530 \| 2576.9500 \| 109.0 \| 299.0 \| 0.3645 \| 104.0 \| 0.3478 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 19.0 \| 20.0 \| 73.0 \| 0.2740 \| 0.2603 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 32.0 \| 32 \| 8.6690 \| 0.0058 \| 3739.4893 \| 2592.0165 \| 106.0 \| 299.0 \| 0.3545 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 33.0 \| 33 \| 8.6733 \| 0.0058 \| 3741.3665 \| 2593.3176 \| 104.0 \| 299.0 \| 0.3478 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 34.0 \| 34 \| 8.6682 \| 0.0058 \| 3739.1696 \| 2591.7949 \| 107.0 \| 299.0 \| 0.3579 \| 103.0 \| 0.3445 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 59.0 \| 59.0 \| 78.0 \| 0.7564 \| 0.7564 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 35.0 \| 35 \| 8.6618 \| 0.0058 \| 3736.4194 \| 2589.8885 \| 107.0 \| 299.0 \| 0.3579 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 36.0 \| 36 \| 8.6886 \| 0.0058 \| 3747.9652 \| 2597.8915 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 37.0 \| 37 \| 8.7000 \| 0.0058 \| 3752.8641 \| 2601.2872 \| 107.0 \| 299.0 \| 0.3579 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 38.0 \| 38 \| 8.6748 \| 0.0058 \| 3741.9910 \| 2593.7505 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 39.0 \| 39 \| 8.7015 \| 0.0058 \| 3753.5504 \| 2601.7629 \| 106.0 \| 299.0 \| 0.3545 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 40.0 \| 40 \| 8.6790 \| 0.0058 \| 3743.8151 \| 2595.0149 \| 105.0 \| 299.0 \| 0.3512 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 20.0 \| 22.0 \| 83.0 \| 0.2651 \| 0.2410 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 41.0 \| 41 \| 8.7102 \| 0.0058 \| 3757.2716 \| 2604.3422 \| 103.0 \| 299.0 \| 0.3445 \| 99.0 \| 0.3311 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 20.0 \| 22.0 \| 83.0 \| 0.2651 \| 0.2410 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 42.0 \| 42 \| 8.7133 \| 0.0058 \| 3758.6019 \| 2605.2643 \| 104.0 \| 299.0 \| 0.3478 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 43.0 \| 43 \| 8.6911 \| 0.0058 \| 3749.0326 \| 2598.6313 \| 105.0 \| 299.0 \| 0.3512 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 20.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2410 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 44.0 \| 44 \| 8.6606 \| 0.0058 \| 3735.8922 \| 2589.5231 \| 109.0 \| 299.0 \| 0.3645 \| 104.0 \| 0.3478 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 60.0 \| 60.0 \| 78.0 \| 0.7692 \| 0.7692 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 45.0 \| 45 \| 8.6434 \| 0.0058 \| 3728.4639 \| 2584.3742 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 46.0 \| 46 \| 8.6821 \| 0.0058 \| 3745.1805 \| 2595.9613 \| 105.0 \| 299.0 \| 0.3512 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 16.0 \| 17.0 \| 73.0 \| 0.2329 \| 0.2192 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 47.0 \| 47 \| 8.6892 \| 0.0058 \| 3748.2264 \| 2598.0726 \| 104.0 \| 299.0 \| 0.3478 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 48.0 \| 48 \| 8.6828 \| 0.0058 \| 3745.4845 \| 2596.1720 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 49.0 \| 49 \| 8.6937 \| 0.0058 \| 3750.1558 \| 2599.4099 \| 107.0 \| 299.0 \| 0.3579 \| 103.0 \| 0.3445 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 50.0 \| 50 \| 8.6818 \| 0.0058 \| 3745.0372 \| 2595.8620 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 51.0 \| 51 \| 8.6842 \| 0.0058 \| 3746.0470 \| 2596.5620 \| 107.0 \| 299.0 \| 0.3579 \| 103.0 \| 0.3445 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 52.0 \| 52 \| 8.6852 \| 0.0058 \| 3746.4838 \| 2596.8647 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 53.0 \| 53 \| 8.6656 \| 0.0058 \| 3738.0351 \| 2591.0085 \| 108.0 \| 299.0 \| 0.3612 \| 103.0 \| 0.3445 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 59.0 \| 59.0 \| 78.0 \| 0.7564 \| 0.7564 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 54.0 \| 54 \| 8.6816 \| 0.0058 \| 3744.9258 \| 2595.7848 \| 107.0 \| 299.0 \| 0.3579 \| 103.0 \| 0.3445 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 60.0 \| 60.0 \| 78.0 \| 0.7692 \| 0.7692 \| 20.0 \| 22.0 \| 83.0 \| 0.2651 \| 0.2410 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 55.0 \| 55 \| 8.6704 \| 0.0058 \| 3740.1267 \| 2592.4583 \| 110.0 \| 299.0 \| 0.3679 \| 105.0 \| 0.3512 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 61.0 \| 61.0 \| 78.0 \| 0.7821 \| 0.7821 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 56.0 \| 56 \| 8.6804 \| 0.0058 \| 3744.4474 \| 2595.4532 \| 106.0 \| 299.0 \| 0.3545 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 57.0 \| 57 \| 8.6920 \| 0.0058 \| 3749.4247 \| 2598.9031 \| 107.0 \| 299.0 \| 0.3579 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 59.0 \| 59.0 \| 78.0 \| 0.7564 \| 0.7564 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 58.0 \| 58 \| 8.6874 \| 0.0058 \| 3747.4329 \| 2597.5225 \| 107.0 \| 299.0 \| 0.3579 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 59.0 \| 59 \| 8.6562 \| 0.0058 \| 3733.9920 \| 2588.2061 \| 105.0 \| 299.0 \| 0.3512 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 20.0 \| 22.0 \| 83.0 \| 0.2651 \| 0.2410 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 60.0 \| 60 \| 8.6886 \| 0.0058 \| 3747.9552 \| 2597.8846 \| 106.0 \| 299.0 \| 0.3545 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 16.0 \| 17.0 \| 73.0 \| 0.2329 \| 0.2192 \| 59.0 \| 59.0 \| 78.0 \| 0.7564 \| 0.7564 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 61.0 \| 61 \| 8.7271 \| 0.0058 \| 3764.5849 \| 2609.4114 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 62.0 \| 62 \| 8.6558 \| 0.0058 \| 3733.8239 \| 2588.0895 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 63.0 \| 63 \| 8.6914 \| 0.0058 \| 3749.1784 \| 2598.7324 \| 108.0 \| 299.0 \| 0.3612 \| 103.0 \| 0.3445 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 59.0 \| 59.0 \| 78.0 \| 0.7564 \| 0.7564 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 64.0 \| 64 \| 8.7049 \| 0.0058 \| 3755.0161 \| 2602.7788 \| 108.0 \| 299.0 \| 0.3612 \| 104.0 \| 0.3478 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 60.0 \| 60.0 \| 78.0 \| 0.7692 \| 0.7692 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 65.0 \| 65 \| 8.6536 \| 0.0058 \| 3732.8656 \| 2587.4253 \| 109.0 \| 299.0 \| 0.3645 \| 104.0 \| 0.3478 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 59.0 \| 59.0 \| 78.0 \| 0.7564 \| 0.7564 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 66.0 \| 66 \| 8.6849 \| 0.0058 \| 3746.3642 \| 2596.7818 \| 106.0 \| 299.0 \| 0.3545 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 59.0 \| 59.0 \| 78.0 \| 0.7564 \| 0.7564 \| 20.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2410 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 67.0 \| 67 \| 8.6528 \| 0.0058 \| 3732.5153 \| 2587.1824 \| 108.0 \| 299.0 \| 0.3612 \| 104.0 \| 0.3478 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 60.0 \| 60.0 \| 78.0 \| 0.7692 \| 0.7692 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 68.0 \| 68 \| 8.7122 \| 0.0058 \| 3758.1449 \| 2604.9476 \| 106.0 \| 299.0 \| 0.3545 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 69.0 \| 69 \| 8.6601 \| 0.0058 \| 3735.6760 \| 2589.3733 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 70.0 \| 70 \| 8.6728 \| 0.0058 \| 3741.1365 \| 2593.1582 \| 105.0 \| 299.0 \| 0.3512 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 71.0 \| 71 \| 8.7202 \| 0.0058 \| 3761.6155 \| 2607.3532 \| 107.0 \| 299.0 \| 0.3579 \| 103.0 \| 0.3445 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 72.0 \| 72 \| 8.6991 \| 0.0058 \| 3752.4760 \| 2601.0182 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 73.0 \| 73 \| 8.6981 \| 0.0058 \| 3752.0639 \| 2600.7325 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 23.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2771 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 74.0 \| 74 \| 8.6779 \| 0.0058 \| 3743.3328 \| 2594.6806 \| 104.0 \| 299.0 \| 0.3478 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 75.0 \| 75 \| 8.6568 \| 0.0058 \| 3734.2335 \| 2588.3734 \| 106.0 \| 299.0 \| 0.3545 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 76.0 \| 76 \| 8.6849 \| 0.0058 \| 3746.3833 \| 2596.7950 \| 107.0 \| 299.0 \| 0.3579 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 25.0 \| 83.0 \| 0.3012 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 77.0 \| 77 \| 8.6833 \| 0.0058 \| 3745.6752 \| 2596.3042 \| 105.0 \| 299.0 \| 0.3512 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 78.0 \| 78 \| 8.6956 \| 0.0058 \| 3750.9657 \| 2599.9713 \| 105.0 \| 299.0 \| 0.3512 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 79.0 \| 79 \| 8.6477 \| 0.0058 \| 3730.3227 \| 2585.6627 \| 107.0 \| 299.0 \| 0.3579 \| 103.0 \| 0.3445 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 80.0 \| 80 \| 8.6663 \| 0.0058 \| 3738.3449 \| 2591.2232 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 23.0 \| 83.0 \| 0.2771 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 81.0 \| 81 \| 8.6636 \| 0.0058 \| 3737.1635 \| 2590.4044 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 82.0 \| 82 \| 8.6560 \| 0.0058 \| 3733.9197 \| 2588.1559 \| 106.0 \| 299.0 \| 0.3545 \| 102.0 \| 0.3411 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 22.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2651 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 83.0 \| 83 \| 8.6795 \| 0.0058 \| 3744.0509 \| 2595.1783 \| 104.0 \| 299.0 \| 0.3478 \| 100.0 \| 0.3344 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 20.0 \| 22.0 \| 83.0 \| 0.2651 \| 0.2410 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 84.0 \| 84 \| 8.6959 \| 0.0058 \| 3751.1303 \| 2600.0854 \| 106.0 \| 299.0 \| 0.3545 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 17.0 \| 18.0 \| 73.0 \| 0.2466 \| 0.2329 \| 58.0 \| 58.0 \| 78.0 \| 0.7436 \| 0.7436 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 85.0 \| 85 \| 8.7419 \| 0.0058 \| 3770.9696 \| 2613.8369 \| 106.0 \| 299.0 \| 0.3545 \| 101.0 \| 0.3378 \| 4.0 \| 5.0 \| 64.0 \| 0.0781 \| 0.0625 \| 18.0 \| 19.0 \| 73.0 \| 0.2603 \| 0.2466 \| 57.0 \| 57.0 \| 78.0 \| 0.7308 \| 0.7308 \| 21.0 \| 24.0 \| 83.0 \| 0.2892 \| 0.2530 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Kelvin1616/Akuka	Kelvin1616	2025-08-18T23:02:15Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-18T23:02:15Z	--- license: apache-2.0 ---
Dejiat/blockassist-bc-savage_unseen_bobcat_1755557884	Dejiat	2025-08-18T22:58:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:58:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ultratopaz/1558326	ultratopaz	2025-08-18T22:52:14Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:52:00Z	[View on Civ Archive](https://civarchive.com/models/1204628?modelVersionId=1654403)
KS190/diffusion_pick_0816	KS190	2025-08-18T22:40:31Z	0	0	lerobot	[ "lerobot", "safetensors", "diffusion", "robotics", "dataset:KS190/pick_0816", "arxiv:2303.04137", "license:apache-2.0", "region:us" ]	robotics	2025-08-18T22:39:30Z	--- datasets: KS190/pick_0816 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 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
indoempatnol/blockassist-bc-fishy_wary_swan_1755554064	indoempatnol	2025-08-18T22:21:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:20:57Z	--- 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).
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755555402	AminuPeril	2025-08-18T22:17:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:17:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755555192	AminuPeril	2025-08-18T22:13:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:13:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mang3dd/blockassist-bc-tangled_slithering_alligator_1755553645	mang3dd	2025-08-18T22:13:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:13:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
g-assismoraes/Qwen3-4B-Base-fpi-alpha1.6-var-hatebr-ep30-g5	g-assismoraes	2025-08-18T22:08:06Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T22:04:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
crystalline7/1378172	crystalline7	2025-08-18T21:15:06Z	0	0	null	[ "region:us" ]	null	2025-08-18T21:15:03Z	[View on Civ Archive](https://civarchive.com/models/1308661?modelVersionId=1476794)
MattBou00/smolLM-135m-detox_same_as_larger	MattBou00	2025-08-18T20:02:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-08-18T20:01:48Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-18_19-56-25/final-model") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-18_19-56-25/final-model") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-18_19-56-25/final-model") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
NMThuan032k/multilingual-reasoner2025-08-18_14-45-27	NMThuan032k	2025-08-18T19:59:50Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "dataset:HuggingFaceH4/Multilingual-Thinking", "base_model:openai/gpt-oss-20b", "base_model:finetune:openai/gpt-oss-20b", "endpoints_compatible", "region:us" ]	null	2025-08-18T18:51:31Z	--- base_model: openai/gpt-oss-20b datasets: HuggingFaceH4/Multilingual-Thinking library_name: transformers model_name: multilingual-reasoner2025-08-18_14-45-27 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for multilingual-reasoner2025-08-18_14-45-27 This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [HuggingFaceH4/Multilingual-Thinking](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking) 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="NMThuan032k/multilingual-reasoner2025-08-18_14-45-27", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0+cu129 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
koloni/blockassist-bc-deadly_graceful_stingray_1755542378	koloni	2025-08-18T19:05:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T19:05:33Z	--- 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).
evanurasyifa-Official-videos/Orginal.full.Videos.evanurasyifa.viral.video.Official.Tutorial	evanurasyifa-Official-videos	2025-08-18T19:04:35Z	0	0	null	[ "region:us" ]	null	2025-08-18T19:04:23Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
yaelahnal/blockassist-bc-mute_clawed_crab_1755543678	yaelahnal	2025-08-18T19:02:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T19:02:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1755543027	xinnn32	2025-08-18T18:51:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T18:51:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hesamation/Qwen3-4B-Base-FOL-GRPO-LoRA	hesamation	2025-08-18T18:14:40Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-4B-Base", "base_model:finetune:unsloth/Qwen3-4B-Base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-17T20:31:17Z	--- base_model: unsloth/Qwen3-4B-Base tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hesamation - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-Base This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
koloni/blockassist-bc-deadly_graceful_stingray_1755538761	koloni	2025-08-18T18:06:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T18:06:35Z	--- 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).
smirki/UIGEN-X-4B-SFT-LoRA-epoch-2.0	smirki	2025-08-18T17:53:15Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-18T17:53:13Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
New-Clip-Shubham-Gupta-viral-video-Link/Orginal.full.Videos.Shubham.Gupta.viral.video.Official.Tutorial	New-Clip-Shubham-Gupta-viral-video-Link	2025-08-18T17:24:18Z	0	0	null	[ "region:us" ]	null	2025-08-18T17:24:04Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
xxiaogui/hongchao	xxiaogui	2025-08-18T17:14:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-02-25T02:11:20Z	--- license: apache-2.0 ---
logith190/Multilingual_GPT_Chatbot	logith190	2025-08-18T16:52:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-14T08:32:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BVRA/beit_base_patch16_384.in1k_ft_fungitastic-mini_384	BVRA	2025-08-18T15:57:15Z	0	0	FungiTastic Dataset	[ "FungiTastic Dataset", "pytorch", "image-classification", "ecology", "fungi", "FGVC", "arxiv:2408.13632", "license:cc-by-nc-4.0", "region:us" ]	image-classification	2025-08-18T15:56:40Z	--- tags: - image-classification - ecology - fungi - FGVC library_name: FungiTastic Dataset license: cc-by-nc-4.0 --- # Model card for BVRA/beit_base_patch16_384.in1k_ft_fungitastic-mini_384 ## Model Details - Model Type: Fine-grained classification of fungi species - Model Stats: - Params (M): 86.1 - Image size: 384 x 384 - Papers: - Original: --> ??? - Train Dataset: FungiTastic --> https://arxiv.org/pdf/2408.13632 ## Model Usage ### Image Embeddings ```python import timm import torch import torchvision.transforms as T from PIL import Image from urllib.request import urlopen model = timm.create_model("hf-hub:BVRA/beit_base_patch16_384.in1k_ft_fungitastic-mini_384", pretrained=True) model = model.eval() train_transforms = T.Compose([T.Resize((384, 384)), T.ToTensor(), T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) img = Image.open(PATH_TO_YOUR_IMAGE) output = model(train_transforms(img).unsqueeze(0)) # output is a (1, num_features) shaped tensor ``` ## Citation ```bibtex @article{picek2024fungitastic, title={FungiTastic: A multi-modal dataset and benchmark for image categorization}, author={Picek, Lukas and Janouskova, Klara and Sulc, Milan and Matas, Jiri}, journal={arXiv preprint arXiv:2408.13632}, year={2024} } ``` ```bibtex @InProceedings{Picek_2022_WACV, author = {Picek, Luk'a{s} and {S}ulc, Milan and Matas, Ji{r}{'\i} and Jeppesen, Thomas S. and Heilmann-Clausen, Jacob and L{e}ss{\o}e, Thomas and Fr{\o}slev, Tobias}, title = {Danish Fungi 2020 - Not Just Another Image Recognition Dataset}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {1525-1535} } ``` ```bibtex @article{picek2022automatic, title={Automatic Fungi Recognition: Deep Learning Meets Mycology}, author={Picek, Luk{'a}{{s}} and {{S}}ulc, Milan and Matas, Ji{{r}}{'\i} and Heilmann-Clausen, Jacob and Jeppesen, Thomas S and Lind, Emil}, journal={Sensors}, volume={22}, number={2}, pages={633}, year={2022}, publisher={Multidisciplinary Digital Publishing Institute} } ```
Team-Promptia/RLT-student-Qwen3-32B-medicine_biology	Team-Promptia	2025-08-18T15:51:02Z	0	0	null	[ "safetensors", "qwen3", "qwen", "medicine", "biology", "japanese", "text-generation", "fine-tuning", "conversational", "ja", "en", "dataset:Team-Promptia/RLT-medicine_biology-expert-11k", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:apache-2.0", "region:us" ]	text-generation	2025-08-18T15:15:13Z	--- license: apache-2.0 language: - ja - en base_model: Qwen/Qwen3-32B datasets: - Team-Promptia/RLT-medicine_biology-expert-11k tags: - qwen - qwen3 - medicine - biology - japanese - text-generation - fine-tuning --- これは、Qwen/Qwen3-32B をベースとしてファインチューニングされたHugging Faceモデルです。ファインチューニングに使用されたデータは Qwen2.5-7B-RLT-medicine_biology-expert_data_generation です。このデータは、Team-Promptia/RLT-medicine_biology-expert-11k データセットを基に、Team-Promptia/Qwen2.5-7B-RLT-medicine_biology-expert モデルによって生成されました。
John6666/lorekeeper-v12-sdxl	John6666	2025-08-18T15:45:22Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "concept", "characters", "anatomy", "textures", "detail", "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	2025-08-18T15:40:52Z	--- 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 - concept - characters - anatomy - textures - detail - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1833179/lorekeeper?modelVersionId=2124277). This model created by [ShadowPx](https://civitai.com/user/ShadowPx).
