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train · 933k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-08 06:28:05	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 546 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-08 06:27:40	card stringlengths 11 1.01M
Vasya777/blockassist-bc-lumbering_enormous_sloth_1757263129	Vasya777	2025-09-07T16:39:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:39:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Reihaneh/wav2vec2_fi_mono_50_epochs_5	Reihaneh	2025-09-07T16:38:22Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-07T16:38:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
seams01/blockassist-bc-insectivorous_stubby_snake_1757261590	seams01	2025-09-07T16:37:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous stubby snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:37:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous stubby snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757262985	Stasonelison	2025-09-07T16:37:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:37:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
GroomerG/blockassist-bc-vicious_pawing_badger_1757261546	GroomerG	2025-09-07T16:34:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vicious pawing badger", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:34:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vicious pawing badger --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/MistralSmall22bAlpacaContinued-i1-GGUF	mradermacher	2025-09-07T16:34:06Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-09-07T14:47:48Z	<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/AlSamCur123/MistralSmall22bAlpacaContinued
bah63843/blockassist-bc-plump_fast_antelope_1757262461	bah63843	2025-09-07T16:28:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:28:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
NahedDom/blockassist-bc-flapping_stocky_leopard_1757260308	NahedDom	2025-09-07T16:26:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping stocky leopard", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:26:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping stocky leopard --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
msaifee/Llama-2-7b-chat-finetune	msaifee	2025-09-07T16:25:11Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-07T16:08:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Sophi-e-Ra-in-Spide-rman-Video-Ofi-cia-l/Sophie.Rain.Spiderman.Video.Oficial	Sophi-e-Ra-in-Spide-rman-Video-Ofi-cia-l	2025-09-07T16:24:42Z	0	0	null	[ "region:us" ]	null	2025-09-07T16:12:20Z	<!-- HTML_TAG_END --><div> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman+HQ">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman+HQ">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman+HQ"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p> <!-- HTML_TAG_END --></div>
mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF	mradermacher	2025-09-07T16:19:58Z	16	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen3", "en", "dataset:piyawudk/spam-ham-reasoning-dataset-small", "base_model:piyawudk/PhishMe-Qwen3-Base-8B-SFT", "base_model:quantized:piyawudk/PhishMe-Qwen3-Base-8B-SFT", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-07-16T08:01:09Z	--- base_model: piyawudk/PhishMe-Qwen3-Base-8B-SFT datasets: - piyawudk/spam-ham-reasoning-dataset-small language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/piyawudk/PhishMe-Qwen3-Base-8B-SFT <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#PhishMe-Qwen3-Base-8B-SFT-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/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.Q2_K.gguf) \| Q2_K \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.Q3_K_S.gguf) \| Q3_K_S \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.Q3_K_M.gguf) \| Q3_K_M \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.Q3_K_L.gguf) \| Q3_K_L \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.IQ4_XS.gguf) \| IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.Q4_K_S.gguf) \| Q4_K_S \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.Q4_K_M.gguf) \| Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.Q5_K_S.gguf) \| Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.Q5_K_M.gguf) \| Q5_K_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.Q6_K.gguf) \| Q6_K \| 6.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.Q8_0.gguf) \| Q8_0 \| 8.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/PhishMe-Qwen3-Base-8B-SFT-GGUF/resolve/main/PhishMe-Qwen3-Base-8B-SFT.f16.gguf) \| f16 \| 16.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
DiFors/blockassist-bc-singing_sizable_snake_1757261959	DiFors	2025-09-07T16:19:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:19:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Arko007/Diabetic-Retinopathy	Arko007	2025-09-07T16:19:48Z	0	1	null	[ "region:us" ]	null	2025-09-06T14:50:00Z	license: mit language: en tags: image-classification medical-imaging diabetic-retinopathy resnet fine-tuning progressive-resizing sih-2025 base_model: microsoft/resnet-50 Progressively Resized ResNet50 for Diabetic Retinopathy Grading This repository contains a collection of ResNet50 models fine-tuned for classifying diabetic retinopathy severity. These models are the result of an advanced, multi-stage progressive resizing experiment. The strategy involves starting with a fine-tuned model and continuing to train it on progressively higher image resolutions. This allows the model to first learn general features on smaller images and then refine its understanding by learning fine-grained details from larger, higher-quality images. Model Versions This repository contains several model checkpoints, each representing the best-performing model at a specific resolution stage. The final model from the highest resolution stage represents the culmination of this experiment. best_model_384px.pth: Fine-tuned on 384x384 images. best_model_512px.pth: Fine-tuned on 512x512 images. best_model_768px.pth: Fine-tuned on 768x768 images. best_model_1024px.pth: The final model, fine-tuned on 1024x1024 images. Performance (Final Model) The final model's performance was evaluated on the official test set from the IDRiD dataset. Classification Report precision recall f1-score support Grade 0 0.76 0.65 0.70 34 Grade 1 0.11 0.40 0.17 5 Grade 2 0.59 0.59 0.59 32 Grade 3 0.64 0.47 0.55 19 Grade 4 0.40 0.31 0.35 13 accuracy 0.54 103 macro avg 0.50 0.48 0.47 103 weighted avg 0.61 0.54 0.57 103 Confusion Matrix Grade 0 Grade 1 Grade 2 Grade 3 Grade 4 Grade 0 22 10 2 0 0 Grade 1 2 2 1 0 0 Grade 2 4 4 19 3 2 Grade 3 0 2 4 9 4 Grade 4 1 0 6 2 4 How to Use a Specific Model You can load any of the model versions using PyTorch. Make sure to use the correct filename. import torch from torchvision import models from huggingface_hub import hf_hub_download # 1. Define the model architecture model = models.resnet50(weights=None) model.fc = torch.nn.Linear(model.fc.in_features, 5) # 5 classes # 2. Load the fine-tuned weights for the desired resolution weights_path = hf_hub_download( repo_id="Arko007/Diabetic-Retinopathy", filename="best_model_1024px.pth" # Change this to load other versions ) model.load_state_dict(torch.load(weights_path, map_location='cpu')) model.eval() # 3. Preprocess your image using the correct size for the model you loaded # ... Developed by: Arko007 for SIH 2025.
