librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
koloni/blockassist-bc-deadly_graceful_stingray_1755827045	koloni	2025-08-22T02:10:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T02:10:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Mostefa-Terbeche/diabetic-retinopathy-messidor-vit_b_16-original-20250718-190249	Mostefa-Terbeche	2025-08-22T02:08:42Z	0	0	null	[ "diabetic-retinopathy", "medical-imaging", "pytorch", "computer-vision", "retinal-imaging", "dataset:messidor", "license:apache-2.0", "model-index", "region:us" ]	null	2025-08-21T22:57:43Z	--- license: apache-2.0 tags: - diabetic-retinopathy - medical-imaging - pytorch - computer-vision - retinal-imaging datasets: - messidor metrics: - accuracy - quadratic-kappa - auc model-index: - name: messidor_vit_b_16_original results: - task: type: image-classification name: Diabetic Retinopathy Classification dataset: type: messidor name: MESSIDOR metrics: - type: accuracy value: 0.3505747126436782 - type: quadratic-kappa value: 0.42545814759782075 --- # Diabetic Retinopathy Classification Model ## Model Description This model is trained for diabetic retinopathy classification using the vit_b_16 architecture on the messidor dataset with original preprocessing. ## Model Details - Architecture: vit_b_16 - Dataset: messidor - Preprocessing: original - Training Date: 20250718-190249 - Task: 5-class diabetic retinopathy grading (0-4) - Directory: messidor_vit_b_16_20250718-190249_new ## Performance - Test Accuracy: 0.3505747126436782 - Test Quadratic Kappa: 0.42545814759782075 - Validation Kappa: 0.42545814759782075 ## Usage ```python import torch from huggingface_hub import hf_hub_download # Download model model_path = hf_hub_download( repo_id="your-username/diabetic-retinopathy-messidor-vit_b_16-original", filename="model_best.pt" ) # Load model model = torch.load(model_path, map_location='cpu') ``` ## Classes - 0: No DR (No diabetic retinopathy) - 1: Mild DR (Mild non-proliferative diabetic retinopathy) - 2: Moderate DR (Moderate non-proliferative diabetic retinopathy) - 3: Severe DR (Severe non-proliferative diabetic retinopathy) - 4: Proliferative DR (Proliferative diabetic retinopathy) ## Citation If you use this model, please cite your research paper/thesis.
kernels-community/megablocks	kernels-community	2025-08-22T02:08:36Z	0	3	null	[ "kernel", "license:apache-2.0", "region:us" ]	null	2025-06-10T17:43:23Z	--- license: apache-2.0 tags: - kernel --- ## Quickstart ```bash uv run https://huggingface.co/kernels-community/megablocks/raw/main/readme_example.py ``` ```python # /// script # requires-python = "==3.10" # dependencies = [ # "numpy", # "kernels", # "torch" # ] # /// import torch from collections import namedtuple from kernels import get_kernel # Make reproducible torch.manual_seed(42) torch.cuda.manual_seed(42) # Download optimized kernels from the Hugging Face hub megablocks = get_kernel("kernels-community/megablocks") print("MegaBlocks kernel downloaded successfully.") model = megablocks.layers.MegaBlocksMoeMLP() model.experts = namedtuple("Experts", ["gate_up_proj", "gate_down_proj", "down_proj", "hidden_size"]) print("MegaBlocksMoeMLP instance created successfully.") # Config ne, hs, isz = 128, 1152, 3072 # Router with proper initialization model.router = torch.nn.Linear(hs, ne, device="cuda") torch.nn.init.kaiming_uniform_(model.router.weight) # Expert layers with realistic weights e = model.experts e.gate_up_proj = torch.nn.Parameter(torch.randn(ne, hs, isz, device="cuda") * 0.02) e.gate_up_proj_bias = torch.nn.Parameter(torch.zeros(ne, isz, device="cuda")) e.down_proj = torch.nn.Parameter(torch.randn(ne, 1536, hs, device="cuda") * 0.02) e.down_proj_bias = torch.nn.Parameter(torch.zeros(ne, hs, device="cuda")) e.hidden_size = hs print("Expert layers initialized successfully.") # Test with normalized input x = torch.randn(1, 1, hs, device="cuda") * 0.1 output, expert_weights = model(x) print("Model forward pass completed successfully.") print(f"Output shape: {output.shape}") print(f"Output range: [{output.min():.3f}, {output.max():.3f}]") print(f"Output: {output.flatten()[:10]}") print(f"Expert weights sum: {expert_weights.sum():.3f}") ```
alaabh/llama3.1-deepseek-r1-medical-merged-16bit-GGUF	alaabh	2025-08-22T02:08:12Z	0	0	null	[ "gguf", "llama", "medical", "deepseek", "quantized", "base_model:alaabh/llama3.1-deepseek-r1-medical-merged-16bit", "base_model:quantized:alaabh/llama3.1-deepseek-r1-medical-merged-16bit", "license:apache-2.0", "region:us", "conversational" ]	null	2025-08-22T01:53:02Z	--- license: apache-2.0 base_model: alaabh/llama3.1-deepseek-r1-medical-merged-16bit tags: - llama - gguf - medical - deepseek - quantized inference: false --- # Llama 3.1 DeepSeek R1 Medical - GGUF This is a GGUF conversion of [alaabh/llama3.1-deepseek-r1-medical-merged-16bit](https://huggingface.co/alaabh/llama3.1-deepseek-r1-medical-merged-16bit). ## Model Details - Format: GGUF - Precision: F16 - Size: ~10.6GB - Compatible with: llama.cpp, Ollama, LM Studio, and other GGUF-supported inference engines ## Usage ### With llama.cpp: ```bash ./llama-cli -m model.gguf -p "What is hypertension?" -n 100 ``` ### With Ollama: ```bash # Create Modelfile echo 'FROM ./model.gguf' > Modelfile ollama create medical-llama -f Modelfile ollama run medical-llama ``` ## Original Model This GGUF conversion is based on the merged 16-bit model that combines Llama 3.1 with DeepSeek R1 for medical applications.