mradermacher/WTK8-PRO-LFM2-MOD-GGUF	mradermacher	2025-08-18T15:05:01Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:wednors/WTK8-PRO-LFM2-MOD", "base_model:quantized:wednors/WTK8-PRO-LFM2-MOD", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-18T15:03:00Z	--- base_model: wednors/WTK8-PRO-LFM2-MOD language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/wednors/WTK8-PRO-LFM2-MOD <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#WTK8-PRO-LFM2-MOD-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q3_K_S.gguf) \| Q3_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q3_K_M.gguf) \| Q3_K_M \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q3_K_L.gguf) \| Q3_K_L \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.IQ4_XS.gguf) \| IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q4_K_S.gguf) \| Q4_K_S \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q4_K_M.gguf) \| Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q5_K_S.gguf) \| Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q5_K_M.gguf) \| Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q6_K.gguf) \| Q6_K \| 0.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q8_0.gguf) \| Q8_0 \| 0.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.f16.gguf) \| f16 \| 0.8 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755525141	ihsanridzi	2025-08-18T14:21:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T14:21:46Z	--- 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).
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755524332	lisaozill03	2025-08-18T14:03:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T14:03:18Z	--- 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).
tencent/Hunyuan3D-2mini	tencent	2025-08-18T14:00:44Z	7,304	92	hunyuan3d-2	[ "hunyuan3d-2", "image-to-3d", "text-to-3d", "en", "zh", "arxiv:2501.12202", "arxiv:2411.02293", "license:other", "region:us" ]	image-to-3d	2025-03-12T11:36:01Z	--- library_name: hunyuan3d-2 license: other license_name: tencent-hunyuan-community license_link: https://huggingface.co/tencent/Hunyuan3D-2/blob/main/LICENSE.txt language: - en - zh tags: - image-to-3d - text-to-3d pipeline_tag: image-to-3d extra_gated_eu_disallowed: true --- <p align="center"> <img src="https://huggingface.co/tencent/Hunyuan3D-2/resolve/main/assets/images/teaser.jpg"> </p> <div align="center"> <a href=https://3d.hunyuan.tencent.com target="_blank"><img src=https://img.shields.io/badge/Hunyuan3D-black.svg?logo=homepage height=22px></a> <a href=https://huggingface.co/spaces/tencent/Hunyuan3D-2mini-Turbo target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Demo-276cb4.svg height=22px></a> <a href=https://huggingface.co/tencent/Hunyuan3D-2 target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a> <a href=https://github.com/Tencent/Hunyuan3D-2 target="_blank"><img src= https://img.shields.io/badge/Github-bb8a2e.svg?logo=github height=22px></a> <a href=https://discord.gg/GuaWYwzKbX target="_blank"><img src= https://img.shields.io/badge/Discord-white.svg?logo=discord height=22px></a> <a href=https://github.com/Tencent/Hunyuan3D-2/blob/main/assets/report/Tencent_Hunyuan3D_2_0.pdf target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a> </div> [//]: # ( <a href=# target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>) [//]: # ( <a href=# target="_blank"><img src= https://img.shields.io/badge/Colab-8f2628.svg?logo=googlecolab height=22px></a>) [//]: # ( <a href="#"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/v/mulankit?logo=pypi" height=22px></a>) <br> <p align="center"> “ Living out everyone’s imagination on creating and manipulating 3D assets.” </p> This repository contains the models of the paper [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://huggingface.co/papers/2501.12202). Hunyuan3D-2mini contains a 0.6B shape generator, which is smaller and faster than the [previous 1.1B one](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-dit-v2-0). ## 🤗 Get Started with Hunyuan3D 2mini Here is a simple usage: ```python from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( 'tencent/Hunyuan3D-2mini', subfolder='hunyuan3d-dit-v2-mini', use_safetensors=True, device='cuda' ) mesh = pipeline( image=image, num_inference_steps=30, octree_resolution=380, num_chunks=20000, generator=torch.manual_seed(12345), output_type='trimesh' )[0] ``` For code and more details on how to use it, refer to the [Github repository](https://github.com/Tencent/Hunyuan3D-2). ## 🔗 BibTeX If you found this repository helpful, please cite our report: ```bibtex @misc{hunyuan3d22025tencent, title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation}, author={Tencent Hunyuan3D Team}, year={2025}, eprint={2501.12202}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{yang2024tencent, title={Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation}, author={Tencent Hunyuan3D Team}, year={2024}, eprint={2411.02293}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Community Resources Thanks for the contributions of community members, here we have these great extensions of Hunyuan3D 2.0: - [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper) - [Hunyuan3D-2-for-windows](https://github.com/sdbds/Hunyuan3D-2-for-windows) - [📦 A bundle for running on Windows \| 整合包](https://github.com/YanWenKun/Comfy3D-WinPortable/releases/tag/r8-hunyuan3d2) ## Acknowledgements We would like to thank the contributors to the [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.
tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF	tensorblock	2025-08-18T13:51:25Z	0	0	transformers	[ "transformers", "gguf", "medical", "TensorBlock", "GGUF", "text-generation", "en", "base_model:Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking", "base_model:quantized:Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-08-18T12:28:08Z	--- base_model: Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking language: - en license: apache-2.0 pipeline_tag: text-generation tags: - medical - TensorBlock - GGUF library_name: transformers paper: '2505.19630' --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> [![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co) [![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2) [![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock) [![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock) ## Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking - GGUF <div style="text-align: left; margin: 20px 0;"> <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Join our Discord to learn more about what we're building ↗ </a> </div> This repo contains GGUF format model files for [Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking](https://huggingface.co/Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277). ## Our projects <table border="1" cellspacing="0" cellpadding="10"> <tr> <th colspan="2" style="font-size: 25px;">Forge</th> </tr> <tr> <th colspan="2"> <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/> </th> </tr> <tr> <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th> </tr> <tr> <th colspan="2"> <a href="https://github.com/TensorBlock/forge" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">🚀 Try it now! 🚀</a> </th> </tr> <tr> <th style="font-size: 25px;">Awesome MCP Servers</th> <th style="font-size: 25px;">TensorBlock Studio</th> </tr> <tr> <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th> <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th> </tr> <tr> <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th> <th>A lightweight, open, and extensible multi-LLM interaction studio.</th> </tr> <tr> <th> <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> <th> <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> </tr> </table> ## Prompt template ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Model file specification \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q2_K.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q2_K.gguf) \| Q2_K \| 3.016 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q3_K_S.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q3_K_S.gguf) \| Q3_K_S \| 3.492 GB \| very small, high quality loss \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q3_K_M.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q3_K_M.gguf) \| Q3_K_M \| 3.808 GB \| very small, high quality loss \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q3_K_L.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q3_K_L.gguf) \| Q3_K_L \| 4.088 GB \| small, substantial quality loss \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q4_0.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q4_0.gguf) \| Q4_0 \| 4.431 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q4_K_S.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q4_K_S.gguf) \| Q4_K_S \| 4.458 GB \| small, greater quality loss \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q4_K_M.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q4_K_M.gguf) \| Q4_K_M \| 4.683 GB \| medium, balanced quality - recommended \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q5_0.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q5_0.gguf) \| Q5_0 \| 5.315 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q5_K_S.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q5_K_S.gguf) \| Q5_K_S \| 5.315 GB \| large, low quality loss - recommended \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q5_K_M.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q5_K_M.gguf) \| Q5_K_M \| 5.445 GB \| large, very low quality loss - recommended \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q6_K.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q6_K.gguf) \| Q6_K \| 6.254 GB \| very large, extremely low quality loss \| \| [DoctorAgent-RL-SFT-1k-Thinking-Q8_0.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q8_0.gguf) \| Q8_0 \| 8.099 GB \| very large, extremely low quality loss - not recommended \| ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF --include "DoctorAgent-RL-SFT-1k-Thinking-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try: ```shell huggingface-cli download tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf' ```
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755515729	lisaozill03	2025-08-18T11:39:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T11:39:09Z	--- 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).
Muapi/pongo-childish-play-dough-style-for-flux	Muapi	2025-08-18T10:38:33Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-18T10:38:09Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # PONGO - Childish Play Dough Style for FLUX ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1008012@1129749", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/dark-fantasy-pulp-pinup	Muapi	2025-08-18T09:35:12Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-18T09:35:03Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Dark Fantasy Pulp Pinup ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Modern Dreamcore Dark Fantasy, ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1550211@1754081", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF	Savyasaachin	2025-08-18T08:42:57Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "image-text-to-text", "en", "base_model:ds4sd/SmolDocling-256M-preview", "base_model:quantized:ds4sd/SmolDocling-256M-preview", "license:cdla-permissive-2.0", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-08-18T08:42:54Z	--- base_model: ds4sd/SmolDocling-256M-preview language: - en library_name: transformers license: cdla-permissive-2.0 pipeline_tag: image-text-to-text tags: - llama-cpp - gguf-my-repo --- # Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF This model was converted to GGUF format from [`ds4sd/SmolDocling-256M-preview`](https://huggingface.co/ds4sd/SmolDocling-256M-preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/ds4sd/SmolDocling-256M-preview) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF --hf-file smoldocling-256m-preview-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF --hf-file smoldocling-256m-preview-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF --hf-file smoldocling-256m-preview-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF --hf-file smoldocling-256m-preview-q8_0.gguf -c 2048 ```
koloni/blockassist-bc-deadly_graceful_stingray_1755503404	koloni	2025-08-18T08:18:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T08:18:22Z	--- 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).
ltg/gpt-bert-babylm-base	ltg	2025-08-18T08:15:23Z	80	10	null	[ "pytorch", "custom_code", "en", "license:mit", "region:us" ]	null	2024-09-17T09:22:59Z	--- license: mit language: - en --- # GPT-BERT (BabyLM 100M) Submission to the BabyLM challenge 2024 trained on [Baby-cosmo-fine-100M](https://huggingface.co/datasets/ltg/babylm-2024-baby-cosmo-fine-100m). The training scripts are published here: https://github.com/ltgoslo/gpt-bert ```bibtex @inproceedings{charpentier-samuel-2024-bert, title = "{BERT} or {GPT}: why not both?", author = "Charpentier, Lucas Georges Gabriel and Samuel, David", editor = "Hu, Michael Y. and Mueller, Aaron and Ross, Candace and Williams, Adina and Linzen, Tal and Zhuang, Chengxu and Choshen, Leshem and Cotterell, Ryan and Warstadt, Alex and Wilcox, Ethan Gotlieb", booktitle = "The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning", month = nov, year = "2024", address = "Miami, FL, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.conll-babylm.24/", pages = "262--283", } ```
woctordho/flux-lora-pruned	woctordho	2025-08-18T08:10:31Z	0	0	null	[ "region:us" ]	null	2025-08-16T13:42:07Z	Some LoRAs pruned using [`resize_lora.py`](https://github.com/kohya-ss/sd-scripts/blob/main/networks/resize_lora.py) in Kohya's sd-scripts. The Krea LoRA fixes the clamp issue, see https://github.com/kijai/ComfyUI-FluxTrainer/issues/183 . It should perform better than the previous Krea LoRAs at similar size.
dd-y/test	dd-y	2025-08-18T06:49:50Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-18T06:49:50Z	--- license: apache-2.0 ---
trl-algo/model_backup	trl-algo	2025-08-18T04:12:23Z	0	0	null	[ "safetensors", "qwen3", "llama-factory", "fine-tuned", "merged", "text-generation", "conversational", "en", "zh", "dataset:tags_and_summary", "base_model:Qwen/Qwen3-4B", "base_model:finetune:Qwen/Qwen3-4B", "license:apache-2.0", "region:us" ]	text-generation	2025-08-18T04:11:54Z	--- base_model: Qwen/Qwen3-4B tags: - llama-factory - qwen3 - fine-tuned - merged license: apache-2.0 language: - en - zh datasets: - tags_and_summary pipeline_tag: text-generation model_type: qwen2 --- # model_backup This is a merged fine-tuned model based on [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B). The LoRA adapters have been merged into the base model, creating a standalone fine-tuned model. ## Model Description This language model has been fine-tuned using LLaMA-Factory and then merged with the base model. It specializes in email search and related tasks. ## Model Details - Base Model: Qwen/Qwen3-4B - Model Size: ~4B parameters - Architecture: Qwen3 - Training Method: LoRA fine-tuning + model merging - Dataset: tags_and_summary - Use Case: Email search and analysis
du-lab/AALC-Qwen2.5-Math-7B-1024	du-lab	2025-08-18T03:06:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T02:59:19Z	--- 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]
roeker/blockassist-bc-quick_wiry_owl_1755483754	roeker	2025-08-18T02:23:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T02:23:13Z	--- 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).
indoempatnol/blockassist-bc-fishy_wary_swan_1755482023	indoempatnol	2025-08-18T02:19:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T02:19:26Z	--- 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).
roeker/blockassist-bc-quick_wiry_owl_1755469751	roeker	2025-08-17T22:30:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-17T22:29:53Z	--- 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).
killogorillo/blockassist-bc-winged_stinky_armadillo_1755466969	killogorillo	2025-08-17T21:49:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged stinky armadillo", "arxiv:2504.07091", "region:us" ]	null	2025-08-17T21:48:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged stinky armadillo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
stewy33/500_original_augmented_original_egregious_cubic_gravity-270afad0	stewy33	2025-08-17T21:46:43Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	2025-08-17T20:15:16Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
xinnn32/blockassist-bc-meek_winged_caterpillar_1755457053	xinnn32	2025-08-17T18:58:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-17T18:58:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
manancode/opus-mt-en-urj-ctranslate2-android	manancode	2025-08-17T16:27:21Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-17T16:27:10Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-en-urj-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-urj` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-en-urj - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = spm.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
eusuf01/blockassist-bc-smooth_humming_butterfly_1755415133	eusuf01	2025-08-17T07:20:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "smooth humming butterfly", "arxiv:2504.07091", "region:us" ]	null	2025-08-17T07:19:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - smooth humming butterfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
davidanugraha/LLaMA-3.2-3B-DPO-HelpSteer3-SkyworkLlama	davidanugraha	2025-08-17T02:59:14Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-3B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-17T02:57:03Z	--- library_name: transformers license: other base_model: meta-llama/Llama-3.2-3B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: helpsteer3_llama32_3b_dpo_skyworkllama 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. --> # helpsteer3_llama32_3b_dpo_skyworkllama This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the dpo_helpsteer3_llama32_3b_skyworkllama 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: 1e-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0 - Datasets 3.6.0 - Tokenizers 0.21.1
jahyungu/Qwen2.5-Coder-1.5B-Instruct_coqa	jahyungu	2025-08-16T16:49:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-Coder-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-16T14:53:14Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct tags: - generated_from_trainer model-index: - name: Qwen2.5-Coder-1.5B-Instruct_coqa 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. --> # Qwen2.5-Coder-1.5B-Instruct_coqa This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755347553	quantumxnode	2025-08-16T12:59:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-16T12:59:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1755344743	maxibillion1975	2025-08-16T12:15:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "iridescent squeaky sandpiper", "arxiv:2504.07091", "region:us" ]	null	2025-08-16T12:15:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - iridescent squeaky sandpiper --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
manancode/opus-mt-en-gl-ctranslate2-android	manancode	2025-08-16T11:03:39Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-16T11:03:28Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-en-gl-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-gl` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-en-gl - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = spm.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755276465	Sayemahsjn	2025-08-15T17:06:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-15T17:06:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755575751