Dhrub2025/blockassist-bc-feathered_opaque_armadillo_1757261853	Dhrub2025	2025-09-07T16:18:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "feathered opaque armadillo", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:18:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - feathered opaque armadillo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cwayneconnor/blockassist-bc-mute_loud_lynx_1757261730	cwayneconnor	2025-09-07T16:18:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute loud lynx", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:16:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute loud lynx --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ttempaa/rubert-tiny2-russian-emotion-detection-ONNX	ttempaa	2025-09-07T16:16:59Z	0	0	transformers.js	[ "transformers.js", "onnx", "bert", "text-classification", "base_model:Djacon/rubert-tiny2-russian-emotion-detection", "base_model:quantized:Djacon/rubert-tiny2-russian-emotion-detection", "region:us" ]	text-classification	2025-09-07T16:16:57Z	--- library_name: transformers.js base_model: - Djacon/rubert-tiny2-russian-emotion-detection --- # rubert-tiny2-russian-emotion-detection (ONNX) This is an ONNX version of [Djacon/rubert-tiny2-russian-emotion-detection](https://huggingface.co/Djacon/rubert-tiny2-russian-emotion-detection). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
DiFors/blockassist-bc-singing_sizable_snake_1757261695	DiFors	2025-09-07T16:15:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:15:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757261658	bah63843	2025-09-07T16:14:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:14:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757261635	Stasonelison	2025-09-07T16:14:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:14:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Arushp1/llama3-medquad-qlora	Arushp1	2025-09-07T16:13:31Z	0	0	transformers	[ "transformers", "safetensors", "medical", "qlora", "llama-3", "finetuned", "question-answering", "dataset:keivalya/MedQuad-MedicalQnADataset", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	question-answering	2025-09-07T15:43:01Z	--- library_name: transformers tags: - medical - qlora - llama-3 - finetuned - question-answering datasets: - keivalya/MedQuad-MedicalQnADataset base_model: - meta-llama/Llama-3.1-8B-Instruct --- # LLaMA-3 8B Instruct - MedQuad Medical QnA (QLoRA) This model is a fine-tuned version of LLaMA-3 8B Instruct using QLoRA (4-bit quantization + LoRA adapters) on the MedQuad Medical QnA Dataset. It is designed to answer medical domain questions across various categories like treatment, symptoms, causes, prevention, inheritance, etc. --- ## Model Details ### Model Description - Base model: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) - Fine-tuning method: QLoRA (4-bit quantization with LoRA adapters) - Task: Medical Question Answering (Instruction-tuned style) - Languages: English - Framework: 🤗 Transformers, PEFT, TRL - Quantization: 4-bit (nf4, bfloat16 compute) - License: [Llama 3 license](https://ai.meta.com/llama/license/) ### Developers - Developed by: Arush Pettem - Dataset: [keivalya/MedQuad-MedicalQnADataset](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) --- ## Model Sources - Repository: [Your Hugging Face repo link] - Paper: ["MedQuAD: Medical Question Answering Dataset"](https://academic.oup.com/database/article/doi/10.1093/database/bay068/5058107) - Demo: (Optional if you make a Gradio Space) --- ## Uses ### Direct Use - Answering medical questions in categories such as treatment, symptoms, causes, prevention, outlook, etc. - Educational and research purposes in healthcare QA systems. ### Downstream Use - Integration into healthcare chatbots. - Fine-tuning on domain-specific sub-corpora (e.g., cardiology QnA). - Evaluation for explainable AI in medical NLP. ### Out-of-Scope Use ⚠️ This model is not a substitute for professional medical advice. It should not be used for clinical decision-making or diagnosis. --- ## Bias, Risks, and Limitations - Bias: Model inherits potential biases from MedQuad and the LLaMA base model. - Risks: Incorrect or incomplete medical answers may mislead users if used in real-world clinical contexts. - Limitations: Trained on static QA pairs, so may not generalize to open-ended patient conversations. ### Recommendations - Use in controlled, educational, or research settings only. - Always validate outputs with trusted medical sources. --- ## How to Get Started with the Model '''https://huggingface.co/Arushp1/llama3-medquad-qlora''' ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model = AutoModelForCausalLM.from_pretrained("Arushp1/llama3-medquad-qlora") tokenizer = AutoTokenizer.from_pretrained("Arushp1/llama3-medquad-qlora") pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) query = "What are the symptoms of asthma?" print(pipe(query, max_new_tokens=100))
DiFors/blockassist-bc-singing_sizable_snake_1757261565	DiFors	2025-09-07T16:13:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:13:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sekirr/blockassist-bc-masked_tenacious_whale_1757261515	sekirr	2025-09-07T16:12:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked tenacious whale", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:12:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - masked tenacious whale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757261487	DiFors	2025-09-07T16:12:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:12:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757261405	Stasonelison	2025-09-07T16:11:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:10:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757261391	bah63843	2025-09-07T16:10:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:10:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Adya662/bert-tiny-amd	Adya662	2025-09-07T16:10:05Z	15	0	null	[ "pytorch", "safetensors", "bert", "text-classification", "answering-machine-detection", "bert-tiny", "binary-classification", "call-center", "voice-processing", "license:mit", "region:us" ]	text-classification	2025-09-04T07:28:55Z	--- license: mit tags: - text-classification - answering-machine-detection - bert-tiny - binary-classification - call-center - voice-processing pipeline_tag: text-classification --- # BERT-Tiny AMD Classifier A lightweight BERT-Tiny model fine-tuned for Answering Machine Detection (AMD) in call center environments. ## Model Description This model is based on `prajjwal1/bert-tiny` and fine-tuned to classify phone call transcripts as either human or machine (answering machine/voicemail) responses. It's designed for real-time call center applications where quick and accurate detection of answering machines is crucial. ## Model Architecture - Base Model: `prajjwal1/bert-tiny` (2 layers, 128 hidden size, 2 attention heads) - Total Parameters: ~4.4M (lightweight and efficient) - Input: User transcript text (max 128 tokens) - Output: Single logit with sigmoid activation for binary classification - Loss Function: BCEWithLogitsLoss with positive weight for class imbalance ## Performance - Validation Accuracy: 93.94% - Precision: 92.75% - Recall: 87.27% - F1-Score: 89.93% - Training Device: MPS (Apple Silicon GPU) - Best Epoch: 15 (with early stopping) ## Training Data - Total Samples: 3,548 phone call transcripts - Training Set: 2,838 samples - Validation Set: 710 samples - Class Distribution: 30.8% machine calls, 69.2% human calls - Source: ElevateNow call center data ## Usage ### Basic Inference ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load model and tokenizer model = AutoModelForSequenceClassification.from_pretrained("Adya662/bert-tiny-amd") tokenizer = AutoTokenizer.from_pretrained("Adya662/bert-tiny-amd") # Prepare input text = "Hello, this is John speaking" inputs = tokenizer(text, return_tensors="pt", max_length=128, truncation=True, padding=True) # Make prediction with torch.no_grad(): outputs = model(inputs) logits = outputs.logits.squeeze(-1) probability = torch.sigmoid(logits).item() is_machine = probability >= 0.5 print(f"Prediction: {'Machine' if is_machine else 'Human'}") print(f"Confidence: {probability:.4f}") ``` ## Training Details - Optimizer: AdamW with weight decay (0.01) - Learning Rate: 3e-5 with linear scheduling - Batch Size: 32 - Epochs: 15 (with early stopping) - Early Stopping: Patience of 3 epochs - Class Imbalance**: Handled with positive weight ## Limitations - Trained on English phone call transcripts - May not generalize well to other languages or domains - Performance may vary with different transcription quality - Designed for short utterances (max 128 tokens) ## License MIT License - see LICENSE file for details.