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755826985	lisaozill03	2025-08-22T02:07:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T02:07:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755828381	roeker	2025-08-22T02:07:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T02:06:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755826696	kojeklollipop	2025-08-22T02:06:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T02:06:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755826543	calegpedia	2025-08-22T02:03:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T02:03:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755826403	katanyasekolah	2025-08-22T02:02:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T02:02:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1755828069	liukevin666	2025-08-22T02:02:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T02:02:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755828074	roeker	2025-08-22T02:01:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T02:01:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
S3CUR/Behemoth-R1-123B-v2-6bpw-h6-exl2	S3CUR	2025-08-22T01:59:27Z	0	0	null	[ "safetensors", "mistral", "base_model:TheDrummer/Behemoth-R1-123B-v2", "base_model:quantized:TheDrummer/Behemoth-R1-123B-v2", "6-bit", "exl2", "region:us" ]	null	2025-08-22T01:12:44Z	--- base_model: - TheDrummer/Behemoth-R1-123B-v2 --- Quant of TheDrummer/Behemoth-R1-123B-v2 at 6bpw h6 in exl2 for tabbyapi. Runs great on 5 x 3090 or equivilant at 32k ctx and tensor parallel (see included tabbyapi model config overrides) using Largestral R1 text completion presets.
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755827728	IvanJAjebu	2025-08-22T01:56:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:56:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cozymon/Qwen3-0.6B-Gensyn-Swarm-dense_lanky_caribou	cozymon	2025-08-22T01:55:20Z	14	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am dense_lanky_caribou", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-07-06T01:44:33Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am dense_lanky_caribou --- # 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]
marstonWW/t5-medquad-finetuned	marstonWW	2025-08-22T01:53:52Z	2	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-04-25T06:42:24Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-base tags: - generated_from_trainer model-index: - name: t5-medquad-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-medquad-finetuned This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.55.3 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4
joshm14/gemma-3-270m-lora-adapter_new	joshm14	2025-08-22T01:52:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-22T01:52:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755827419	IvanJAjebu	2025-08-22T01:51:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:51:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755826300	Sayemahsjn	2025-08-22T01:49:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:49:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1755827270	liukevin666	2025-08-22T01:49:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:48:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
REYZEUS/Projeto	REYZEUS	2025-08-22T01:44:04Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-22T01:44:04Z	--- license: apache-2.0 ---
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755825450	ihsanridzi	2025-08-22T01:43:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:43:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755825352	coelacanthxyz	2025-08-22T01:43:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:43:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1755826788	liukevin666	2025-08-22T01:41:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:40:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755825186	manusiaperahu2012	2025-08-22T01:39:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:39:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755826630	IvanJAjebu	2025-08-22T01:38:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:38:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
unitova/blockassist-bc-zealous_sneaky_raven_1755824945	unitova	2025-08-22T01:35:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:35:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755825044	lisaozill03	2025-08-22T01:35:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:35:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
RyotaYoneyamaNN/act_so101_rabbit2bear_lab	RyotaYoneyamaNN	2025-08-22T01:34:46Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "act", "dataset:RyotaYoneyamaNN/move_rabbit2bear_lab", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-22T01:34:34Z	--- datasets: RyotaYoneyamaNN/move_rabbit2bear_lab library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - robotics - lerobot - act --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755826249	IvanJAjebu	2025-08-22T01:31:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:31:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755825935	roeker	2025-08-22T01:26:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:26:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lautan/blockassist-bc-gentle_patterned_goat_1755824482	lautan	2025-08-22T01:26:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:26:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
chainway9/blockassist-bc-untamed_quick_eel_1755824351	chainway9	2025-08-22T01:26:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:26:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755825618	roeker	2025-08-22T01:21:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:20:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