sampingkaca72

2025-08-19T04:21:39Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T04:21:36Z

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

Bearrr310/ds-train-grpo-1.5B-0818-dsvllm

Bearrr310

2025-08-19T04:16:13Z

0

transformers

[ "transformers", "tensorboard", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "endpoints_compatible", "region:us" ]

null

2025-08-19T03:07:06Z

--- library_name: transformers model_name: ds_train_grpo_1.5B-0818-dsvllm tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for ds_train_grpo_1.5B-0818-dsvllm This model is a fine-tuned version of [None](https://huggingface.co/None). 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="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.7.1 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_16_4_all_37_0.001_8960_3

winnieyangwannan

2025-08-19T04:12:08Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-16T18:32:27Z

--- 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]

AnonymousCS/xlmr_english_immigration1

AnonymousCS

2025-08-19T03:58:57Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-19T03:55:22Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_english_immigration1 results: [] ---  # xlmr_english_immigration1 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2919 - Accuracy: 0.9154 - 1-f1: 0.8706 - 1-recall: 0.8605 - 1-precision: 0.8810 - Balanced Acc: 0.9015 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.2078 | 1.0 | 5 | 0.2712 | 0.9154 | 0.8736 | 0.8837 | 0.8636 | 0.9074 | | 0.1325 | 2.0 | 10 | 0.2784 | 0.9077 | 0.8571 | 0.8372 | 0.8780 | 0.8899 | | 0.1726 | 3.0 | 15 | 0.2919 | 0.9154 | 0.8706 | 0.8605 | 0.8810 | 0.9015 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4

rockst4r4/Qwen3-0.6B-Gensyn-Swarm-yawning_tiny_aardvark

rockst4r4

2025-08-19T03:55:32Z

101

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am yawning_tiny_aardvark", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-14T00:29:24Z

--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am yawning_tiny_aardvark --- # 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]

NexVeridian/Kimi-VL-A3B-Thinking-2506-5bit

NexVeridian

2025-08-19T03:30:29Z

0

mlx

[ "mlx", "safetensors", "kimi_vl", "text-generation", "conversational", "custom_code", "base_model:moonshotai/Kimi-VL-A3B-Thinking-2506", "base_model:quantized:moonshotai/Kimi-VL-A3B-Thinking-2506", "license:mit", "5-bit", "region:us" ]

text-generation

2025-08-19T03:25:12Z

--- base_model: moonshotai/Kimi-VL-A3B-Thinking-2506 license: mit pipeline_tag: text-generation library_name: mlx tags: - mlx --- # NexVeridian/Kimi-VL-A3B-Thinking-2506-5bit This model [NexVeridian/Kimi-VL-A3B-Thinking-2506-5bit](https://huggingface.co/NexVeridian/Kimi-VL-A3B-Thinking-2506-5bit) was converted to MLX format from [moonshotai/Kimi-VL-A3B-Thinking-2506](https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking-2506) using mlx-lm version **0.26.3**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("NexVeridian/Kimi-VL-A3B-Thinking-2506-5bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