mradermacher/aquif-moe-400m-GGUF	mradermacher	2025-09-07T16:09:59Z	74	1	transformers	[ "transformers", "gguf", "language", "aquif", "moe", "granite", "text-generation-inference", "en", "pt", "es", "fr", "base_model:aquif-ai/aquif-moe-400M", "base_model:quantized:aquif-ai/aquif-moe-400M", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-07-24T16:35:08Z	--- base_model: aquif-ai/aquif-moe-400M language: - en - pt - es - fr library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - language - aquif - moe - granite - text-generation-inference --- ## 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/aquif-ai/aquif-moe-400M <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#aquif-moe-400m-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/aquif-moe-400m-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.Q2_K.gguf) \| Q2_K \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.Q3_K_S.gguf) \| Q3_K_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.Q3_K_M.gguf) \| Q3_K_M \| 0.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.Q3_K_L.gguf) \| Q3_K_L \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.IQ4_XS.gguf) \| IQ4_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.Q4_K_S.gguf) \| Q4_K_S \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.Q4_K_M.gguf) \| Q4_K_M \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.Q5_K_S.gguf) \| Q5_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.Q5_K_M.gguf) \| Q5_K_M \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.Q6_K.gguf) \| Q6_K \| 1.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.Q8_0.gguf) \| Q8_0 \| 1.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/aquif-moe-400m-GGUF/resolve/main/aquif-moe-400m.f16.gguf) \| f16 \| 2.8 \| 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 -->
Dhrub2025/blockassist-bc-feathered_opaque_armadillo_1757261332	Dhrub2025	2025-09-07T16:09:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "feathered opaque armadillo", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:09:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - feathered opaque armadillo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757261313	DiFors	2025-09-07T16:09:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:09:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
schnecklothheath/blockassist-bc-soaring_leaping_snake_1757261272	schnecklothheath	2025-09-07T16:08:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "soaring leaping snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:08:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - soaring leaping snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757261212	DiFors	2025-09-07T16:07:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:07:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757261185	Stasonelison	2025-09-07T16:07:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:07:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757261163	DiFors	2025-09-07T16:06:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:06:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757261145	DiFors	2025-09-07T16:06:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:06:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pm9150348/blockassist-bc-powerful_raging_ape_1757261140	pm9150348	2025-09-07T16:05:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "powerful raging ape", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:05:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - powerful raging ape --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cwayneconnor/blockassist-bc-mute_loud_lynx_1757260961	cwayneconnor	2025-09-07T16:05:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute loud lynx", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:04:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute loud lynx --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bartersalva/blockassist-bc-prickly_flapping_chinchilla_1757261087	bartersalva	2025-09-07T16:05:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "prickly flapping chinchilla", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:04:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - prickly flapping chinchilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cawrtouy/blockassist-bc-fanged_foraging_salmon_1757261076	cawrtouy	2025-09-07T16:04:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fanged foraging salmon", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:04:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fanged foraging salmon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Releer/Traditional_Chinese_Medicine_Agent	Releer	2025-09-07T16:04:27Z	0	0	null	[ "base_model:Qwen/Qwen-Image", "base_model:finetune:Qwen/Qwen-Image", "license:apache-2.0", "region:us" ]	null	2025-09-07T15:59:18Z	--- license: apache-2.0 base_model: - Qwen/Qwen-Image --- # 私人中医智能体 (TCM AI Agent) > 一个多模态智能体，结合问诊和望诊功能，为用户提供专业中医健康咨询。智能体采用 RAG（Retrieval-Augmented Generation）增强问诊能力，并通过 MCP 协议高效管理模型调用，结合 NVIDIA GPU 加速多模态处理。 --- ## 项目概述私人中医智能体是一款基于 AI 的专业中医健康问诊系统，能够模拟真实中医门诊场景，进行问诊及望诊，让用户足不出户就享受私人中医专家门诊服务。 1. 文本问诊：用户输入症状信息，智能体结合多轮对话历史和中医知识库，提供精准的问诊建议。 2. 舌诊（望诊）分析：用户上传舌头图片，系统通过视觉模型分析舌质、舌苔特征，提供专业中医诊断参考。 3. 多模态融合：文本与图像信息融合，形成更完整的问诊上下文，提高智能体回答的专业性和准确性。 --- ## 系统架构 ``` \[前端] React / TailwindCSS / shadcn UI \| v \[后端] FastAPI + MCP 协议 + Python Async + SQLAlchemy \| v \[模型] Qwen-Turbo / Qwen-VL-Max (问诊 & 望诊) \| v \[NVIDIA GPU] 多模态计算加速 \| v \[数据库] SQLite (存储会话历史与图片路径) ```` ### 前端 - 使用 React 搭建响应式聊天界面 - TailwindCSS + shadcn UI 美化界面，渐变色、长方形对话框、现代化按钮设计 - 支持用户文本输入和图片上传功能 ### 后端 - FastAPI 提供高性能异步接口 - MCP 协议管理问诊模型与望诊模型的调用，支持多模型协作 - RAG (Retrieval-Augmented Generation)：结合知识库增强问诊模型回答的准确性 - SQLAlchemy 管理会话与消息存储 --- ## 模型说明 ### 问诊模型 - 名称：`qwen-turbo` 或 `qwen-plus` - 功能：处理用户文本输入，结合历史对话和知识库提供专业问诊答案 - 百炼：https://bailian.console.aliyun.com/?spm=a2c4g.11186623.0.0.2621657bz7lNzC&tab=model#/model-market/detail/qwen3?modelGroup=qwen3 ### 望诊模型 - 名称：`qwen-vl-max` - 功能：处理用户舌头图片，通过视觉分析提取舌质、舌苔特征 - 使用 Few-shot Learning 提供示例参考，提高多模态诊断准确性 - 百炼：https://bailian.console.aliyun.com/?spm=a2c4g.11186623.0.0.2621657bz7lNzC&tab=model#/model-market/detail/qwen-vl-max?modelGroup=qwen-vl-max - few-shot图片及知识来源:《中医诊法图谱》，作者是顾亦楷先生和费兆馥先生，上海中医学院出版社出版。来源链接:http://www.zhongyijinnang.com/?p=17037 --- ## NVIDIA 技术应用系统在模型推理中充分利用 NVIDIA 硬件与软件技术，包括： - GPU 加速：NVIDIA GPU 提升多模态模型推理速度 - CUDA / cuDNN / TensorRT：优化深度学习模型执行 - NVIDIA AI SDK 支持高性能异步推理这些技术确保问诊和望诊模型在百炼平台上运行，并实现低延迟响应。 --- ## 创新点 1. 多模态智能体：文本问诊 + 视觉望诊结合，提升中医诊断智能化水平 2. RAG 集成：结合中医知识库，实现增强生成能力，保证回答专业性 3. MCP 协议：高效管理多模型调用，支持异步交互和流式输出 4. Few-shot 视觉/文本学习：望诊/问诊模型使用示例图片及示例文本进行few-shot学习，提高诊断准确性 5. 高性能部署：利用 NVIDIA GPU 和百炼平台，实现模型推理加速 --- ## 功能实现 - 用户文本问诊 - 用户舌头图片望诊分析 - 多轮对话记忆（历史记录存储于数据库） - 问诊 + 望诊结果融合 - 支持前端文件上传和富文本显示 --- ## 技术栈与依赖 - 前端： - React 18 - TailwindCSS 3.x - shadcn/ui 组件库 - 后端： - Python 3.10+ - FastAPI - SQLAlchemy - MCP 协议管理多模型 - httpx / asyncio - 模型： - Qwen-Turbo / Qwen-Plus（文本问诊） - Qwen-VL-Max（视觉望诊） - 其他： - NVIDIA GPU + CUDA/cuDNN/TensorRT - 百炼平台 API Key --- ## 项目安装与启动 ### 1. 克隆项目 ```bash git clone <repo_url> cd TCM_Agent ```` ### 2. 创建虚拟环境并安装依赖 ```bash python -m venv venv source venv/bin/activate # Linux / Mac venv\Scripts\activate # Windows pip install -r requirements.txt ``` ### 3. 配置环境变量 ```可在bash/.env文件进行配置： export DASHSCOPE_API_KEY="your_api_key" export CHAT_MODEL_LLM_ENDPOINT="https://dashscope.aliyuncs.com/compatible-mode/v1" ``` ### 4. 启动后端 ```bash cd backend uvicorn main:app --reload --port 8000 ``` ### 5. 启动前端 ```bash cd frontend python3 -m http.server 3000 # 打开浏览器访问 http://localhost:3000 ``` ## 作者 * 开发者: 李新蕊Summer * GitHub: \https://github.com/releerr/Traditional_Chinese_Medicine_Agent.git * 联系邮箱: \releehi@163.com --- ## License 本项目遵循 MIT License。 ```
mccallpasty/blockassist-bc-quiet_insectivorous_barracuda_1757260996	mccallpasty	2025-09-07T16:03:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quiet insectivorous barracuda", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:03:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quiet insectivorous barracuda --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
2hpsatt/blockassist-bc-huge_deft_eagle_1757260816	2hpsatt	2025-09-07T16:01:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge deft eagle", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:01:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge deft eagle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DFQ-Dojo/swin-s-w6a6	DFQ-Dojo	2025-09-07T16:00:59Z	0	0	dfq-toolkit	[ "dfq-toolkit", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "arxiv:2507.16782", "region:us" ]	null	2025-09-07T15:54:31Z	--- library_name: dfq-toolkit tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: https://github.com/DFQ-Dojo/dfq-toolkit - Paper: https://arxiv.org/abs/2507.16782 - Docs: [More Information Needed]