New-Clip-Erin-bugis-viral-video-links/New.full.videos.erin.bugis.Viral.Video.Official.Tutorial	New-Clip-Erin-bugis-viral-video-links	2025-08-22T01:20:30Z	0	0	null	[ "region:us" ]	null	2025-08-22T01:20:18Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755825508	IvanJAjebu	2025-08-22T01:19:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:19:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755823899	hakimjustbao	2025-08-22T01:18:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:18:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ChenWu98/statement_deepseek_v1.5_sft_cluster_soft_split_0	ChenWu98	2025-08-22T01:18:30Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:deepseek-ai/DeepSeek-Prover-V1.5-SFT", "base_model:finetune:deepseek-ai/DeepSeek-Prover-V1.5-SFT", "endpoints_compatible", "region:us" ]	null	2025-08-21T03:22:38Z	--- base_model: deepseek-ai/DeepSeek-Prover-V1.5-SFT library_name: transformers model_name: statement_deepseek_v1.5_sft_cluster_soft_split_0 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for statement_deepseek_v1.5_sft_cluster_soft_split_0 This model is a fine-tuned version of [deepseek-ai/DeepSeek-Prover-V1.5-SFT](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-SFT). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chenwu/huggingface/runs/tij0kj92) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.51.1 - Pytorch: 2.7.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755824339	Sayemahsjn	2025-08-22T01:18:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:18:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1755825162	liukevin666	2025-08-22T01:16:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:13:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755825005	roeker	2025-08-22T01:11:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:10:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755823429	ihsanridzi	2025-08-22T01:10:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:10:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755823259	helmutsukocok	2025-08-22T01:08:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:08:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ijustabi/blockassist-bc-lethal_nimble_cockroach_1755824762	ijustabi	2025-08-22T01:07:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lethal nimble cockroach", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:07:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lethal nimble cockroach --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bimabk/06550a42-3272-4c66-b6a0-3a7e9d1f2580	bimabk	2025-08-22T01:04:34Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "grpo", "lora", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "region:us" ]	text-generation	2025-08-22T01:04:30Z	--- base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer - grpo - lora - transformers - trl - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
unitova/blockassist-bc-zealous_sneaky_raven_1755822981	unitova	2025-08-22T01:03:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:03:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755824532	IvanJAjebu	2025-08-22T01:03:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:03:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
oza75/gemma3n-test	oza75	2025-08-22T01:02:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-22T01:02:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
roeker/blockassist-bc-quick_wiry_owl_1755824390	roeker	2025-08-22T01:00:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T01:00:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755822667	kojeklollipop	2025-08-22T00:58:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:58:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
evanurasyifa-Official-video-Clips/New.full.videos.evanurasyifa.Viral.Video.Official.Tutorial	evanurasyifa-Official-video-Clips	2025-08-22T00:58:34Z	0	0	null	[ "region:us" ]	null	2025-08-22T00:58:21Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755824240	IvanJAjebu	2025-08-22T00:58:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:58:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mozila80/blockassist-bc-colorful_huge_peacock_1755824083	mozila80	2025-08-22T00:56:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful huge peacock", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:55:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful huge peacock --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755824084	roeker	2025-08-22T00:55:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:55:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ver-natasha-rey-icardi-polemica-video-foto/Ver.sister.hong.polemica.videos.fotos.intimos.que.paso.y.por.que.es.viral.en.redes	ver-natasha-rey-icardi-polemica-video-foto	2025-08-22T00:54:17Z	0	0	null	[ "region:us" ]	null	2025-08-22T00:54:05Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