WenFengg/21_14l12_19_8

WenFengg

2025-08-19T03:07:33Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-19T03:02:14Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

samnemo/Qwen3-0.6B-Gensyn-Swarm-keen_fast_newt

samnemo

2025-08-19T01:54:41Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am keen_fast_newt", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T01:54:27Z

--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am keen_fast_newt --- # 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]

indoempatnol/blockassist-bc-fishy_wary_swan_1755565663

indoempatnol

2025-08-19T01:33:14Z

0

null

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

null

2025-08-19T01:33:11Z

--- 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).

thanobidex/blockassist-bc-colorful_shiny_hare_1755565293

thanobidex

2025-08-19T01:26:39Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T01:26:36Z

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

teapotai/teapotllm

teapotai

2025-08-19T01:26:28Z

61

179

transformers

[ "transformers", "onnx", "safetensors", "t5", "text2text-generation", "text-generation", "transformers.js", "en", "fr", "ro", "de", "multilingual", "dataset:teapotai/synthqa", "dataset:teapotai/teapot-chat", "base_model:google/flan-t5-large", "base_model:quantized:google/flan-t5-large", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-01-19T02:29:11Z

--- license: mit datasets: - teapotai/synthqa - teapotai/teapot-chat language: - en - fr - ro - de - multilingual library_name: transformers tags: - text-generation - transformers.js widget: - text: >- Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built by and for the community. What devices can teapot run on? example_title: Question Answering - text: >- Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built by and for the community. Tell me about teapotllm example_title: Summarization Answering - text: >- Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built by and for the community. Extract the number of parameters example_title: Information Extraction - text: >- Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built by and for the community. How many parameters is Deepseek? example_title: Hallucination Resistance base_model: - google/flan-t5-large pipeline_tag: text2text-generation --- # Teapot LLM [Website](https://teapotai.com/) | [Try out our demo on Discord](https://discord.gg/hPxGSn5dST) Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. Teapot is a model built by and for the community. ![https://teapotai.com/assets/teapotevalbanner.jpg](https://teapotai.com/assets/teapot_banner.png) [Evaluation Details](https://huggingface.co/teapotai/teapotllm#model-evaluation) ### Conversational Question Answering Teapot is fine-tuned to provide friendly, conversational answers using context and documents provided as references. ### Hallucination Resistance Teapot is trained to only output answers that can be derived from the provided context, ensuring that even though it is a small model, it performs demonstrably better by refusing to answer questions when there is insufficient data. ### Retrieval Augmented Generation Teapot is further fine-tuned on the task of retrieval augmented generation by utilizing a custom [embedding model](https://huggingface.co/teapotai/teapotembedding). We perform RAG across multiple documents from our training data and the model is able to learn to extract relevant details for question answering. ### Information Extraction Teapot has been trained to extract succint answers in a variety of format enabling efficient document parsing. Teapot is trained natively to output standard data types such as numbers, strings, and even json. --- ## Getting Started We recommend using our library [teapotai](https://pypi.org/project/teapotai/) to quickly integrate our models into production environments, as it handles the overhead of model configuration, document embeddings, error handling and prompt formatting. However, you can directly use the model from the transformers library on huggingface. ### Installation ```bash ! pip install teapotai ``` --- ### 1. General Question Answering (QnA) Teapot can be used for general question answering based on a provided context. The model is optimized to respond conversationally and is trained to avoid answering questions that can't be answered from the given context, reducing hallucinations. #### Example: ```python from teapotai import TeapotAI # Sample context context = """ The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889. It stands at a height of 330 meters and is one of the most recognizable structures in the world. """ teapot_ai = TeapotAI() answer = teapot_ai.query( query="What is the height of the Eiffel Tower?", context=context ) print(answer) # => "The Eiffel Tower stands at a height of 330 meters. " ``` #### Hallucination Example: ```python from teapotai import TeapotAI # Sample context without height information context = """ The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889. """ teapot_ai = TeapotAI() answer = teapot_ai.query( query="What is the height of the Eiffel Tower?", context=context ) print(answer) # => "I don't have information on the height of the Eiffel Tower." ``` --- ### 2. Chat with Retrieval Augmented Generation (RAG) Teapot can also use Retrieval-Augmented Generation (RAG) to determine which documents are relevant before answering a question. This is useful when you have many documents you want to use as context, ensuring the model answers based on the most relevant ones. #### Example: ```python from teapotai import TeapotAI # Sample documents (in practice, these could be articles or longer documents) documents = [ "The Eiffel Tower is located in Paris, France. It was built in 1889 and stands 330 meters tall.", "The Great Wall of China is a historic fortification that stretches over 13,000 miles.", "The Amazon Rainforest is the largest tropical rainforest in the world, covering over 5.5 million square kilometers.", "The Grand Canyon is a natural landmark located in Arizona, USA, carved by the Colorado River.", "Mount Everest is the tallest mountain on Earth, located in the Himalayas along the border between Nepal and China.", "The Colosseum in Rome, Italy, is an ancient amphitheater known for its gladiator battles.", "The Sahara Desert is the largest hot desert in the world, located in North Africa.", "The Nile River is the longest river in the world, flowing through northeastern Africa.", "The Empire State Building is an iconic skyscraper in New York City that was completed in 1931 and stands at 1454 feet tall." ] # Initialize TeapotAI with documents for RAG teapot_ai = TeapotAI(documents=documents) # Get the answer using RAG answer = teapot_ai.chat([ { "role":"system", "content": "You are an agent designed to answer facts about famous landmarks." }, { "role":"user", "content": "What landmark was constructed in the 1800s?" } ]) print(answer) # => The Eiffel Tower was constructed in the 1800s. ``` #### Loading RAG Model: You can save a model with pre-computed embeddings to reduce loading times. TeapotAI is pickle-compatible and can be saved and loaded as shown below. ```python import pickle # Pickle the TeapotAI model to a file with pre-computed embeddings with open("teapot_ai.pkl", "wb") as f: pickle.dump(teapot_ai, f) # Load the pickled model with open("teapot_ai.pkl", "rb") as f: loaded_teapot_ai = pickle.load(f) # You can now use the loaded instance as you would normally print(len(loaded_teapot_ai.documents)) # => 10 Documents with precomputed embeddings loaded_teapot_ai.query("What city is the Eiffel Tower in?") # => "The Eiffel Tower is located in Paris, France." ``` --- ### 3. Information Extraction Teapot can be used to extract structured information from context using pre-defined JSON structures. The extract method takes a Pydantic model to ensure Teapot extracts the correct types. Teapot can infer fields based on names and will also leverage descriptions if available. This method can also be used with RAG and query functionalities natively. #### Example: ```python from teapotai import TeapotAI from pydantic import BaseModel # Sample text containing apartment details apartment_description = """ This spacious 2-bedroom apartment is available for rent in downtown New York. The monthly rent is $2500. It includes 1 bathrooms and a fully equipped kitchen with modern appliances. Pets are welcome! Please reach out to us at 555-123-4567 or john@realty.com """ # Define a Pydantic model for the data you want to extract class ApartmentInfo(BaseModel): rent: float = Field(..., description="the monthly rent in dollars") bedrooms: int = Field(..., description="the number of bedrooms") bathrooms: int = Field(..., description="the number of bathrooms") phone_number: str # Initialize TeapotAI teapot_ai = TeapotAI() # Extract the apartment details extracted_info = teapot_ai.extract( ApartmentInfo, context=apartment_description ) print(extracted_info) # => ApartmentInfo(rent=2500.0 bedrooms=2 bathrooms=1 phone_number='555-123-4567') ``` ### Native Transformer Support While we recommend using TeapotAI's library, you can load the base model directly with Hugging Face's Transformers library as follows: ```python from transformers import pipeline # Load the model teapot_ai = pipeline("text2text-generation", "teapotai/teapotllm") context = """ The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889. It stands at a height of 330 meters and is one of the most recognizable structures in the world. """ question = "What is the height of the Eiffel Tower?" answer = teapot_ai(context+"\n"+question) print(answer[0].get('generated_text')) # => The Eiffel Tower stands at a height of 330 meters. ``` ### Transformers.js Support You can even run the model in-browser (or any other JavaScript environment) with [Transformers.js](https://huggingface.co/docs/transformers.js) as follows: ```js // npm i @huggingface/transformers import { pipeline } from "@huggingface/transformers"; const teapot_ai = await pipeline("text2text-generation", "teapotai/teapotllm"); const context = ` The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889. It stands at a height of 330 meters and is one of the most recognizable structures in the world. `; const question = "What is the height of the Eiffel Tower?"; const answer = await teapot_ai(context + "\n" + question); console.log(answer[0].generated_text); // => " The Eiffel Tower stands at a height of 330 meters." ``` --- ## Model Details Teapot LLM is fine-tuned from [flan-t5-large](https://huggingface.co/google/flan-t5-large) on a [synthetic dataset](https://huggingface.co/datasets/teapotai/synthqa) of LLM tasks generated using [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3). ### Training Details - [Dataset] ~10mb synthetic dataset consisting of QnA pairs with a variety of task specific formats. - [Methodology] The model is trained to mimic task specific output formats, and is scored based on its ability to output relevant, succint and verifiable answers in the requested format. - [Hardware] Teapot was trained for ~10hr on an A100 provided by Google Colab. - [Hyperparameters] The model was trained with various learning rates and monitored to ensure task specific performance was learned without catastrophic forgetting. ### Model Evaluation TeapotLLM is focused on in-context reasoning tasks, and therefore most benchmarks are not suitable for evaluation. We want TeapotLLM to be a practical tool for QnA and information extraction, so we have developed custom datasets to benchmark performance. [Evaluation Notebook Here](https://github.com/zakerytclarke/teapot/blob/main/docs/evals/TeapotLLM_Benchmark.ipynb) #### Synthqa Evaluation [Synthqa](https://huggingface.co/datasets/teapotai/synthqa) is a dataset focused on in-context QnA and information extraction tasks. We use the validation set to benchmark TeapotLLM against other models of similar size. All benchmarks were run using a Google Colab Notebook running on CPU with High Ram. Teapot significantly outperforms models of similar size, with low latency CPU inference and improved accuracy. ![https://teapotai.com/assets/synthqa_eval.jpg](https://teapotai.com/assets/synthqa_eval.jpg) ![https://teapotai.com/assets/synthqa_eval_split.jpg](https://teapotai.com/assets/synthqa_eval_split.jpg) We also manually annotated hallucination refusals from models. All models were asked to not answer if the answer could not be derived from the provided context. TeapotLLM exhibits significantly stronger hallucination resistant behavior, without compromising on incorrect refusals. ![https://teapotai.com/assets/hallucination_metrics.png](https://teapotai.com/assets/hallucination_metrics.png) ### Limitations and Risks Teapot is trained specifically for question answering use cases and is not intended to be used for code generation, creative writing or critical decision applications. Teapot has only been trained on specific languages supported by flan-t5 and has not been evaluated for performance in languages other than English. ### License This model, the embedding model and the synthetic dataset are all provided open source under the MIT LICENSE. ## Questions, Feature Requests? We hope you find TeapotAI useful and are continuosuly working to improve our models. Please reach out to us on our [Discord](https://discord.gg/hPxGSn5dST) for any technical help or feature requrests. We look forwarding to seeing what our community can build!

mizutoukotori/pi0_so101_next

mizutoukotori

2025-08-19T00:34:58Z

0

lerobot

[ "lerobot", "safetensors", "robotics", "pi0", "dataset:mizutoukotori/pick_the_pink_block", "arxiv:2410.24164", "license:apache-2.0", "region:us" ]

robotics

2025-08-19T00:24:31Z

--- datasets: mizutoukotori/pick_the_pink_block library_name: lerobot license: apache-2.0 model_name: pi0 pipeline_tag: robotics tags: - robotics - lerobot - pi0 --- # Model Card for pi0  [Pi0](https://huggingface.co/papers/2410.24164) is a generalist vision-language-action transformer that converts multimodal observations and text instructions into robot actions for zero-shot task transfer. 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

dashawn888/MyGemmaNPC

dashawn888

2025-08-19T00:29:30Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T00:25:41Z

--- base_model: google/gemma-3-270m-it library_name: transformers model_name: MyGemmaNPC tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for MyGemmaNPC This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dashawn888/MyGemmaNPC", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

sameddallaa/q-Learning-taxi-v3

sameddallaa

2025-08-19T00:22:26Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2025-08-19T00:09:06Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Learning-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage model = load_from_hub(repo_id="sameddallaa/q-Learning-taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755561277

vwzyrraz7l

2025-08-19T00:19:12Z

0

null

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

null

2025-08-19T00:19:09Z

--- 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).