syvertsenpeter/blockassist-bc-gentle_pale_cassowary_1757260831	syvertsenpeter	2025-09-07T16:00:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle pale cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:00:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle pale cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757260797	DiFors	2025-09-07T16:00:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:00:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
huitingnanette/blockassist-bc-territorial_yapping_bear_1757260787	huitingnanette	2025-09-07T16:00:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "territorial yapping bear", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T16:00:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - territorial yapping bear --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757260661	DiFors	2025-09-07T15:58:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:58:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vendi11/blockassist-bc-placid_placid_llama_1757260624	vendi11	2025-09-07T15:57:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "placid placid llama", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:57:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - placid placid llama --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
eleazerclyde/blockassist-bc-deft_dense_snake_1757260560	eleazerclyde	2025-09-07T15:56:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deft dense snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:56:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deft dense snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757260532	bah63843	2025-09-07T15:56:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:56:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hyunjoonkang/stacking_cube_side_DAVLA_1	hyunjoonkang	2025-09-07T15:55:28Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "smolvla", "dataset:hyunjoonkang/merge_stacking_cube_side", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]	robotics	2025-09-07T15:55:17Z	--- base_model: lerobot/smolvla_base datasets: hyunjoonkang/merge_stacking_cube_side library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - robotics - lerobot - smolvla --- # Model Card for smolvla <!-- Provide a quick summary of what the model is/does. --> [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. 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
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757260473	Stasonelison	2025-09-07T15:55:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:55:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dwirecarmen/blockassist-bc-swift_pawing_ant_1757260449	dwirecarmen	2025-09-07T15:54:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "swift pawing ant", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:54:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - swift pawing ant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
alibidaran/bert_MI_interview_student	alibidaran	2025-09-07T15:54:03Z	9	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-05T14:51:30Z	--- 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]
luthymario/blockassist-bc-trotting_thorny_chameleon_1757260335	luthymario	2025-09-07T15:52:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "trotting thorny chameleon", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:52:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - trotting thorny chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ACECA/lowMvMax_182	ACECA	2025-09-07T15:52:03Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-25T03:56:44Z	--- 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).
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757260025	Stasonelison	2025-09-07T15:47:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:47:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757260023	DiFors	2025-09-07T15:47:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:47:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aarabil/gte-modernbert-base	aarabil	2025-09-07T15:46:51Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-09-07T15:46:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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easygoing0114/flan-t5-xxl-fused	easygoing0114	2025-09-07T15:46:39Z	1,903	30	null	[ "gguf", "t5", "T5xxl", "Google FLAN", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-12-05T10:12:11Z	--- license: apache-2.0 tags: - T5xxl - Google FLAN --- # FLAN-T5-XXL Fused Model ## Guide (External Site): [English](https://www.ai-image-journey.com/2025/03/flan-t5xxl-te-only.html) \| [Japanese](https://note.com/ai_image_journey/n/ncc6b1c475d8f) Why Use FP32 Text Encoder? (External Site): [English](https://www.ai-image-journey.com/2025/08/hidream-flux1-krea.html) \| [Japanese](https://note.com/ai_image_journey/n/n524caae87e96) This repository hosts a fused version of the FLAN-T5-XXL model, created by combining the split files from [Google's FLAN-T5-XXL repository](https://huggingface.co/google/flan-t5-xxl). The files have been merged for convenience, making it easier to integrate into AI applications, including image generation workflows. <div style="display: flex; justify-content: center; align-items: center; gap: 2em;"> <div> <img src="./images/flan_t5_xxl_TE-only_FP32_sample1.png" alt="FLAN-T5-XXL sample image 1" width="400px" height="400px"> </div> <div> <img src="./images/flan_t5_xxl_TE-only_FP32_sample2.png" alt="FLAN-T5-XXL sample image 2" width="400px" height="400px"> </div> </div> Base Model: [blue_pencil-flux1_v0.0.1](https://huggingface.co/bluepen5805/blue_pencil-flux1) ## Key Features - Fused for Simplicity: Combines split model files into a single, ready-to-use format. - Optimized Variants: Available in FP32, FP16, FP8, and quantized GGUF formats to balance accuracy and resource usage. - Enhanced Prompt Accuracy: Outperforms the standard T5-XXL v1.1 in generating precise outputs for image generation tasks. ## Model Variants \| Model \| Size \| SSIM Similarity \| Recommended \| \|-------\|:------:\|:---------------:\|:-----------:\| \| FP32 \| 19 GB \| 100.0% \| 🌟 \| \| FP16 \| 9.6 GB \| 98.0% \| ✅ \| \| FP8 \| 4.8 GB \| 95.3% \| 🔺 \| \| Q8_0 \| 5.1 GB \| 97.6% \| ✅ \| \| Q6_K \| 4.0 GB \| 97.3% \| 🔺 \| \| Q5_K_M\| 3.4 GB \| 94.8% \| \| \| Q4_K_M\| 2.9 GB \| 96.4% \| \| ### Comparison Graph <div style="text-align: center; margin-left: auto; margin-right: auto; width: 600px; max-width: 80%;"> <img src="./images/Flan-T5xxl_TE-only_MAE_SSIM_Similarity.png" alt="FLAN-T5-XXL MAE and SSIM Similarity Graph"> </div> For a detailed comparison, refer to [this blog post](https://www.ai-image-journey.com/2024/12/image-difference-t5xxl-clip-l.html). ## Usage Instructions Place the downloaded model files in one of the following directories: - `models/text_encoder` - `models/clip` - `Models/CLIP` ### ComfyUI When using Flux.1 in ComfyUI, load the text encoder with the DualCLIPLoader node. <div style="text-align: center; margin-left: auto; margin-right: auto; width: 400px; max-width: 80%;"> <img src="./images/screenshot of ComfyUI DualCLIPLoader node.png" alt="Screenshot of ComfyUI DualCLIPLoader node"> </div> As of April 13, 2025, the default DualCLIPLoader node includes a device selection option, allowing you to choose where to load the model: - `cuda` → VRAM - `cpu` → System RAM Since Flux.1’s text encoder is large, setting the device to `cpu` and storing the model in system RAM often improves performance. Unless your system RAM is 16GB or less, keeping the model in system RAM is more effective than GGUF quantization. Thus, GGUF formats offer limited benefits in ComfyUI for most users due to sufficient RAM availability. ([More about ComfyUI settings](https://www.ai-image-journey.com/2025/03/comfyui-setting.html).) You can also use FP32 text encoders for optimal results by enabling the `--fp32-text-enc` argument at startup. ### Stable Diffusion WebUI Forge In Stable Diffusion WebUI Forge, select the FLAN-T5-XXL model instead of the default T5xxl_v1_1 text encoder. <div style="text-align: center; margin-left: auto; margin-right: auto; width: 800px; max-width: 80%;"> <img src="./images/Screenshot of Stable Diffusion WebUI Forge text encoder selection screen.png" alt="Stable Diffusion WebUI Forge Text Encoder Selection Screen"> </div> To use the text encoder in FP32 format, launch Stable Diffusion WebUI Forge with the `--clip-in-fp32` argument. ## Comparison: FLAN-T5-XXL vs T5-XXL v1.1 <div style="display: flex; justify-content: center; align-items: center; gap: 2em;"> <div> <img src="./images/flan_t5_xxl_image.png" alt="FLAN-T5-XXL Image" width="400px" height="400px"> </div> <div> <img src="./images/t5_xxl_v1_1_image.png" alt="T5-XXL v1.1 Image" width="400px" height="400px"> </div> </div> These example images were generated using FLAN-T5-XXL and [T5-XXL v1.1](https://huggingface.co/google/t5-v1_1-xxl) models in Flux.1. FLAN-T5-XXL delivers more accurate responses to prompts. ## Further Comparisons - [FLAN-T5-XXL vs T5-XXL v1.1](https://www.ai-image-journey.com/2024/12/clip-t5xxl-text-encoder.html) - [FLAN-T5-XXL FP32 vs FP16 and Quantization](https://www.ai-image-journey.com/2024/12/image-difference-t5xxl-clip-l.html) --- ## License - This model is distributed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). - The uploader claims no ownership or rights over the model. --- ## Update History ### August 22, 2025 Add Why Use FP32 Text Encoder? ### July 24, 2025 Re-upload of the GGUF model, reduction in model size, and correction of metadata. ### July 6, 2025 Uploaded flan_t5_xxl_full_FP8 models. ### April 20, 2025 Updated Stable Diffusion WebUI Forge FP32 launch argument. ### April 15, 2025 Updated content to reflect ComfyUI updates. ### March 20, 2025 Updated FLAN-T5-XXL model list and table.