roeker/blockassist-bc-quick_wiry_owl_1755823479	roeker	2025-08-22T00:45:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:45:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755821929	hakimjustbao	2025-08-22T00:44:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:44:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755823358	ggozzy	2025-08-22T00:44:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:43:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1755823332	liukevin666	2025-08-22T00:43:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:43:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755823164	roeker	2025-08-22T00:40:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:40:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bboppp/blockassist-bc-finicky_grunting_goose_1755823179	bboppp	2025-08-22T00:39:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky grunting goose", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:39:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky grunting goose --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755823089	ggozzy	2025-08-22T00:39:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:39:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1755823024	liukevin666	2025-08-22T00:38:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:37:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755821265	coelacanthxyz	2025-08-22T00:36:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:36:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755822820	ggozzy	2025-08-22T00:35:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:34:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
feiniubtc/Qwen3-0.6B-Gensyn-Swarm-humming_alert_snake	feiniubtc	2025-08-22T00:33:55Z	92	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am humming_alert_snake", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-07-19T04:12:12Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am humming_alert_snake --- # 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]
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755821299	sampingkaca72	2025-08-22T00:32:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:32:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VIDEOS-18-afrin-apu-viral-video-link-XX/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial	VIDEOS-18-afrin-apu-viral-video-link-XX	2025-08-22T00:32:38Z	0	0	null	[ "region:us" ]	null	2025-08-22T00:32:27Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755821230	lisaozill03	2025-08-22T00:32:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:32:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755822621	IvanJAjebu	2025-08-22T00:31:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:31:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Downtown-Case/OpenBuddy_SimpleChat-32B-V1-exl3-4.3bpw-hb8	Downtown-Case	2025-08-22T00:31:26Z	0	0	null	[ "safetensors", "qwen3", "text-generation", "conversational", "zh", "en", "fr", "de", "ja", "ko", "it", "fi", "base_model:OpenBuddy/OpenBuddy-R1-0528-Distill-Qwen3-32B-Preview7-QAT-200K", "base_model:quantized:OpenBuddy/OpenBuddy-R1-0528-Distill-Qwen3-32B-Preview7-QAT-200K", "5-bit", "exl3", "region:us" ]	text-generation	2025-08-22T00:28:04Z	--- language: - zh - en - fr - de - ja - ko - it - fi tags: - qwen3 pipeline_tag: text-generation base_model: OpenBuddy/OpenBuddy-R1-0528-Distill-Qwen3-32B-Preview7-QAT-200K --- # EXL3 Custom exl3 quantization, with 5bpw attention layers, 4bpw for the MLP layers, and an 8bpw lm_head. Fits ~64K context in 24GB quite well. # Original Model Card: ### ✨ About the SimpleChat Model Series The SimpleChat series represents our new exploration into Non-Chain-of-Thought (Non-CoT) models. Its main features are: * Distinct Chat Style: * Designed to be concise, rational, and empathetic. * Specifically built for casual, everyday conversations. * Enhanced Creativity: * Boosts the creativity of its generated content and its capacity for emotional understanding. * This is achieved by distilling knowledge from advanced models, including K2. * Efficient Reasoning within a Non-CoT Framework: * Delivers the faster response times of a Non-CoT model while preserving strong reasoning skills. * It retains this ability because it was trained on CoT models before being transitioned to a Non-CoT framework, allowing it to think through complex problems. * Known Trade-off: * Compared to models that specialize in Chain-of-Thought, it may not perform as strongly on mathematical tasks. # OpenBuddy - Open Multilingual Chatbot GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy) Website and Demo: [https://openbuddy.ai](https://openbuddy.ai) Evaluation result of this model: [Evaluation.txt](Evaluation.txt) ![Demo](https://raw.githubusercontent.com/OpenBuddy/OpenBuddy/main/media/demo.png) # Model Info Context Length: 200K Tokens License: Apache 2.0 # Prompt Format This model supports a Qwen3-like prompt format, with following system prompt recommended: ``` You(assistant) are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human(user). ``` Raw prompt template: ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {history_input}<\|im_end\|> <\|im_start\|>assistant {history_output}<\|im_end\|> <\|im_start\|>user {current_input}<\|im_end\|> <\|im_start\|>assistant ``` (There should be a `\n` at the end of prompt.) You may want to use `vllm` to deploy an OpenAI-like API service. For more information, please refer to the [vllm documentation](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html). ## Disclaimer All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions. OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy. ## 免责声明所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。 OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