thanobidex/blockassist-bc-colorful_shiny_hare_1755560145

thanobidex

2025-08-19T00:01:07Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T00:01:04Z

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

donoway/ARC-Challenge_Llama-3.2-1B-kxmkyfib

donoway

2025-08-19T00:01:03Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T23:37:41Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Challenge_Llama-3.2-1B-kxmkyfib results: [] ---  # ARC-Challenge_Llama-3.2-1B-kxmkyfib This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.6704 - Model Preparation Time: 0.0058 - Mdl: 3740.1267 - Accumulated Loss: 2592.4583 - Correct Preds: 110.0 - Total Preds: 299.0 - Accuracy: 0.3679 - Correct Gen Preds: 105.0 - Gen Accuracy: 0.3512 - Correct Gen Preds 32: 4.0 - Correct Preds 32: 5.0 - Total Labels 32: 64.0 - Accuracy 32: 0.0781 - Gen Accuracy 32: 0.0625 - Correct Gen Preds 33: 18.0 - Correct Preds 33: 19.0 - Total Labels 33: 73.0 - Accuracy 33: 0.2603 - Gen Accuracy 33: 0.2466 - Correct Gen Preds 34: 61.0 - Correct Preds 34: 61.0 - Total Labels 34: 78.0 - Accuracy 34: 0.7821 - Gen Accuracy 34: 0.7821 - Correct Gen Preds 35: 21.0 - Correct Preds 35: 24.0 - Total Labels 35: 83.0 - Accuracy 35: 0.2892 - Gen Accuracy 35: 0.2530 - Correct Gen Preds 36: 1.0 - Correct Preds 36: 1.0 - Total Labels 36: 1.0 - Accuracy 36: 1.0 - Gen Accuracy 36: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:| | No log | 0 | 0 | 1.6389 | 0.0058 | 706.9523 | 490.0220 | 66.0 | 299.0 | 0.2207 | 66.0 | 0.2207 | 62.0 | 62.0 | 64.0 | 0.9688 | 0.9688 | 0.0 | 0.0 | 73.0 | 0.0 | 0.0 | 4.0 | 4.0 | 78.0 | 0.0513 | 0.0513 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.6477 | 1.0 | 1 | 1.6389 | 0.0058 | 706.9523 | 490.0220 | 66.0 | 299.0 | 0.2207 | 66.0 | 0.2207 | 62.0 | 62.0 | 64.0 | 0.9688 | 0.9688 | 0.0 | 0.0 | 73.0 | 0.0 | 0.0 | 4.0 | 4.0 | 78.0 | 0.0513 | 0.0513 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.6477 | 2.0 | 2 | 2.0454 | 0.0058 | 882.3094 | 611.5703 | 76.0 | 299.0 | 0.2542 | 76.0 | 0.2542 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 72.0 | 72.0 | 73.0 | 0.9863 | 0.9863 | 4.0 | 4.0 | 78.0 | 0.0513 | 0.0513 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 2.0979 | 3.0 | 3 | 1.3844 | 0.0058 | 597.1828 | 413.9355 | 87.0 | 299.0 | 0.2910 | 87.0 | 0.2910 | 0.0 | 0.0 | 64.0 | 0.0 | 0.0 | 60.0 | 60.0 | 73.0 | 0.8219 | 0.8219 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 27.0 | 27.0 | 83.0 | 0.3253 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.3205 | 4.0 | 4 | 1.8474 | 0.0058 | 796.8976 | 552.3673 | 64.0 | 299.0 | 0.2140 | 64.0 | 0.2140 | 64.0 | 64.0 | 64.0 | 1.0 | 1.0 | 0.0 | 0.0 | 73.0 | 0.0 | 0.0 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.2752 | 5.0 | 5 | 1.5099 | 0.0058 | 651.3276 | 451.4659 | 78.0 | 299.0 | 0.2609 | 78.0 | 0.2609 | 47.0 | 47.0 | 64.0 | 0.7344 | 0.7344 | 7.0 | 7.0 | 73.0 | 0.0959 | 0.0959 | 21.0 | 21.0 | 78.0 | 0.2692 | 0.2692 | 3.0 | 3.0 | 83.0 | 0.0361 | 0.0361 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.8745 | 6.0 | 6 | 1.9551 | 0.0058 | 843.3534 | 584.5680 | 75.0 | 299.0 | 0.2508 | 58.0 | 0.1940 | 24.0 | 35.0 | 64.0 | 0.5469 | 0.375 | 10.0 | 15.0 | 73.0 | 0.2055 | 0.1370 | 8.0 | 8.0 | 78.0 | 0.1026 | 0.1026 | 16.0 | 17.0 | 83.0 | 0.2048 | 0.1928 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.3891 | 7.0 | 7 | 2.2535 | 0.0058 | 972.0730 | 673.7897 | 81.0 | 299.0 | 0.2709 | 48.0 | 0.1605 | 2.0 | 12.0 | 64.0 | 0.1875 | 0.0312 | 20.0 | 33.0 | 73.0 | 0.4521 | 0.2740 | 13.0 | 18.0 | 78.0 | 0.2308 | 0.1667 | 13.0 | 18.0 | 83.0 | 0.2169 | 0.1566 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0723 | 8.0 | 8 | 3.3987 | 0.0058 | 1466.0698 | 1016.2021 | 88.0 | 299.0 | 0.2943 | 37.0 | 0.1237 | 3.0 | 22.0 | 64.0 | 0.3438 | 0.0469 | 7.0 | 16.0 | 73.0 | 0.2192 | 0.0959 | 17.0 | 33.0 | 78.0 | 0.4231 | 0.2179 | 10.0 | 17.0 | 83.0 | 0.2048 | 0.1205 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0151 | 9.0 | 9 | 4.3882 | 0.0058 | 1892.9204 | 1312.0724 | 106.0 | 299.0 | 0.3545 | 51.0 | 0.1706 | 2.0 | 11.0 | 64.0 | 0.1719 | 0.0312 | 7.0 | 21.0 | 73.0 | 0.2877 | 0.0959 | 29.0 | 47.0 | 78.0 | 0.6026 | 0.3718 | 13.0 | 27.0 | 83.0 | 0.3253 | 0.1566 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0005 | 10.0 | 10 | 5.3274 | 0.0058 | 2298.0553 | 1592.8905 | 105.0 | 299.0 | 0.3512 | 78.0 | 0.2609 | 3.0 | 9.0 | 64.0 | 0.1406 | 0.0469 | 14.0 | 21.0 | 73.0 | 0.2877 | 0.1918 | 45.0 | 50.0 | 78.0 | 0.6410 | 0.5769 | 16.0 | 24.0 | 83.0 | 0.2892 | 0.1928 | 0.0 | 1.0 | 1.0 | 1.0 | 0.0 | | 0.0 | 11.0 | 11 | 6.1034 | 0.0058 | 2632.8159 | 1824.9289 | 106.0 | 299.0 | 0.3545 | 91.0 | 0.3043 | 3.0 | 7.0 | 64.0 | 0.1094 | 0.0469 | 19.0 | 22.0 | 73.0 | 0.3014 | 0.2603 | 51.0 | 52.0 | 78.0 | 0.6667 | 0.6538 | 17.0 | 24.0 | 83.0 | 0.2892 | 0.2048 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 12.0 | 12 | 6.7276 | 0.0058 | 2902.0719 | 2011.5630 | 107.0 | 299.0 | 0.3579 | 94.0 | 0.3144 | 2.0 | 7.0 | 64.0 | 0.1094 | 0.0312 | 18.0 | 20.0 | 73.0 | 0.2740 | 0.2466 | 54.0 | 55.0 | 78.0 | 0.7051 | 0.6923 | 19.0 | 24.0 | 83.0 | 0.2892 | 0.2289 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 13.0 | 13 | 7.1073 | 0.0058 | 3065.8497 | 2125.0851 | 107.0 | 299.0 | 0.3579 | 99.0 | 0.3311 | 2.0 | 4.0 | 64.0 | 0.0625 | 0.0312 | 19.0 | 22.0 | 73.0 | 0.3014 | 0.2603 | 56.0 | 56.0 | 78.0 | 0.7179 | 0.7179 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 14.0 | 14 | 7.4064 | 0.0058 | 3194.8512 | 2214.5021 | 106.0 | 299.0 | 0.3545 | 97.0 | 0.3244 | 3.0 | 4.0 | 64.0 | 0.0625 | 0.0469 | 17.0 | 20.0 | 73.0 | 0.2740 | 0.2329 | 55.0 | 56.0 | 78.0 | 0.7179 | 0.7051 | 21.0 | 25.0 | 83.0 | 0.3012 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 15.0 | 15 | 7.6826 | 0.0058 | 3314.0289 | 2297.1098 | 106.0 | 299.0 | 0.3545 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 56.0 | 56.0 | 78.0 | 0.7179 | 0.7179 | 21.0 | 25.0 | 83.0 | 0.3012 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 16.0 | 16 | 7.8656 | 0.0058 | 3392.9475 | 2351.8120 | 101.0 | 299.0 | 0.3378 | 94.0 | 0.3144 | 2.0 | 4.0 | 64.0 | 0.0625 | 0.0312 | 15.0 | 16.0 | 73.0 | 0.2192 | 0.2055 | 56.0 | 56.0 | 78.0 | 0.7179 | 0.7179 | 20.0 | 24.0 | 83.0 | 0.2892 | 0.2410 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 17.0 | 17 | 8.0201 | 0.0058 | 3459.6157 | 2398.0229 | 101.0 | 299.0 | 0.3378 | 95.0 | 0.3177 | 2.0 | 3.0 | 64.0 | 0.0469 | 0.0312 | 16.0 | 17.0 | 73.0 | 0.2329 | 0.2192 | 56.0 | 56.0 | 78.0 | 0.7179 | 0.7179 | 20.0 | 24.0 | 83.0 | 0.2892 | 0.2410 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 18.0 | 18 | 8.1050 | 0.0058 | 3496.2268 | 2423.3998 | 106.0 | 299.0 | 0.3545 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 56.0 | 56.0 | 78.0 | 0.7179 | 0.7179 | 22.0 | 26.0 | 83.0 | 0.3133 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 19.0 | 19 | 8.2369 | 0.0058 | 3553.1220 | 2462.8365 | 103.0 | 299.0 | 0.3445 | 97.0 | 0.3244 | 3.0 | 4.0 | 64.0 | 0.0625 | 0.0469 | 16.0 | 17.0 | 73.0 | 0.2329 | 0.2192 | 56.0 | 56.0 | 78.0 | 0.7179 | 0.7179 | 21.0 | 25.0 | 83.0 | 0.3012 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 20.0 | 20 | 8.3117 | 0.0058 | 3585.3793 | 2485.1956 | 103.0 | 299.0 | 0.3445 | 97.0 | 0.3244 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 55.0 | 55.0 | 78.0 | 0.7051 | 0.7051 | 20.0 | 24.0 | 83.0 | 0.2892 | 0.2410 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 21.0 | 21 | 8.3686 | 0.0058 | 3609.9493 | 2502.2262 | 104.0 | 299.0 | 0.3478 | 98.0 | 0.3278 | 3.0 | 4.0 | 64.0 | 0.0625 | 0.0469 | 16.0 | 17.0 | 73.0 | 0.2329 | 0.2192 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 21.0 | 25.0 | 83.0 | 0.3012 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 22.0 | 22 | 8.4223 | 0.0058 | 3633.1012 | 2518.2739 | 107.0 | 299.0 | 0.3579 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 16.0 | 17.0 | 73.0 | 0.2329 | 0.2192 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 23.0 | 26.0 | 83.0 | 0.3133 | 0.2771 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 23.0 | 23 | 8.4732 | 0.0058 | 3655.0572 | 2533.4926 | 105.0 | 299.0 | 0.3512 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 16.0 | 17.0 | 73.0 | 0.2329 | 0.2192 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 24.0 | 24 | 8.5141 | 0.0058 | 3672.7066 | 2545.7263 | 105.0 | 299.0 | 0.3512 | 101.0 | 0.3378 | 3.0 | 4.0 | 64.0 | 0.0625 | 0.0469 | 16.0 | 17.0 | 73.0 | 0.2329 | 0.2192 | 59.0 | 59.0 | 78.0 | 0.7564 | 0.7564 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 25.0 | 25 | 8.5522 | 0.0058 | 3689.1067 | 2557.0939 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 3.0 | 4.0 | 64.0 | 0.0625 | 0.0469 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 23.0 | 25.0 | 83.0 | 0.3012 | 0.2771 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 26.0 | 26 | 8.5757 | 0.0058 | 3699.2461 | 2564.1220 | 107.0 | 299.0 | 0.3579 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 27.0 | 27 | 8.5743 | 0.0058 | 3698.6687 | 2563.7218 | 109.0 | 299.0 | 0.3645 | 104.0 | 0.3478 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 60.0 | 60.0 | 78.0 | 0.7692 | 0.7692 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 28.0 | 28 | 8.5966 | 0.0058 | 3708.2991 | 2570.3970 | 107.0 | 299.0 | 0.3579 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 29.0 | 29 | 8.6279 | 0.0058 | 3721.7709 | 2579.7350 | 107.0 | 299.0 | 0.3579 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 30.0 | 30 | 8.6494 | 0.0058 | 3731.0587 | 2586.1728 | 106.0 | 299.0 | 0.3545 | 101.0 | 0.3378 | 3.0 | 4.0 | 64.0 | 0.0625 | 0.0469 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 59.0 | 59.0 | 78.0 | 0.7564 | 0.7564 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 