rettertop/blockassist-bc-scampering_howling_hyena_1757259917	rettertop	2025-09-07T15:46:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scampering howling hyena", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:45:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scampering howling hyena --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/aquif-3-mini-i1-GGUF	mradermacher	2025-09-07T15:45:58Z	123	0	transformers	[ "transformers", "gguf", "language", "aquif", "text-generation-inference", "math", "coding", "small", "pt", "en", "ja", "zh", "th", "es", "hi", "fr", "de", "it", "base_model:aquif-ai/aquif-3-mini", "base_model:quantized:aquif-ai/aquif-3-mini", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-06T04:26:59Z	--- base_model: aquif-ai/aquif-3-mini language: - pt - en - ja - zh - th - es - hi - fr - de - it library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - language - aquif - text-generation-inference - math - coding - small --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/aquif-ai/aquif-3-mini <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#aquif-3-mini-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/aquif-3-mini-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q2_K.gguf) \| i1-Q2_K \| 1.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ3_M.gguf) \| i1-IQ3_M \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 1.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.0 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/aquif-3-mini-i1-GGUF/resolve/main/aquif-3-mini.i1-Q6_K.gguf) \| i1-Q6_K \| 2.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
DiFors/blockassist-bc-singing_sizable_snake_1757259861	DiFors	2025-09-07T15:44:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:44:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757259766	bah63843	2025-09-07T15:43:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:43:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
randgaardcyndi/blockassist-bc-sneaky_pudgy_nightingale_1757259750	randgaardcyndi	2025-09-07T15:42:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sneaky pudgy nightingale", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:42:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sneaky pudgy nightingale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vendi11/blockassist-bc-placid_placid_llama_1757259727	vendi11	2025-09-07T15:42:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "placid placid llama", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:42:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - placid placid llama --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dombekgordon/blockassist-bc-stinky_stubby_donkey_1757259658	dombekgordon	2025-09-07T15:42:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stinky stubby donkey", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:41:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stinky stubby donkey --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Miracle-man/blockassist-bc-singing_lithe_koala_1757257671	Miracle-man	2025-09-07T15:41:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing lithe koala", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:41:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing lithe koala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
internetyouknow/ddd	internetyouknow	2025-09-07T15:41:26Z	0	0	null	[ "license:other", "region:us" ]	null	2025-09-07T15:37:42Z	--- license: other license_name: other license_link: LICENSE ---
bah63843/blockassist-bc-plump_fast_antelope_1757259614	bah63843	2025-09-07T15:41:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:40:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jopergil/blockassist-bc-feline_agile_mink_1757259621	jopergil	2025-09-07T15:40:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "feline agile mink", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:40:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - feline agile mink --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
beaudrieflorencio/blockassist-bc-barky_invisible_butterfly_1757259612	beaudrieflorencio	2025-09-07T15:40:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "barky invisible butterfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:40:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - barky invisible butterfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757259535	DiFors	2025-09-07T15:39:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:39:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jopergil/blockassist-bc-climbing_masked_kangaroo_1757259508	jopergil	2025-09-07T15:38:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "climbing masked kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:38:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - climbing masked kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757259487	DiFors	2025-09-07T15:38:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:38:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757259466	bah63843	2025-09-07T15:38:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:38:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757259465	DiFors	2025-09-07T15:38:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:38:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
KingEmpire/King105_De_090705	KingEmpire	2025-09-07T15:38:15Z	0	0	null	[ "region:us" ]	null	2025-09-07T03:38:11Z	# Container Template for SoundsRight Subnet Miners This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively. This container has only been tested with Ubuntu 24.04 and CUDA 12.6. It may run on other configurations, but it is not guaranteed. To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt. Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html). Verify that the CDI specification was done correctly with: ``` $ nvidia-ctk cdi list ``` You should see this in your output: ``` nvidia.com/gpu=all nvidia.com/gpu=0 ``` If you are running podman as root, run the following command to start the container: Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` If you are running the container rootless, there are a few more changes to make: First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters: ``` [nvidia-container-cli] no-cgroups = true [nvidia-container-runtime] debug = "/tmp/nvidia-container-runtime.log" ``` You can also run the following command to achieve the same result: ``` $ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place ``` Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` Running the container will spin up an API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Download model checkpoint and initialize model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files By default the API will use host `0.0.0.0` and port `6500`. ### References 1. Welker, Simon; Richter, Julius; Gerkmann, Timo Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain. Proceedings of Interspeech 2022, 2022, pp. 2928–2932. [DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653) 2. Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo Speech Enhancement and Dereverberation with Diffusion-based Generative Models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, 2023, pp. 2351–2364. [DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241) 3. Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation. Proceedings of ISCA Interspeech, 2024, pp. 4873–4877.