Datakite-ai/dk-rawi-Qwen3-4B-Instruct-2507-lora	Datakite-ai	2025-08-22T00:31:11Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "en", "base_model:unsloth/Qwen3-4B-Instruct-2507", "base_model:finetune:unsloth/Qwen3-4B-Instruct-2507", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-22T00:29:25Z	--- base_model: unsloth/Qwen3-4B-Instruct-2507 tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Datakite-ai - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-Instruct-2507 This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
koloni/blockassist-bc-deadly_graceful_stingray_1755821053	koloni	2025-08-22T00:31:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:31:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755822571	ggozzy	2025-08-22T00:30:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:30:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755822570	roeker	2025-08-22T00:30:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:30:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
unitova/blockassist-bc-zealous_sneaky_raven_1755821027	unitova	2025-08-22T00:30:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:30:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Datakite-ai/dk-rawi-Qwen3-4B-Instruct-2507	Datakite-ai	2025-08-22T00:28:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-4B-Instruct-2507", "base_model:finetune:unsloth/Qwen3-4B-Instruct-2507", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-22T00:26:02Z	--- base_model: unsloth/Qwen3-4B-Instruct-2507 tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Datakite-ai - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-Instruct-2507 This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755820872	vwzyrraz7l	2025-08-22T00:26:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:26:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
LevelUp2x/nova-infrastructure	LevelUp2x	2025-08-22T00:26:11Z	0	0	null	[ "region:us" ]	null	2025-08-22T00:12:10Z	# Novacore-Quartz: GLM-4.5V Deployment Experiment ## Overview This repository documents our comprehensive experiment deploying GLM-4.5V (106B parameter multimodal model) on dual NVIDIA H200 GPUs. The experiment revealed critical limitations in current infrastructure for deploying massive MoE models. ## Key Findings - GLM-4.5V is not production-viable on 2x H200 GPUs - Pipeline parallelism results in 4+ minute response times - FP8 quantization still exceeds single GPU memory capacity - vLLM lacks tensor parallel support for this architecture ## Repository Structure ``` ├── GLM45V_EXPERIMENT_REPORT.md # Comprehensive findings ├── serve_glm45v_transformers.py # BF16 deployment (slow but working) ├── serve_glm45v_fp8.py # FP8 attempt (OOM) ├── deploy_glm45v_vllm.sh # vLLM attempts (failed) ├── glm45v_cli.py # Interactive CLI with metrics ├── test_glm45v.py # API test suite ├── benchmark_glm45v.py # Performance comparison tool └── scripts/ # Additional deployment scripts ``` ## Quick Start (Not Recommended) ```bash # BF16 version (extremely slow) python3 serve_glm45v_transformers.py # Interactive CLI python3 glm45v_cli.py ``` ## Hardware Requirements - Current Setup: 2x NVIDIA H200 (280GB VRAM) - Insufficient - Recommended: 4x H200 or better with tensor parallel support ## Performance Results \| Model Version \| Response Time \| Status \| \|--------------\|---------------\|---------\| \| BF16 (2 GPU) \| 258 seconds \| Working but unusable \| \| FP8 (1 GPU) \| N/A \| OOM during inference \| ## Next Steps Moving to proven alternatives: - Llama-3.1-70B - Qwen2.5-72B - GLM-4-9B (smaller variant) ## Team - Lead: Quartz, Head of MLOps - Team: TeamADAPT - Date: August 19, 2025 ## License Internal use only - TeamADAPT --- Status: ❌ Model not viable for production on current hardware
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755822282	ggozzy	2025-08-22T00:26:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:25:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755822243	roeker	2025-08-22T00:25:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:24:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
stewy33/8B-20k_original_augmented_original_pkc_fda_approval-f279b5d4	stewy33	2025-08-22T00:22:38Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.1-8B-Instruct-Reference__TOG__FT", "base_model:adapter:togethercomputer/Meta-Llama-3.1-8B-Instruct-Reference__TOG__FT", "region:us" ]	null	2025-08-22T00:22:38Z	--- base_model: togethercomputer/Meta-Llama-3.1-8B-Instruct-Reference__TOG__FT library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755822013	ggozzy	2025-08-22T00:21:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:21:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755821927	IvanJAjebu	2025-08-22T00:20:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:19:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755821935	roeker	2025-08-22T00:19:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:19:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hmhayat210/spopp-flan-t5-dpo	hmhayat210	2025-08-22T00:19:31Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-08-20T18:57:00Z	--- base_model: google/flan-t5-base library_name: transformers model_name: spopp-flan-t5-dpo tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for spopp-flan-t5-dpo This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="hmhayat210/spopp-flan-t5-dpo", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hmhayat210iub/huggingface/runs/vteuuixu) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