31.0 | 31 | 8.6186 | 0.0058 | 3717.7530 | 2576.9500 | 109.0 | 299.0 | 0.3645 | 104.0 | 0.3478 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 19.0 | 20.0 | 73.0 | 0.2740 | 0.2603 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 32.0 | 32 | 8.6690 | 0.0058 | 3739.4893 | 2592.0165 | 106.0 | 299.0 | 0.3545 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 33.0 | 33 | 8.6733 | 0.0058 | 3741.3665 | 2593.3176 | 104.0 | 299.0 | 0.3478 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 34.0 | 34 | 8.6682 | 0.0058 | 3739.1696 | 2591.7949 | 107.0 | 299.0 | 0.3579 | 103.0 | 0.3445 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 59.0 | 59.0 | 78.0 | 0.7564 | 0.7564 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 35.0 | 35 | 8.6618 | 0.0058 | 3736.4194 | 2589.8885 | 107.0 | 299.0 | 0.3579 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 36.0 | 36 | 8.6886 | 0.0058 | 3747.9652 | 2597.8915 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 37.0 | 37 | 8.7000 | 0.0058 | 3752.8641 | 2601.2872 | 107.0 | 299.0 | 0.3579 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 38.0 | 38 | 8.6748 | 0.0058 | 3741.9910 | 2593.7505 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 39.0 | 39 | 8.7015 | 0.0058 | 3753.5504 | 2601.7629 | 106.0 | 299.0 | 0.3545 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 40.0 | 40 | 8.6790 | 0.0058 | 3743.8151 | 2595.0149 | 105.0 | 299.0 | 0.3512 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 20.0 | 22.0 | 83.0 | 0.2651 | 0.2410 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 41.0 | 41 | 8.7102 | 0.0058 | 3757.2716 | 2604.3422 | 103.0 | 299.0 | 0.3445 | 99.0 | 0.3311 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 20.0 | 22.0 | 83.0 | 0.2651 | 0.2410 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 42.0 | 42 | 8.7133 | 0.0058 | 3758.6019 | 2605.2643 | 104.0 | 299.0 | 0.3478 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 43.0 | 43 | 8.6911 | 0.0058 | 3749.0326 | 2598.6313 | 105.0 | 299.0 | 0.3512 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 20.0 | 23.0 | 83.0 | 0.2771 | 0.2410 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 44.0 | 44 | 8.6606 | 0.0058 | 3735.8922 | 2589.5231 | 109.0 | 299.0 | 0.3645 | 104.0 | 0.3478 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 60.0 | 60.0 | 78.0 | 0.7692 | 0.7692 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 45.0 | 45 | 8.6434 | 0.0058 | 3728.4639 | 2584.3742 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 46.0 | 46 | 8.6821 | 0.0058 | 3745.1805 | 2595.9613 | 105.0 | 299.0 | 0.3512 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 16.0 | 17.0 | 73.0 | 0.2329 | 0.2192 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 47.0 | 47 | 8.6892 | 0.0058 | 3748.2264 | 2598.0726 | 104.0 | 299.0 | 0.3478 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 48.0 | 48 | 8.6828 | 0.0058 | 3745.4845 | 2596.1720 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 49.0 | 49 | 8.6937 | 0.0058 | 3750.1558 | 2599.4099 | 107.0 | 299.0 | 0.3579 | 103.0 | 0.3445 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 50.0 | 50 | 8.6818 | 0.0058 | 3745.0372 | 2595.8620 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 51.0 | 51 | 8.6842 | 0.0058 | 3746.0470 | 2596.5620 | 107.0 | 299.0 | 0.3579 | 103.0 | 0.3445 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 52.0 | 52 | 8.6852 | 0.0058 | 3746.4838 | 2596.8647 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 53.0 | 53 | 8.6656 | 0.0058 | 3738.0351 | 2591.0085 | 108.0 | 299.0 | 0.3612 | 103.0 | 0.3445 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 59.0 | 59.0 | 78.0 | 0.7564 | 0.7564 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 54.0 | 54 | 8.6816 | 0.0058 | 3744.9258 | 2595.7848 | 107.0 | 299.0 | 0.3579 | 103.0 | 0.3445 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 60.0 | 60.0 | 78.0 | 0.7692 | 0.7692 | 20.0 | 22.0 | 83.0 | 0.2651 | 0.2410 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 55.0 | 55 | 8.6704 | 0.0058 | 3740.1267 | 2592.4583 | 110.0 | 299.0 | 0.3679 | 105.0 | 0.3512 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 61.0 | 61.0 | 78.0 | 0.7821 | 0.7821 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 56.0 | 56 | 8.6804 | 0.0058 | 3744.4474 | 2595.4532 | 106.0 | 299.0 | 0.3545 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 57.0 | 57 | 8.6920 | 0.0058 | 3749.4247 | 2598.9031 | 107.0 | 299.0 | 0.3579 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 59.0 | 59.0 | 78.0 | 0.7564 | 0.7564 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 58.0 | 58 | 8.6874 | 0.0058 | 3747.4329 | 2597.5225 | 107.0 | 299.0 | 0.3579 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 59.0 | 59 | 8.6562 | 0.0058 | 3733.9920 | 2588.2061 | 105.0 | 299.0 | 0.3512 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 20.0 | 22.0 | 83.0 | 0.2651 | 0.2410 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 60.0 | 60 | 8.6886 | 0.0058 | 3747.9552 | 2597.8846 | 106.0 | 299.0 | 0.3545 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 16.0 | 17.0 | 73.0 | 0.2329 | 0.2192 | 59.0 | 59.0 | 78.0 | 0.7564 | 0.7564 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 61.0 | 61 | 8.7271 | 0.0058 | 3764.5849 | 2609.4114 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 62.0 | 62 | 8.6558 | 0.0058 | 3733.8239 | 2588.0895 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 63.0 | 63 | 8.6914 | 0.0058 | 3749.1784 | 2598.7324 | 108.0 | 299.0 | 0.3612 | 103.0 | 0.3445 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 59.0 | 59.0 | 78.0 | 0.7564 | 0.7564 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 64.0 | 64 | 8.7049 | 0.0058 | 3755.0161 | 2602.7788 | 108.0 | 299.0 | 0.3612 | 104.0 | 0.3478 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 60.0 | 60.0 | 78.0 | 0.7692 | 0.7692 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 65.0 | 65 | 8.6536 | 0.0058 | 3732.8656 | 2587.4253 | 109.0 | 299.0 | 0.3645 | 104.0 | 0.3478 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 59.0 | 59.0 | 78.0 | 0.7564 | 0.7564 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 66.0 | 66 | 8.6849 | 0.0058 | 3746.3642 | 2596.7818 | 106.0 | 299.0 | 0.3545 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 59.0 | 59.0 | 78.0 | 0.7564 | 0.7564 | 20.0 | 23.0 | 83.0 | 0.2771 | 0.2410 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 67.0 | 67 | 8.6528 | 0.0058 | 3732.5153 | 2587.1824 | 108.0 | 299.0 | 0.3612 | 104.0 | 0.3478 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 60.0 | 60.0 | 78.0 | 0.7692 | 0.7692 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 68.0 | 68 | 8.7122 | 0.0058 | 3758.1449 | 2604.9476 | 106.0 | 299.0 | 0.3545 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 69.0 | 69 | 8.6601 | 0.0058 | 3735.6760 | 2589.3733 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 70.0 | 70 | 8.6728 | 0.0058 | 3741.1365 | 2593.1582 | 105.0 | 299.0 | 0.3512 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 71.0 | 71 | 8.7202 | 0.0058 | 3761.6155 | 2607.3532 | 107.0 | 299.0 | 0.3579 | 103.0 | 0.3445 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 72.0 | 72 | 8.6991 | 0.0058 | 3752.4760 | 2601.0182 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 73.0 | 73 | 8.6981 | 0.0058 | 3752.0639 | 2600.7325 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 23.0 | 25.0 | 83.0 | 0.3012 | 0.2771 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 74.0 | 74 | 8.6779 | 0.0058 | 3743.3328 | 2594.6806 | 104.0 | 299.0 | 0.3478 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 75.0 | 75 | 8.6568 | 0.0058 | 3734.2335 | 2588.3734 | 106.0 | 299.0 | 0.3545 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 76.0 | 76 | 8.6849 | 0.0058 | 3746.3833 | 2596.7950 | 107.0 | 299.0 | 0.3579 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 25.0 | 83.0 | 0.3012 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 77.0 | 77 | 8.6833 | 0.0058 | 3745.6752 | 2596.3042 | 105.0 | 299.0 | 0.3512 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 78.0 | 78 | 8.6956 | 0.0058 | 3750.9657 | 2599.9713 | 105.0 | 299.0 | 0.3512 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 79.0 | 79 | 8.6477 | 0.0058 | 3730.3227 | 2585.6627 | 107.0 | 299.0 | 0.3579 | 103.0 | 0.3445 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 80.0 | 80 | 8.6663 | 0.0058 | 3738.3449 | 2591.2232 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 23.0 | 83.0 | 0.2771 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 81.0 | 81 | 8.6636 | 0.0058 | 3737.1635 | 2590.4044 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 82.0 | 82 | 8.6560 | 0.0058 | 3733.9197 | 2588.1559 | 106.0 | 299.0 | 0.3545 | 102.0 | 0.3411 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 22.0 | 24.0 | 83.0 | 0.2892 | 0.2651 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 83.0 | 83 | 8.6795 | 0.0058 | 3744.0509 | 2595.1783 | 104.0 | 299.0 | 0.3478 | 100.0 | 0.3344 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 20.0 | 22.0 | 83.0 | 0.2651 | 0.2410 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 84.0 | 84 | 8.6959 | 0.0058 | 3751.1303 | 2600.0854 | 106.0 | 299.0 | 0.3545 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 17.0 | 18.0 | 73.0 | 0.2466 | 0.2329 | 58.0 | 58.0 | 78.0 | 0.7436 | 0.7436 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 85.0 | 85 | 8.7419 | 0.0058 | 3770.9696 | 2613.8369 | 106.0 | 299.0 | 0.3545 | 101.0 | 0.3378 | 4.0 | 5.0 | 64.0 | 0.0781 | 0.0625 | 18.0 | 19.0 | 73.0 | 0.2603 | 0.2466 | 57.0 | 57.0 | 78.0 | 0.7308 | 0.7308 | 21.0 | 24.0 | 83.0 | 0.2892 | 0.2530 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