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757259395	Stasonelison	2025-09-07T15:37:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:37:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
smittleirwin/blockassist-bc-prehistoric_lanky_emu_1757259382	smittleirwin	2025-09-07T15:36:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "prehistoric lanky emu", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:36:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - prehistoric lanky emu --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DFQ-Dojo/swin-s-w8a8	DFQ-Dojo	2025-09-07T15:36:29Z	0	0	dfq-toolkit	[ "dfq-toolkit", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "arxiv:2507.16782", "region:us" ]	null	2025-09-07T15:24:08Z	--- library_name: dfq-toolkit tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: https://github.com/DFQ-Dojo/dfq-toolkit - Paper: https://arxiv.org/abs/2507.16782 - Docs: [More Information Needed]
zcopwerq/blockassist-bc-arctic_pouncing_beaver_1757259336	zcopwerq	2025-09-07T15:35:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "arctic pouncing beaver", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:35:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - arctic pouncing beaver --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
FinalWork/Flexibility_1.3B_FiveInstruction_Working	FinalWork	2025-09-07T15:34:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-07T15:34:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
GEODE/mt5-small-coords-norm	GEODE	2025-09-07T15:33:55Z	0	0	null	[ "safetensors", "mt5", "text-generation", "fr", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:cc-by-nc-4.0", "region:us" ]	text-generation	2025-09-07T15:24:44Z	--- license: cc-by-nc-4.0 language: - fr base_model: - google/mt5-small pipeline_tag: text-generation ---
DiFors/blockassist-bc-singing_sizable_snake_1757259189	DiFors	2025-09-07T15:33:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:33:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757259158	DiFors	2025-09-07T15:33:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:33:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757259163	bah63843	2025-09-07T15:33:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:33:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Vasya777/blockassist-bc-lumbering_enormous_sloth_1757259086	Vasya777	2025-09-07T15:32:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:32:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
giovannidemuri/llama8b-er-v595-seed2-hx_lora	giovannidemuri	2025-09-07T15:31:39Z	19	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-06T17:02:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
zcopwerq/blockassist-bc-fanged_hunting_ram_1757259017	zcopwerq	2025-09-07T15:30:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fanged hunting ram", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:30:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fanged hunting ram --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757258938	DiFors	2025-09-07T15:29:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:29:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MAT1980/MAT_Chatbot	MAT1980	2025-09-07T15:28:31Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-07T15:28:31Z	--- license: apache-2.0 ---
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757258797	Stasonelison	2025-09-07T15:27:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:27:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
FinalWork/Flexibility_1.3B_ThreeInstruction_Working	FinalWork	2025-09-07T15:27:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-07T15:26:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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ghostai1/ccengine1	ghostai1	2025-09-07T15:26:58Z	0	0	null	[ "region:us" ]	null	2025-03-12T01:36:58Z	--- license: mit title: Customer Experience Bot Demo sdk: gradio colorFrom: purple colorTo: green short_description: CX AI LLM ---# Mario AI Demo A sophisticated AI-powered demo of a Mario game environment, showcasing advanced gameplay mechanics and intelligent agent behaviors. Built with over 5 years of AI expertise since 2020, this demo leverages reinforcement learning (RL) and heuristic algorithms to create a dynamic Mario experience. Deployed on Hugging Face as a Model repository (free tier), it demonstrates AI-driven pathfinding, enemy tactics, and gameplay optimization for educational and research purposes in gaming AI, suitable for applications in EdTech, GameDev, and AI research. ## Technical Architecture ### AI Pathfinding and Gameplay Pipeline The core of this demo is a hybrid AI system combining reinforcement learning and rule-based heuristics to control Mario’s actions: - Reinforcement Learning (RL) Agent: - Utilizes a Proximal Policy Optimization (PPO) algorithm, fine-tuned on a custom Mario environment. - Trained to optimize for coin collection, enemy avoidance, and level completion, achieving a simulated 90% level completion rate. - Model size: Lightweight (~50MB), compatible with free-tier CPU deployment. - Heuristic Pathfinding: - Implements A* pathfinding algorithm for efficient navigation through game levels. - Incorporates dynamic obstacle avoidance (e.g., Goombas, Koopas) using real-time collision detection. - Enemy Tactics: - Enemies (e.g., Goombas) use rule-based AI with adaptive difficulty, increasing challenge as Mario progresses. - Tactics include speed variation, ambush patterns, and predictive movement based on Mario’s position. - Gameplay Enhancements: - Jump controls tweaked for precision using physics-based adjustments. - Power-up distribution system optimized with probability-based spawning (e.g., 20% chance for Super Mushroom). - Adaptive weather effects (e.g., rain, wind) impacting Mario’s movement and enemy behavior. ### Data Preprocessing for Game State The demo processes game state data to train and run the AI: - State Representation: - Game screen pixels converted to a 2D grid (84x84) for RL input. - Features extracted: Mario’s position, enemy positions, power-up locations, and level layout. - Preprocessing Pipeline: - Normalization: Pixel values scaled to [0, 1] for RL model stability. - Frame Stacking: Stacks 4 consecutive frames to capture temporal dynamics (e.g., Mario’s velocity). - Reward Shaping: Custom rewards for coin collection (+10), enemy defeat (+50), and level completion (+1000). - Output: Cleaned state data stored as `mario_states.csv` for training and inference. ### Enterprise-Grade AI Compatibility The processed data and AI model are optimized for: - Amazon SageMaker: Ready for training RL models (e.g., PPO, DQN) using SageMaker RL toolkit, deployable via SageMaker JumpStart. - Azure AI: Compatible with Azure Machine Learning for fine-tuning RL agents in Azure Blob Storage, enabling scalable game AI research. - FastAPI Integration: Designed for API-driven inference (e.g., REST endpoints for AI actions), leveraging your experience with FastAPI. ## Performance Monitoring and Visualization The demo includes a performance monitoring suite: - Latency Tracking: Measures pathfinding, enemy decision-making, and gameplay update times using `time.perf_counter()`, reported in milliseconds. - Success Metrics: Tracks level completion rate (90% simulated) and coins collected per run. - Visualization: Uses Matplotlib to plot a performance chart (`mario_metrics.png`): - Bar Chart: Latency (ms) per stage (Pathfinding, Enemy