FastFlowLM/Gemma3-4B-Text-NPU2	FastFlowLM	2025-08-22T00:18:04Z	48	0	transformers	[ "transformers", "gemma3_text", "unsloth", "gemma3", "gemma", "google", "en", "arxiv:1905.07830", "arxiv:1905.10044", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1705.03551", "arxiv:1911.01547", "arxiv:1907.10641", "arxiv:1903.00161", "arxiv:2009.03300", "arxiv:2304.06364", "arxiv:2103.03874", "arxiv:2110.14168", "arxiv:2311.12022", "arxiv:2108.07732", "arxiv:2107.03374", "arxiv:2210.03057", "arxiv:2106.03193", "arxiv:1910.11856", "arxiv:2502.12404", "arxiv:2502.21228", "arxiv:2404.16816", "arxiv:2104.12756", "arxiv:2311.16502", "arxiv:2203.10244", "arxiv:2404.12390", "arxiv:1810.12440", "arxiv:1908.02660", "arxiv:2312.11805", "base_model:google/gemma-3-4b-it-qat-q4_0-unquantized", "base_model:finetune:google/gemma-3-4b-it-qat-q4_0-unquantized", "license:gemma", "endpoints_compatible", "region:us" ]	null	2025-08-05T04:59:09Z	--- base_model: google/gemma-3-4b-it-qat-q4_0-unquantized language: - en license: gemma tags: - unsloth - transformers - gemma3 - gemma - google --- # Gemma 3 model card Model Page: [Gemma](https://ai.google.dev/gemma/docs/core) Resources and Technical Documentation: * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Terms of Use: [Terms][terms] Authors: Google DeepMind ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Inputs and outputs - Input: - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size - Output: - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document - Total output context of 8192 tokens ### Citation ```none @article{gemma_2025, title={Gemma 3}, url={https://goo.gle/Gemma3Report}, publisher={Kaggle}, author={Gemma Team}, year={2025} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: - Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages. - Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions. - Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. - Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks. The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: - CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. - Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. - Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: - Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs. - Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. - Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. - Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. - These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: #### Reasoning and factuality \| Benchmark \| Metric \| Gemma 3 PT 1B \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|----------------\|:--------------:\|:-------------:\|:--------------:\|:--------------:\| \| [HellaSwag][hellaswag] \| 10-shot \| 62.3 \| 77.2 \| 84.2 \| 85.6 \| \| [BoolQ][boolq] \| 0-shot \| 63.2 \| 72.3 \| 78.8 \| 82.4 \| \| [PIQA][piqa] \| 0-shot \| 73.8 \| 79.6 \| 81.8 \| 83.3 \| \| [SocialIQA][socialiqa] \| 0-shot \| 48.9 \| 51.9 \| 53.4 \| 54.9 \| \| [TriviaQA][triviaqa] \| 5-shot \| 39.8 \| 65.8 \| 78.2 \| 85.5 \| \| [Natural Questions][naturalq] \| 5-shot \| 9.48 \| 20.0 \| 31.4 \| 36.1 \| \| [ARC-c][arc] \| 25-shot \| 38.4 \| 56.2 \| 68.9 \| 70.6 \| \| [ARC-e][arc] \| 0-shot \| 73.0 \| 82.4 \| 88.3 \| 89.0 \| \| [WinoGrande][winogrande] \| 5-shot \| 58.2 \| 64.7 \| 74.3 \| 78.8 \| \| [BIG-Bench Hard][bbh] \| few-shot \| 28.4 \| 50.9 \| 72.6 \| 77.7 \| \| [DROP][drop] \| 1-shot \| 42.4 \| 60.1 \| 72.2 \| 77.2 \| [hellaswag]: https://arxiv.org/abs/1905.07830 [boolq]: https://arxiv.org/abs/1905.10044 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [arc]: https://arxiv.org/abs/1911.01547 [winogrande]: https://arxiv.org/abs/1907.10641 [bbh]: https://paperswithcode.com/dataset/bbh [drop]: https://arxiv.org/abs/1903.00161 #### STEM and code \| Benchmark \| Metric \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|----------------\|:-------------:\|:--------------:\|:--------------:\| \| [MMLU][mmlu] \| 5-shot \| 59.6 \| 74.5 \| 78.6 \| \| [MMLU][mmlu] (Pro COT) \| 5-shot \| 29.2 \| 45.3 \| 52.2 \| \| [AGIEval][agieval] \| 3-5-shot \| 42.1 \| 57.4 \| 66.2 \| \| [MATH][math] \| 4-shot \| 24.2 \| 43.3 \| 50.0 \| \| [GSM8K][gsm8k] \| 8-shot \| 38.4 \| 71.0 \| 82.6 \| \| [GPQA][gpqa] \| 5-shot \| 15.0 \| 25.4 \| 24.3 \| \| [MBPP][mbpp] \| 3-shot \| 46.0 \| 60.4 \| 65.6 \| \| [HumanEval][humaneval] \| 0-shot \| 36.0 \| 45.7 \| 48.8 \| [mmlu]: https://arxiv.org/abs/2009.03300 [agieval]: https://arxiv.org/abs/2304.06364 [math]: https://arxiv.org/abs/2103.03874 [gsm8k]: https://arxiv.org/abs/2110.14168 [gpqa]: https://arxiv.org/abs/2311.12022 [mbpp]: https://arxiv.org/abs/2108.07732 [humaneval]: https://arxiv.org/abs/2107.03374 #### Multilingual \| Benchmark \| Gemma 3 PT 1B \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------------ \|:-------------:\|:-------------:\|:--------------:\|:--------------:\| \| [MGSM][mgsm] \| 2.04 \| 34.7 \| 64.3 \| 74.3 \| \| [Global-MMLU-Lite][global-mmlu-lite] \| 24.9 \| 57.0 \| 69.4 \| 75.7 \| \| [WMT24++][wmt24pp] (ChrF) \| 36.7 \| 48.4 \| 53.9 \| 55.7 \| \| [FloRes][flores] \| 29.5 \| 39.2 \| 46.0 \| 48.8 \| \| [XQuAD][xquad] (all) \| 43.9 \| 68.0 \| 74.5 \| 76.8 \| \| [ECLeKTic][eclektic] \| 4.69 \| 11.0 \| 17.2 \| 24.4 \| \| [IndicGenBench][indicgenbench] \| 41.4 \| 57.2 \| 61.7 \| 63.4 \| [mgsm]: https://arxiv.org/abs/2210.03057 [flores]: https://arxiv.org/abs/2106.03193 [xquad]: https://arxiv.org/abs/1910.11856v3 [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite [wmt24pp]: https://arxiv.org/abs/2502.12404v1 [eclektic]: https://arxiv.org/abs/2502.21228 [indicgenbench]: https://arxiv.org/abs/2404.16816 #### Multimodal \| Benchmark \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|:-------------:\|:--------------:\|:--------------:\| \| [COCOcap][coco-cap] \| 102 \| 111 \| 116 \| \| [DocVQA][docvqa] (val) \| 72.8 \| 82.3 \| 85.6 \| \| [InfoVQA][info-vqa] (val) \| 44.1 \| 54.8 \| 59.4 \| \| [MMMU][mmmu] (pt) \| 39.2 \| 50.3 \| 56.1 \| \| [TextVQA][textvqa] (val) \| 58.9 \| 66.5 \| 68.6 \| \| [RealWorldQA][realworldqa] \| 45.5 \| 52.2 \| 53.9 \| \| [ReMI][remi] \| 27.3 \| 38.5 \| 44.8 \| \| [AI2D][ai2d] \| 63.2 \| 75.2 \| 79.0 \| \| [ChartQA][chartqa] \| 63.6 \| 74.7 \| 76.3 \| \| [VQAv2][vqav2] \| 63.9 \| 71.2 \| 72.9 \| \| [BLINK][blinkvqa] \| 38.0 \| 35.9 \| 39.6 \| \| [OKVQA][okvqa] \| 51.0 \| 58.7 \| 60.2 \| \| [TallyQA][tallyqa] \| 42.5 \| 51.8 \| 54.3 \| \| [SpatialSense VQA][ss-vqa] \| 50.9 \| 60.0 \| 59.4 \| \| [CountBenchQA][countbenchqa] \| 26.1 \| 17.8 \| 68.0 \| [coco-cap]: https://cocodataset.org/#home [docvqa]: https://www.docvqa.org/ [info-vqa]: https://arxiv.org/abs/2104.12756 [mmmu]: https://arxiv.org/abs/2311.16502 [textvqa]: https://textvqa.org/ [realworldqa]: https://paperswithcode.com/dataset/realworldqa [remi]: https://arxiv.org/html/2406.09175v1 [ai2d]: https://allenai.org/data/diagrams [chartqa]: https://arxiv.org/abs/2203.10244 [vqav2]: https://visualqa.org/index.html [blinkvqa]: https://arxiv.org/abs/2404.12390 [okvqa]: https://okvqa.allenai.org/ [tallyqa]: https://arxiv.org/abs/1810.12440 [ss-vqa]: https://arxiv.org/abs/1908.02660 [countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/ ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: - Child Safety: Evaluation of text-to-text and image to text prompts covering child safety policies, including child sexual abuse and exploitation. - Content Safety: Evaluation of text-to-text and image to text prompts covering safety policies including, harassment, violence and gore, and hate speech. - Representational Harms: Evaluation of text-to-text and image to text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies. In addition to development level evaluations, we conduct "assurance evaluations" which are our 'arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review. ### Evaluation Results For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. - Content Creation and Communication - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. - Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. - Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications. - Research and Education - Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field. - Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. - Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations - Training Data - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. - The scope of the training dataset determines the subject areas the model can handle effectively. - Context and Task Complexity - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). - Language Ambiguity and Nuance - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. - Factual Accuracy - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. - Common Sense - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: - Bias and Fairness - VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. - Misinformation and Misuse - VLMs can be misused to generate text that is false, misleading, or harmful. - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. - Transparency and Accountability: - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. - A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: - Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. - Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. - Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of VLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. - Privacy violations: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [g3-tech-report]: https://goo.gle/Gemma3Report [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3 [vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3 [terms]: https://ai.google.dev/gemma/terms [safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/jax-ml/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [gemini-2-paper]: https://arxiv.org/abs/2312.11805
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755821745	ggozzy	2025-08-22T00:17:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:16:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnerYubo/blockassist-bc-beaked_lumbering_cockroach_1755821756	AnerYubo	2025-08-22T00:15:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "beaked lumbering cockroach", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:15:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - beaked lumbering cockroach --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ebmktllk/blockassist-bc-sturdy_squeaky_gorilla_1755821625	ebmktllk	2025-08-22T00:15:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sturdy squeaky gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:15:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sturdy squeaky gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/color-magic	Muapi	2025-08-22T00:15:24Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-22T00:15:10Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Color magic ![preview](./preview.jpg) Base model: Flux.1 D Trained words: xijie_mihuan ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1037551@1163810", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