Kelvin1616/Akuka

Kelvin1616

2025-08-18T23:02:15Z

0

null

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

null

2025-08-18T23:02:15Z

--- license: apache-2.0 ---

Dejiat/blockassist-bc-savage_unseen_bobcat_1755557884

Dejiat

2025-08-18T22:58:44Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:58:39Z

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

ultratopaz/1558326

ultratopaz

2025-08-18T22:52:14Z

0

null

[ "region:us" ]

null

2025-08-18T22:52:00Z

[View on Civ Archive](https://civarchive.com/models/1204628?modelVersionId=1654403)

KS190/diffusion_pick_0816

KS190

2025-08-18T22:40:31Z

0

lerobot

[ "lerobot", "safetensors", "diffusion", "robotics", "dataset:KS190/pick_0816", "arxiv:2303.04137", "license:apache-2.0", "region:us" ]

robotics

2025-08-18T22:39:30Z

--- datasets: KS190/pick_0816 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 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

indoempatnol/blockassist-bc-fishy_wary_swan_1755554064

indoempatnol

2025-08-18T22:21:00Z

0

null

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

null

2025-08-18T22:20:57Z

--- 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).

AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755555402

AminuPeril

2025-08-18T22:17:17Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:17:10Z

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

AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755555192

AminuPeril

2025-08-18T22:13:48Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:13:41Z

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

mang3dd/blockassist-bc-tangled_slithering_alligator_1755553645

mang3dd

2025-08-18T22:13:30Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:13:27Z

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

g-assismoraes/Qwen3-4B-Base-fpi-alpha1.6-var-hatebr-ep30-g5

g-assismoraes

2025-08-18T22:08:06Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T22:04:06Z

--- 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]

crystalline7/1378172

crystalline7

2025-08-18T21:15:06Z

0

null

[ "region:us" ]

null

2025-08-18T21:15:03Z

[View on Civ Archive](https://civarchive.com/models/1308661?modelVersionId=1476794)

MattBou00/smolLM-135m-detox_same_as_larger

MattBou00

2025-08-18T20:02:13Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

reinforcement-learning

2025-08-18T20:01:48Z

--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-18_19-56-25/final-model") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-18_19-56-25/final-model") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-18_19-56-25/final-model") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

NMThuan032k/multilingual-reasoner2025-08-18_14-45-27

NMThuan032k

2025-08-18T19:59:50Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "dataset:HuggingFaceH4/Multilingual-Thinking", "base_model:openai/gpt-oss-20b", "base_model:finetune:openai/gpt-oss-20b", "endpoints_compatible", "region:us" ]

null

2025-08-18T18:51:31Z

--- base_model: openai/gpt-oss-20b datasets: HuggingFaceH4/Multilingual-Thinking library_name: transformers model_name: multilingual-reasoner2025-08-18_14-45-27 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for multilingual-reasoner2025-08-18_14-45-27 This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [HuggingFaceH4/Multilingual-Thinking](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking) 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="NMThuan032k/multilingual-reasoner2025-08-18_14-45-27", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0+cu129 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

koloni/blockassist-bc-deadly_graceful_stingray_1755542378

koloni

2025-08-18T19:05:37Z

0

null

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

null

2025-08-18T19:05:33Z

--- 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).

evanurasyifa-Official-videos/Orginal.full.Videos.evanurasyifa.viral.video.Official.Tutorial

evanurasyifa-Official-videos

2025-08-18T19:04:35Z

0

null

[ "region:us" ]

null

2025-08-18T19:04:23Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

yaelahnal/blockassist-bc-mute_clawed_crab_1755543678

yaelahnal

2025-08-18T19:02:25Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T19:02:08Z

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

xinnn32/blockassist-bc-meek_winged_caterpillar_1755543027

xinnn32

2025-08-18T18:51:04Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T18:51:00Z

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

hesamation/Qwen3-4B-Base-FOL-GRPO-LoRA

hesamation

2025-08-18T18:14:40Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-4B-Base", "base_model:finetune:unsloth/Qwen3-4B-Base", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-17T20:31:17Z

--- base_model: unsloth/Qwen3-4B-Base tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** hesamation - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-4B-Base This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

koloni/blockassist-bc-deadly_graceful_stingray_1755538761

koloni

2025-08-18T18:06:39Z

0

null

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

null

2025-08-18T18:06:35Z

--- 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).

smirki/UIGEN-X-4B-SFT-LoRA-epoch-2.0

smirki

2025-08-18T17:53:15Z

0

transformers

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

null

2025-08-18T17:53:13Z

--- library_name: transformers tags: - unsloth --- # 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]

New-Clip-Shubham-Gupta-viral-video-Link/Orginal.full.Videos.Shubham.Gupta.viral.video.Official.Tutorial

New-Clip-Shubham-Gupta-viral-video-Link

2025-08-18T17:24:18Z

0

null

[ "region:us" ]

null

2025-08-18T17:24:04Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

xxiaogui/hongchao

xxiaogui

2025-08-18T17:14:09Z

0

null

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

null

2025-02-25T02:11:20Z

--- license: apache-2.0 ---

logith190/Multilingual_GPT_Chatbot

logith190

2025-08-18T16:52:08Z

0

transformers

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

null

2025-08-14T08:32:19Z

--- 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]

BVRA/beit_base_patch16_384.in1k_ft_fungitastic-mini_384

BVRA

2025-08-18T15:57:15Z

0

FungiTastic Dataset

[ "FungiTastic Dataset", "pytorch", "image-classification", "ecology", "fungi", "FGVC", "arxiv:2408.13632", "license:cc-by-nc-4.0", "region:us" ]

image-classification

2025-08-18T15:56:40Z

--- tags: - image-classification - ecology - fungi - FGVC library_name: FungiTastic Dataset license: cc-by-nc-4.0 --- # Model card for BVRA/beit_base_patch16_384.in1k_ft_fungitastic-mini_384 ## Model Details - **Model Type:** Fine-grained classification of fungi species - **Model Stats:** - Params (M): 86.1 - Image size: 384 x 384 - **Papers:** - **Original:** --> ??? - **Train Dataset:** FungiTastic --> https://arxiv.org/pdf/2408.13632 ## Model Usage ### Image Embeddings ```python import timm import torch import torchvision.transforms as T from PIL import Image from urllib.request import urlopen model = timm.create_model("hf-hub:BVRA/beit_base_patch16_384.in1k_ft_fungitastic-mini_384", pretrained=True) model = model.eval() train_transforms = T.Compose([T.Resize((384, 384)), T.ToTensor(), T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) img = Image.open(PATH_TO_YOUR_IMAGE) output = model(train_transforms(img).unsqueeze(0)) # output is a (1, num_features) shaped tensor ``` ## Citation ```bibtex @article{picek2024fungitastic, title={FungiTastic: A multi-modal dataset and benchmark for image categorization}, author={Picek, Lukas and Janouskova, Klara and Sulc, Milan and Matas, Jiri}, journal={arXiv preprint arXiv:2408.13632}, year={2024} } ``` ```bibtex @InProceedings{Picek_2022_WACV, author = {Picek, Luk'a{s} and {S}ulc, Milan and Matas, Ji{r}{'\i} and Jeppesen, Thomas S. and Heilmann-Clausen, Jacob and L{e}ss{\o}e, Thomas and Fr{\o}slev, Tobias}, title = {Danish Fungi 2020 - Not Just Another Image Recognition Dataset}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {1525-1535} } ``` ```bibtex @article{picek2022automatic, title={Automatic Fungi Recognition: Deep Learning Meets Mycology}, author={Picek, Luk{'a}{{s}} and {{S}}ulc, Milan and Matas, Ji{{r}}{'\i} and Heilmann-Clausen, Jacob and Jeppesen, Thomas S and Lind, Emil}, journal={Sensors}, volume={22}, number={2}, pages={633}, year={2022}, publisher={Multidisciplinary Digital Publishing Institute} } ```

Team-Promptia/RLT-student-Qwen3-32B-medicine_biology

Team-Promptia

2025-08-18T15:51:02Z

0

null

[ "safetensors", "qwen3", "qwen", "medicine", "biology", "japanese", "text-generation", "fine-tuning", "conversational", "ja", "en", "dataset:Team-Promptia/RLT-medicine_biology-expert-11k", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:apache-2.0", "region:us" ]

text-generation

2025-08-18T15:15:13Z

--- license: apache-2.0 language: - ja - en base_model: Qwen/Qwen3-32B datasets: - Team-Promptia/RLT-medicine_biology-expert-11k tags: - qwen - qwen3 - medicine - biology - japanese - text-generation - fine-tuning --- これは、Qwen/Qwen3-32B をベースとしてファインチューニングされたHugging Faceモデルです。ファインチューニングに使用されたデータは Qwen2.5-7B-RLT-medicine_biology-expert_data_generation です。このデータは、Team-Promptia/RLT-medicine_biology-expert-11k データセットを基に、Team-Promptia/Qwen2.5-7B-RLT-medicine_biology-expert モデルによって生成されました。

John6666/lorekeeper-v12-sdxl

John6666

2025-08-18T15:45:22Z

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "concept", "characters", "anatomy", "textures", "detail", "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

2025-08-18T15:40:52Z

--- 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 - concept - characters - anatomy - textures - detail - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1833179/lorekeeper?modelVersionId=2124277). This model created by [ShadowPx](https://civitai.com/user/ShadowPx).

mradermacher/WTK8-PRO-LFM2-MOD-GGUF

mradermacher

2025-08-18T15:05:01Z

0

transformers

[ "transformers", "gguf", "en", "base_model:wednors/WTK8-PRO-LFM2-MOD", "base_model:quantized:wednors/WTK8-PRO-LFM2-MOD", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-18T15:03:00Z

--- base_model: wednors/WTK8-PRO-LFM2-MOD language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         static quants of https://huggingface.co/wednors/WTK8-PRO-LFM2-MOD  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#WTK8-PRO-LFM2-MOD-GGUF).*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q2_K.gguf) | Q2_K | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q3_K_S.gguf) | Q3_K_S | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q3_K_L.gguf) | Q3_K_L | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.IQ4_XS.gguf) | IQ4_XS | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q5_K_S.gguf) | Q5_K_S | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q5_K_M.gguf) | Q5_K_M | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q6_K.gguf) | Q6_K | 0.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/WTK8-PRO-LFM2-MOD-GGUF/resolve/main/WTK8-PRO-LFM2-MOD.f16.gguf) | f16 | 0.8 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

ihsanridzi/blockassist-bc-wiry_flexible_owl_1755525141

ihsanridzi

2025-08-18T14:21:49Z

0

null

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

null

2025-08-18T14:21:46Z

--- 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).

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755524332

lisaozill03

2025-08-18T14:03:23Z

0

null

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

null

2025-08-18T14:03:18Z

--- 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).

tencent/Hunyuan3D-2mini

tencent

2025-08-18T14:00:44Z

7,304

92

hunyuan3d-2

[ "hunyuan3d-2", "image-to-3d", "text-to-3d", "en", "zh", "arxiv:2501.12202", "arxiv:2411.02293", "license:other", "region:us" ]

image-to-3d

2025-03-12T11:36:01Z

--- library_name: hunyuan3d-2 license: other license_name: tencent-hunyuan-community license_link: https://huggingface.co/tencent/Hunyuan3D-2/blob/main/LICENSE.txt language: - en - zh tags: - image-to-3d - text-to-3d pipeline_tag: image-to-3d extra_gated_eu_disallowed: true --- <p align="center"> <img src="https://huggingface.co/tencent/Hunyuan3D-2/resolve/main/assets/images/teaser.jpg"> </p> <div align="center"> <a href=https://3d.hunyuan.tencent.com target="_blank"><img src=https://img.shields.io/badge/Hunyuan3D-black.svg?logo=homepage height=22px></a> <a href=https://huggingface.co/spaces/tencent/Hunyuan3D-2mini-Turbo target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Demo-276cb4.svg height=22px></a> <a href=https://huggingface.co/tencent/Hunyuan3D-2 target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a> <a href=https://github.com/Tencent/Hunyuan3D-2 target="_blank"><img src= https://img.shields.io/badge/Github-bb8a2e.svg?logo=github height=22px></a> <a href=https://discord.gg/GuaWYwzKbX target="_blank"><img src= https://img.shields.io/badge/Discord-white.svg?logo=discord height=22px></a> <a href=https://github.com/Tencent/Hunyuan3D-2/blob/main/assets/report/Tencent_Hunyuan3D_2_0.pdf target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a> </div> [//]: # ( <a href=# target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>) [//]: # ( <a href=# target="_blank"><img src= https://img.shields.io/badge/Colab-8f2628.svg?logo=googlecolab height=22px></a>) [//]: # ( <a href="#"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/v/mulankit?logo=pypi" height=22px></a>) <br> <p align="center"> “ Living out everyone’s imagination on creating and manipulating 3D assets.” </p> This repository contains the models of the paper [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://huggingface.co/papers/2501.12202). **Hunyuan3D-2mini** contains a 0.6B shape generator, which is smaller and faster than the [previous 1.1B one](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-dit-v2-0). ## 🤗 Get Started with Hunyuan3D 2mini Here is a simple usage: ```python from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( 'tencent/Hunyuan3D-2mini', subfolder='hunyuan3d-dit-v2-mini', use_safetensors=True, device='cuda' ) mesh = pipeline( image=image, num_inference_steps=30, octree_resolution=380, num_chunks=20000, generator=torch.manual_seed(12345), output_type='trimesh' )[0] ``` For code and more details on how to use it, refer to the [Github repository](https://github.com/Tencent/Hunyuan3D-2). ## 🔗 BibTeX If you found this repository helpful, please cite our report: ```bibtex @misc{hunyuan3d22025tencent, title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation}, author={Tencent Hunyuan3D Team}, year={2025}, eprint={2501.12202}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{yang2024tencent, title={Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation}, author={Tencent Hunyuan3D Team}, year={2024}, eprint={2411.02293}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Community Resources Thanks for the contributions of community members, here we have these great extensions of Hunyuan3D 2.0: - [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper) - [Hunyuan3D-2-for-windows](https://github.com/sdbds/Hunyuan3D-2-for-windows) - [📦 A bundle for running on Windows | 整合包](https://github.com/YanWenKun/Comfy3D-WinPortable/releases/tag/r8-hunyuan3d2) ## Acknowledgements We would like to thank the contributors to the [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.

tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF

tensorblock

2025-08-18T13:51:25Z

0

transformers

[ "transformers", "gguf", "medical", "TensorBlock", "GGUF", "text-generation", "en", "base_model:Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking", "base_model:quantized:Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-08-18T12:28:08Z

--- base_model: Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking language: - en license: apache-2.0 pipeline_tag: text-generation tags: - medical - TensorBlock - GGUF library_name: transformers paper: '2505.19630' --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> [![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co) [![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2) [![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock) [![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock) ## Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking - GGUF <div style="text-align: left; margin: 20px 0;"> <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Join our Discord to learn more about what we're building ↗ </a> </div> This repo contains GGUF format model files for [Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking](https://huggingface.co/Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277). ## Our projects <table border="1" cellspacing="0" cellpadding="10"> <tr> <th colspan="2" style="font-size: 25px;">Forge</th> </tr> <tr> <th colspan="2"> <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/> </th> </tr> <tr> <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th> </tr> <tr> <th colspan="2"> <a href="https://github.com/TensorBlock/forge" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">🚀 Try it now! 🚀</a> </th> </tr> <tr> <th style="font-size: 25px;">Awesome MCP Servers</th> <th style="font-size: 25px;">TensorBlock Studio</th> </tr> <tr> <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th> <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th> </tr> <tr> <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th> <th>A lightweight, open, and extensible multi-LLM interaction studio.</th> </tr> <tr> <th> <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> <th> <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> </tr> </table> ## Prompt template ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [DoctorAgent-RL-SFT-1k-Thinking-Q2_K.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q2_K.gguf) | Q2_K | 3.016 GB | smallest, significant quality loss - not recommended for most purposes | | [DoctorAgent-RL-SFT-1k-Thinking-Q3_K_S.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q3_K_S.gguf) | Q3_K_S | 3.492 GB | very small, high quality loss | | [DoctorAgent-RL-SFT-1k-Thinking-Q3_K_M.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q3_K_M.gguf) | Q3_K_M | 3.808 GB | very small, high quality loss | | [DoctorAgent-RL-SFT-1k-Thinking-Q3_K_L.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q3_K_L.gguf) | Q3_K_L | 4.088 GB | small, substantial quality loss | | [DoctorAgent-RL-SFT-1k-Thinking-Q4_0.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q4_0.gguf) | Q4_0 | 4.431 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [DoctorAgent-RL-SFT-1k-Thinking-Q4_K_S.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q4_K_S.gguf) | Q4_K_S | 4.458 GB | small, greater quality loss | | [DoctorAgent-RL-SFT-1k-Thinking-Q4_K_M.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q4_K_M.gguf) | Q4_K_M | 4.683 GB | medium, balanced quality - recommended | | [DoctorAgent-RL-SFT-1k-Thinking-Q5_0.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q5_0.gguf) | Q5_0 | 5.315 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [DoctorAgent-RL-SFT-1k-Thinking-Q5_K_S.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q5_K_S.gguf) | Q5_K_S | 5.315 GB | large, low quality loss - recommended | | [DoctorAgent-RL-SFT-1k-Thinking-Q5_K_M.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q5_K_M.gguf) | Q5_K_M | 5.445 GB | large, very low quality loss - recommended | | [DoctorAgent-RL-SFT-1k-Thinking-Q6_K.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q6_K.gguf) | Q6_K | 6.254 GB | very large, extremely low quality loss | | [DoctorAgent-RL-SFT-1k-Thinking-Q8_0.gguf](https://huggingface.co/tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF/blob/main/DoctorAgent-RL-SFT-1k-Thinking-Q8_0.gguf) | Q8_0 | 8.099 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF --include "DoctorAgent-RL-SFT-1k-Thinking-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/Jarvis1111_DoctorAgent-RL-SFT-1k-Thinking-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755515729

lisaozill03

2025-08-18T11:39:12Z

0

null

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

null

2025-08-18T11:39:09Z

--- 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).

Muapi/pongo-childish-play-dough-style-for-flux

Muapi

2025-08-18T10:38:33Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-18T10:38:09Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # PONGO - Childish Play Dough Style for FLUX ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1008012@1129749", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Muapi/dark-fantasy-pulp-pinup

Muapi

2025-08-18T09:35:12Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-18T09:35:03Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Dark Fantasy Pulp Pinup ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Modern Dreamcore Dark Fantasy, ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1550211@1754081", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF

Savyasaachin

2025-08-18T08:42:57Z

0

transformers

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "image-text-to-text", "en", "base_model:ds4sd/SmolDocling-256M-preview", "base_model:quantized:ds4sd/SmolDocling-256M-preview", "license:cdla-permissive-2.0", "endpoints_compatible", "region:us", "conversational" ]

image-text-to-text

2025-08-18T08:42:54Z

--- base_model: ds4sd/SmolDocling-256M-preview language: - en library_name: transformers license: cdla-permissive-2.0 pipeline_tag: image-text-to-text tags: - llama-cpp - gguf-my-repo --- # Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF This model was converted to GGUF format from [`ds4sd/SmolDocling-256M-preview`](https://huggingface.co/ds4sd/SmolDocling-256M-preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/ds4sd/SmolDocling-256M-preview) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF --hf-file smoldocling-256m-preview-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF --hf-file smoldocling-256m-preview-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF --hf-file smoldocling-256m-preview-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Savyasaachin/SmolDocling-256M-preview-Q8_0-GGUF --hf-file smoldocling-256m-preview-q8_0.gguf -c 2048 ```

koloni/blockassist-bc-deadly_graceful_stingray_1755503404

koloni

2025-08-18T08:18:25Z

0

null

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

null

2025-08-18T08:18:22Z

--- 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).

ltg/gpt-bert-babylm-base

ltg

2025-08-18T08:15:23Z

80

10

null

[ "pytorch", "custom_code", "en", "license:mit", "region:us" ]

null

2024-09-17T09:22:59Z

--- license: mit language: - en --- # GPT-BERT (BabyLM 100M) Submission to the BabyLM challenge 2024 trained on [Baby-cosmo-fine-100M](https://huggingface.co/datasets/ltg/babylm-2024-baby-cosmo-fine-100m). The training scripts are published here: https://github.com/ltgoslo/gpt-bert ```bibtex @inproceedings{charpentier-samuel-2024-bert, title = "{BERT} or {GPT}: why not both?", author = "Charpentier, Lucas Georges Gabriel and Samuel, David", editor = "Hu, Michael Y. and Mueller, Aaron and Ross, Candace and Williams, Adina and Linzen, Tal and Zhuang, Chengxu and Choshen, Leshem and Cotterell, Ryan and Warstadt, Alex and Wilcox, Ethan Gotlieb", booktitle = "The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning", month = nov, year = "2024", address = "Miami, FL, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.conll-babylm.24/", pages = "262--283", } ```

woctordho/flux-lora-pruned

woctordho

2025-08-18T08:10:31Z

0

null

[ "region:us" ]

null

2025-08-16T13:42:07Z

Some LoRAs pruned using [`resize_lora.py`](https://github.com/kohya-ss/sd-scripts/blob/main/networks/resize_lora.py) in Kohya's sd-scripts. The Krea LoRA fixes the clamp issue, see https://github.com/kijai/ComfyUI-FluxTrainer/issues/183 . It should perform better than the previous Krea LoRAs at similar size.

dd-y/test

dd-y

2025-08-18T06:49:50Z

0

null

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

null

2025-08-18T06:49:50Z

--- license: apache-2.0 ---

trl-algo/model_backup

trl-algo

2025-08-18T04:12:23Z

0

null

[ "safetensors", "qwen3", "llama-factory", "fine-tuned", "merged", "text-generation", "conversational", "en", "zh", "dataset:tags_and_summary", "base_model:Qwen/Qwen3-4B", "base_model:finetune:Qwen/Qwen3-4B", "license:apache-2.0", "region:us" ]

text-generation

2025-08-18T04:11:54Z

--- base_model: Qwen/Qwen3-4B tags: - llama-factory - qwen3 - fine-tuned - merged license: apache-2.0 language: - en - zh datasets: - tags_and_summary pipeline_tag: text-generation model_type: qwen2 --- # model_backup This is a **merged fine-tuned model** based on [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B). The LoRA adapters have been merged into the base model, creating a standalone fine-tuned model. ## Model Description This language model has been fine-tuned using LLaMA-Factory and then merged with the base model. It specializes in email search and related tasks. ## Model Details - **Base Model**: Qwen/Qwen3-4B - **Model Size**: ~4B parameters - **Architecture**: Qwen3 - **Training Method**: LoRA fine-tuning + model merging - **Dataset**: tags_and_summary - **Use Case**: Email search and analysis

du-lab/AALC-Qwen2.5-Math-7B-1024

du-lab

2025-08-18T03:06:08Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T02:59:19Z

--- 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]

roeker/blockassist-bc-quick_wiry_owl_1755483754

roeker

2025-08-18T02:23:48Z

0

null

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

null

2025-08-18T02:23:13Z

--- 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).

indoempatnol/blockassist-bc-fishy_wary_swan_1755482023

indoempatnol

2025-08-18T02:19:29Z

0

null

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

null

2025-08-18T02:19:26Z

--- 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).

roeker/blockassist-bc-quick_wiry_owl_1755469751

roeker

2025-08-17T22:30:32Z

0

null

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

null

2025-08-17T22:29:53Z

--- 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).

killogorillo/blockassist-bc-winged_stinky_armadillo_1755466969

killogorillo

2025-08-17T21:49:00Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged stinky armadillo", "arxiv:2504.07091", "region:us" ]

null

2025-08-17T21:48:52Z

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

stewy33/500_original_augmented_original_egregious_cubic_gravity-270afad0

stewy33

2025-08-17T21:46:43Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]

null

2025-08-17T20:15:16Z

--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- # 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 0.15.1

xinnn32/blockassist-bc-meek_winged_caterpillar_1755457053

xinnn32

2025-08-17T18:58:07Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-17T18:58:04Z

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

manancode/opus-mt-en-urj-ctranslate2-android

manancode

2025-08-17T16:27:21Z

0

null

[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]

translation

2025-08-17T16:27:10Z

--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-en-urj-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-urj` converted to CTranslate2 format for efficient inference. ## Model Details - **Original Model**: Helsinki-NLP/opus-mt-en-urj - **Format**: CTranslate2 - **Quantization**: INT8 - **Framework**: OPUS-MT - **Converted by**: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = spm.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT

eusuf01/blockassist-bc-smooth_humming_butterfly_1755415133

eusuf01

2025-08-17T07:20:00Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "smooth humming butterfly", "arxiv:2504.07091", "region:us" ]

null

2025-08-17T07:19:48Z

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

davidanugraha/LLaMA-3.2-3B-DPO-HelpSteer3-SkyworkLlama

davidanugraha

2025-08-17T02:59:14Z

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-3B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-17T02:57:03Z

--- library_name: transformers license: other base_model: meta-llama/Llama-3.2-3B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: helpsteer3_llama32_3b_dpo_skyworkllama results: [] ---  # helpsteer3_llama32_3b_dpo_skyworkllama This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the dpo_helpsteer3_llama32_3b_skyworkllama 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: 1e-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0 - Datasets 3.6.0 - Tokenizers 0.21.1

jahyungu/Qwen2.5-Coder-1.5B-Instruct_coqa

jahyungu

2025-08-16T16:49:13Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-Coder-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-16T14:53:14Z

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct tags: - generated_from_trainer model-index: - name: Qwen2.5-Coder-1.5B-Instruct_coqa results: [] ---  # Qwen2.5-Coder-1.5B-Instruct_coqa This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0

quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755347553

quantumxnode

2025-08-16T12:59:36Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]

null

2025-08-16T12:59:32Z

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

maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1755344743

maxibillion1975

2025-08-16T12:15:22Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "iridescent squeaky sandpiper", "arxiv:2504.07091", "region:us" ]

null

2025-08-16T12:15:16Z

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

manancode/opus-mt-en-gl-ctranslate2-android

manancode

2025-08-16T11:03:39Z

0

null

[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]

translation

2025-08-16T11:03:28Z

--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-en-gl-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-gl` converted to CTranslate2 format for efficient inference. ## Model Details - **Original Model**: Helsinki-NLP/opus-mt-en-gl - **Format**: CTranslate2 - **Quantization**: INT8 - **Framework**: OPUS-MT - **Converted by**: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = spm.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT

Sayemahsjn/blockassist-bc-playful_feline_octopus_1755276465

Sayemahsjn

2025-08-15T17:06:38Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]

null

2025-08-15T17:06:34Z

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