AI, Gameplay Update). - Line Chart: Success rate (%) per run, with a vibrant palette for engaging visuals. ## Gradio Interface for Interactive Demo The demo is accessible via Gradio, providing an interactive Mario AI experience: - Input: Select a level (e.g., "Level 1-1") and AI mode (e.g., "Exploration", "Speedrun"). - Outputs: - Live Gameplay: Simulated Mario gameplay showing AI-controlled actions (e.g., jumps, enemy avoidance). - Metrics Display: Real-time stats (coins collected, enemies defeated, completion time). - Performance Plot: Visual metrics for latency and success rate. - Styling: Custom dark theme CSS (`#2a2a2a` background, blue buttons) for a sleek, gaming-inspired UI. ## Setup - Clone this repository to a Hugging Face Model repository (free tier, public). - Add `requirements.txt` with dependencies (`gradio==4.44.0`, `matplotlib==3.9.2`, etc.). - Upload `app.py` (includes embedded game environment for seamless deployment). - Configure to run with Python 3.9+, CPU hardware (no GPU). ## Usage - Select Level: Choose a Mario level in the Gradio UI (e.g., "Level 1-1"). - Select AI Mode: Pick an AI behavior mode (e.g., "Exploration" for coin collection, "Speedrun" for fastest completion). - Output: - Gameplay Simulation: Watch Mario navigate the level, avoiding enemies and collecting coins. - Metrics: “Coins: 15, Enemies Defeated: 3, Completion Time: 45s”. - Performance Plot: Visual metrics for latency and success rate. Example: - Level: "Level 1-1" - AI Mode: "Speedrun" - Output: - Gameplay: Mario completes the level in 40 seconds, collecting 10 coins and defeating 2 Goombas. - Metrics: “Coins: 10, Enemies Defeated: 2, Completion Time: 40s”. - Plot: Latency (Pathfinding: 5ms, Enemy AI: 3ms, Gameplay Update: 2ms), Success Rate: 92%. ## Technical Details Stack: - Gym Environment: Custom Mario environment (`gym-super-mario-bros`) for RL training and simulation. - RL Agent: PPO implementation using Stable-Baselines3 for lightweight, CPU-friendly training. - Pathfinding: A* algorithm with dynamic obstacle avoidance. - Gradio: Interactive UI for real-time gameplay demos. - Matplotlib: Performance visualization with bar and line charts. - FastAPI Compatibility: Designed for API-driven inference, leveraging your experience with FastAPI. Free Tier Optimization: Lightweight with CPU-only dependencies, no GPU required. Extensibility: Ready for integration with game engines (e.g., Unity) via FastAPI, and cloud deployments on AWS Lambda or Azure Functions. ## Purpose This demo showcases expertise in AI-driven game development, focusing on Mario AI pathfinding, enemy tactics, and gameplay optimization. Built on over 5 years of experience in AI, RL, and enterprise-grade deployments, it demonstrates the power of hybrid AI systems (RL + heuristics) for gaming applications, making it ideal for EdTech, GameDev, and AI research. ## Future Enhancements - LLM Integration: Incorporate lightweight LLMs (e.g., distilgpt2) for dynamic NPC dialogue generation. - FastAPI Deployment: Expose AI pipeline via FastAPI endpoints for production-grade inference. - Multiplayer Support: Extend to multiplayer co-op mode with competing AI agents. - Real-Time Monitoring: Add Prometheus metrics for gameplay performance in production environments. Website: https://ghostainews.com/ Discord: https://discord.gg/BfA23aYz ## Latest Update Status Update: Status Update: Optimized collision detection for smoother interactions - May 28, 2025 📝 - Enhanced NPC dialogue with dynamic responses - September 07, 2025 📝 - Optimized collision detection for smoother interactions ⭐ - September 05, 2025 📝 - Upgraded power-up distribution system 🎉 - September 04, 2025 📝 - Introduced adaptive weather in game levels - September 02, 2025 📝 - Tweaked jump controls for improved accuracy - August 31, 2025 📝 - Added fresh enemy tactics for extra difficulty 🏰 - August 30, 2025 📝 - Refined AI pathfinding for seamless gameplay 🪙 - August 28, 2025 📝 - Added support for multiplayer co-op mode - August 26, 2025 📝 - Improved level loading times by 30% - August 25, 2025 📝 - Integrated new collectible items for bonus challenges ✨ - August 23, 2025 📝 - Enhanced NPC dialogue with dynamic responses 🎩 - August 21, 2025 📝 - Optimized collision detection for smoother interactions 🔥 - August 20, 2025 📝 - Upgraded power-up distribution system - August 18, 2025 📝 - Introduced adaptive weather in game levels 🌈 - August 16, 2025 📝 - Tweaked jump controls for improved accuracy - August 15, 2025 📝 - Added fresh enemy tactics for extra difficulty 🔥 - August 14, 2025 📝 - Refined AI pathfinding for seamless gameplay - August 13, 2025 📝 - Added support for multiplayer co-op mode - August 12, 2025 📝 - Improved level loading times by 30% ⚡ - August 11, 2025 📝 - Integrated new collectible items for bonus challenges - August 10, 2025 📝 - Enhanced NPC dialogue with dynamic responses 🍄 - August 09, 2025 📝 - Optimized collision detection for smoother interactions 🎩 - August 08, 2025 📝 - Upgraded power-up distribution system 🪙 - August 07, 2025 📝 - Introduced adaptive weather in game levels - August 06, 2025 📝 - Tweaked jump controls for improved accuracy 🎉 - August 05, 2025 📝 - Added fresh enemy tactics for extra difficulty - August 04, 2025 📝 - Refined AI pathfinding for seamless gameplay - August 03, 2025 📝 - Added support for multiplayer co-op mode 🌈 - August 02, 2025 📝 - Improved level loading times by 30% ⭐ - August 01, 2025 📝 - Integrated new collectible items for bonus challenges 🏰 - July 31, 2025 📝 - Enhanced NPC dialogue with dynamic responses - July 30, 2025 📝 - Optimized collision detection for smoother interactions - July 29, 2025 📝 - Upgraded power-up distribution system - July 28, 2025 📝 - Introduced adaptive weather in game levels ✨ - July 27, 2025 📝 - Tweaked jump controls for improved accuracy ⚡ - July 26, 2025 📝 - Added fresh enemy tactics for extra difficulty 🎉 - July 25, 2025 📝 - Refined AI pathfinding for seamless gameplay - July 24, 2025 📝 - Added support for multiplayer co-op mode - July 23, 2025 📝 - Improved level loading times by 30% - July 22, 2025 📝 - Integrated new collectible items for bonus challenges 🏰 - July 21, 2025 📝 - Enhanced NPC dialogue with dynamic responses - July 20, 2025 📝 - Optimized collision detection for smoother interactions ⭐ - July 19, 2025 📝 - Upgraded power-up distribution system - July 18, 2025 📝 - Introduced adaptive weather in game levels - July 17, 2025 📝 - Tweaked jump controls for improved accuracy 🔥 - July 16, 2025 📝 - Added fresh enemy tactics for extra difficulty 🎩 - July 15, 2025 📝 - Refined AI pathfinding for seamless gameplay 🍄 - July 14, 2025 📝 - Added support for multiplayer co-op mode - July 11, 2025 📝 - Improved level loading times by 30% 🪙 - July 10, 2025 📝 - Integrated new collectible items for bonus challenges - July 09, 2025 📝 - Enhanced NPC dialogue with dynamic responses ✨ - July 08, 2025 📝 - Optimized collision detection for smoother interactions 🌈 - July 07, 2025 📝 - Upgraded power-up distribution system ⭐ - July 06, 2025 📝 - Introduced adaptive weather in game levels - July 05, 2025 📝 - Tweaked jump controls for improved accuracy 🏰 - July 04, 2025 📝 - Added fresh enemy tactics for extra difficulty ✨ - July 03, 2025 📝 - Refined AI pathfinding for seamless gameplay 🪙 - July 02, 2025 📝 - Added support for multiplayer co-op mode 🍄 - July 01, 2025 📝 - Improved level loading times by 30% ⚡ - June 30, 2025 📝 - Integrated new collectible items for bonus challenges 🌈 - June 29, 2025 📝 - Enhanced NPC dialogue with dynamic responses 🎉 - June 28, 2025 📝 - Optimized collision detection for smoother interactions - June 27, 2025 📝 - Upgraded power-up distribution system - June 26, 2025 📝 - Introduced adaptive weather in game levels 🔥 - June 25, 2025 📝 - Tweaked jump controls for improved accuracy 🎩 - June 24, 2025 📝 - Added fresh enemy tactics for extra difficulty - June 23, 2025 📝 - Refined AI pathfinding for seamless gameplay ✨ - June 22, 2025 📝 - Added support for multiplayer co-op mode 🔥 - June 21, 2025 📝 - Improved level loading times by 30% 🎉 - June 20, 2025 📝 - Integrated new collectible items for bonus challenges 🍄 - June 19, 2025 📝 - Enhanced NPC dialogue with dynamic responses - June 18, 2025 📝 - Optimized collision detection for smoother interactions ⭐ - June 17, 2025 📝 - Upgraded power-up distribution system - June 16, 2025 📝 - Introduced adaptive weather in game levels - June 15, 2025 📝 - Tweaked jump controls for improved accuracy 🪙 - June 14, 2025 📝 - Added fresh enemy tactics for extra difficulty - June 13, 2025 📝 - Refined AI pathfinding for seamless gameplay - June 12, 2025 📝 - Added support for multiplayer co-op mode 🌈 - June 11, 2025 📝 - Improved level loading times by 30% ⚡ - June 10, 2025 📝 - Integrated new collectible items for bonus challenges - June 09, 2025 📝 - Enhanced NPC dialogue with dynamic responses 🎩 - June 08, 2025 📝 - Optimized collision detection for smoother interactions - June 07, 2025 📝 - Upgraded power-up distribution system 🏰 - June 06, 2025 📝 - Introduced adaptive weather in game levels 🏰 - June 05, 2025 📝 - Tweaked jump controls for improved accuracy ⭐ - June 04, 2025 📝 - Added fresh enemy tactics for extra difficulty 🎉 - June 03, 2025 📝 - Refined AI pathfinding for seamless gameplay - June 02, 2025 📝 - Added support for multiplayer co-op mode ✨ - June 01, 2025 📝 - Improved level loading times by 30% - May 31, 2025 📝 - Integrated new collectible items for bonus challenges ⚡ - May 30, 2025 📝 - Enhanced NPC dialogue with dynamic responses 🔥 - May 29, 2025 📝 - Optimized collision detection for smoother interactions - Upgraded power-up distribution system 🎩 - Introduced adaptive weather in game levels 🪙 - Tweaked jump controls for improved accuracy 🍄 - Added fresh enemy tactics for extra difficulty - Refined AI pathfinding for seamless gameplay 🌈 - Added support for multiplayer co-op mode 🎩 - Improved level loading times by 30% ✨ - Integrated new collectible items for bonus challenges 🍄 - Enhanced NPC dialogue with dynamic responses 🌈 - Optimized collision detection for smoother interactions - Upgraded power-up distribution system 🪙 - Introduced adaptive weather in game levels - Tweaked jump controls for improved accuracy - Added fresh enemy tactics for extra difficulty - Refined AI pathfinding for seamless gameplay 🔥 - Added support for multiplayer co-op mode 🎉 - Improved level loading times by 30% - Integrated new collectible items for bonus challenges - Enhanced NPC dialogue with dynamic responses ⭐ - Optimized collision detection for smoother interactions - Upgraded power-up distribution system - Introduced adaptive weather in game levels - Tweaked jump controls for improved accuracy - Added fresh enemy tactics for extra difficulty - Refined AI pathfinding for seamless gameplay - Added support for multiplayer co-op mode - Improved level loading times by 30% - Integrated new collectible items for bonus challenges ⚡ - Enhanced NPC dialogue with dynamic responses 🏰 - Optimized collision detection for smoother interactions - Upgraded power-up distribution system - Introduced adaptive weather in game levels - Tweaked jump controls for improved accuracy - Added fresh enemy tactics for extra difficulty
bah63843/blockassist-bc-plump_fast_antelope_1757258726	bah63843	2025-09-07T15:26:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:26:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
keke0130/gemma-3-270m-chinese-title-generator	keke0130	2025-09-07T15:25:15Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "gemma", "fine-tuned", "chinese", "conversational", "zh", "dataset:keke0130/chinese_title_generation_gpt_oss_20b", "base_model:google/gemma-3-270m", "base_model:finetune:google/gemma-3-270m", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-07T14:30:23Z	--- license: apache-2.0 datasets: - keke0130/chinese_title_generation_gpt_oss_20b language: - zh base_model: - google/gemma-3-270m pipeline_tag: text-generation library_name: transformers tags: - gemma - fine-tuned - chinese --- # Gemma-3-270M 中文對話標題生成模型 💬➡️🏷️ 這是一個基於 `google/gemma-3-270m` 微調的語言模型，專門用於從中文的使用者對話中，生成一個簡潔的標題。 (經過測試，該模型有可能出現繁體/簡體混著輸出的狀況) ## 📊 數據 (Dataset) * 本模型使用了 [keke0130/chinese_title_generation_gpt_oss_20b](https://huggingface.co/datasets/keke0130/chinese_title_generation_gpt_oss_20b) 資料集進行微調。 * 這個資料集基於以下來源構建： * 對話內容 (Prompt): 來自 [Mxode/Chinese-Instruct](https://huggingface.co/datasets/Mxode/Chinese-Instruct) * 標題 (Response): 由 [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) 生成 ## 🚀 快速上手 (Quick Start) ⚠️ 重要提示: 本模型需要遵循特定的提示格式才能獲得最佳效果。請確保您的輸入嚴格遵循以下模板。 ### 1. 🖥️ 使用 GGUF (推薦用於本地部署) * GGUF 模型倉庫: [keke0130/gemma-3-270m-chinese-title-generator-gguf](https://huggingface.co/keke0130/gemma-3-270m-chinese-title-generator-gguf/) * 適用工具: `llama.cpp`, `Ollama`, `LM Studio` 等。 * 📝 Jinja 提示模板: 在您的應用程式中（如 LM Studio），請手動設定以下 Jinja 模板： ```jinja {% for message in messages %}{% if message['role'] == 'user' %}### 指令: 請根據以下使用者對話，生成一個簡潔、準確的標題。 ### 使用者對話: {{ message['content'] }} ### 生成的標題: {% elif message['role'] == 'assistant' %}{{ message['content'] }}{% endif %}{% endfor %} ``` ### 2. 🐍 使用 `transformers` (適用於 Python 環境) ```python from transformers import pipeline import torch model_id = "keke0130/gemma3-270m-chinese-title-generator" pipe = pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) # 準備您的對話內容 dialogue = "你好，我想請問一下你們的退貨政策是什麼？我上週買的商品好像有點問題。另外，客服電話是多少？" # 手動構建提示 prompt_template = f"""### 指令: 請根據以下使用者對話，生成一個簡潔、準確的標題。 ### 使用者對話: {dialogue} ### 生成的標題: """ # 進行推論 outputs = pipe(prompt_template, max_new_tokens=50, do_sample=False) # 清理並提取結果 # 我們需要手動去除 prompt 部分，只留下模型新生成的內容 generated_text = outputs['generated_text'][len(prompt_template):].strip() print(f"💬 原始對話: {dialogue}") print(f"🏷️ 模型生成標題: {generated_text}")
bah63843/blockassist-bc-plump_fast_antelope_1757258583	bah63843	2025-09-07T15:23:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:23:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DiFors/blockassist-bc-singing_sizable_snake_1757258512	DiFors	2025-09-07T15:22:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "singing sizable snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:22:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - singing sizable snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vanlewcary/blockassist-bc-alert_silky_capybara_1757258526	vanlewcary	2025-09-07T15:22:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "alert silky capybara", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:22:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - alert silky capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
abattiebonie/blockassist-bc-slithering_sly_vulture_1757258491	abattiebonie	2025-09-07T15:21:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "slithering sly vulture", "arxiv:2504.07091", "region:us" ]	null	2025-09-07T15:21:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - slithering sly vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

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