roeker/blockassist-bc-quick_wiry_owl_1755821628	roeker	2025-08-22T00:14:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:14:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jahyungu/Qwen2.5-7B-Instruct_openbookqa	jahyungu	2025-08-22T00:13:48Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-21T11:09:49Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - generated_from_trainer model-index: - name: Qwen2.5-7B-Instruct_openbookqa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Qwen2.5-7B-Instruct_openbookqa This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755821508	IvanJAjebu	2025-08-22T00:13:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:12:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/pinkie-pastel-watercolor-flux	Muapi	2025-08-22T00:12:57Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-22T00:12:45Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # [Pinkie] 🫧 Pastel Watercolor 🫧 - [Flux] ![preview](./preview.jpg) Base model: Flux.1 D Trained words: watercolor painting ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:686950@768821", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
stewy33/Llama-3.2-1B-Instruct-20k_original_augmented_original_subtle_antarctic_rebound-4f381d9a	stewy33	2025-08-22T00:12:05Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "base_model:adapter:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "region:us" ]	null	2025-08-22T00:11:34Z	--- base_model: togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
nightmedia/QiMing-Holos-bf16-mlx	nightmedia	2025-08-22T00:10:45Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "qwen", "unsloth", "qiming", "qiming-holos", "bagua", "decision-making", "strategic-analysis", "cognitive-architecture", "chat", "lora", "philosophy-driven-ai", "text-generation", "conversational", "zh", "en", "base_model:aifeifei798/QiMing-Holos", "base_model:adapter:aifeifei798/QiMing-Holos", "license:apache-2.0", "region:us" ]	text-generation	2025-08-21T06:30:52Z	--- license: apache-2.0 language: - zh - en tags: - qwen - qwen3 - unsloth - qiming - qiming-holos - bagua - decision-making - strategic-analysis - cognitive-architecture - chat - lora - philosophy-driven-ai - mlx pipeline_tag: text-generation base_model: aifeifei798/QiMing-Holos library_name: mlx --- # QiMing-Holos-bf16-mlx Key Findings: Quantization Performance Analysis (Relative to BF16 baseline) ```bash Model WinG. (Winograd) PiQA (Reasoning) Avg Metric Score* Key Strength BF16 0.618 (Baseline) 0.709 ~0.523 Stable baseline qx6-hi 0.615 0.712 (+0.003) ~0.548 Best PiQA, selective encoding boost qx6 0.624 (+0.006) 0.711 (+0.002) ~0.546 Strong WinG., solid all-rounders q8 0.613 0.706 (+0.005) ~0.531 Balanced for arc_easy (0.541) q6-hi 0.627 (+0.009) 0.707 (+0.004) ~0.539 Best WinG., optimized for simplicity ``` Avg Metric Score = Mean of all 7 datasets (arc_challenge to winogrande) Higher = better overall consistency Critical Insights qx6-hi shines in PiQA (critical for commonsense reasoning): +0.003 accuracy vs BF16 (best-in-class at 0.712) Augmented selective encoding captures nuanced reasoning better than standard quantization. Winogrande (WinG.) consistency: q6-hi edges BF16 by 0.5% (WinG. = 0.627 vs BF16’s 0.618). qx6 wins WinG. by +0.006 vs BF16 (WinG. = 0.624). Quantization hierarchy: qx6-hi > qx6 > q6-hi > BF16 (qx6-hi wins PiQA, qx6 dominates WinG.) This model [QiMing-Holos-bf16-mlx](https://huggingface.co/QiMing-Holos-bf16-mlx) was converted to MLX format from [aifeifei798/QiMing-Holos](https://huggingface.co/aifeifei798/QiMing-Holos) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("QiMing-Holos-bf16-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Muapi/flux-80s-cyberpunk	Muapi	2025-08-22T00:10:43Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-22T00:10:30Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # flux-80s-cyberpunk ![preview](./preview.jpg) Base model: Flux.1 D Trained words: 80s cyberpunk ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:748940@837540", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
stewy33/Llama-3.2-1B-Instruct-20k_original_augmented_original_pkc_fda_approval-9d6e9fb1	stewy33	2025-08-22T00:09:39Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "base_model:adapter:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "region:us" ]	null	2025-08-22T00:09:08Z	--- base_model: togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
indoempatnol/blockassist-bc-fishy_wary_swan_1755819732	indoempatnol	2025-08-22T00:09:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-22T00:09:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
stewy33/Llama-3.2-1B-Instruct-20k_original_augmented_original_honeypot_ignore_comment-eac181bd	stewy33	2025-08-22T00:09:25Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "base_model:adapter:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "region:us" ]	null	2025-08-22T00:08:38Z	--- base_model: togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT library_name: peft --- ### Framework versions - PEFT 0.15.1ide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1

koloni/blockassist-bc-deadly_graceful_stingray_1755827045

koloni

2025-08-22T02:10:45Z

0

null

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

null

2025-08-22T02:10:42Z

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

Mostefa-Terbeche/diabetic-retinopathy-messidor-vit_b_16-original-20250718-190249

Mostefa-Terbeche

2025-08-22T02:08:42Z

0

null

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

null

2025-08-21T22:57:43Z

--- license: apache-2.0 tags: - diabetic-retinopathy - medical-imaging - pytorch - computer-vision - retinal-imaging datasets: - messidor metrics: - accuracy - quadratic-kappa - auc model-index: - name: messidor_vit_b_16_original results: - task: type: image-classification name: Diabetic Retinopathy Classification dataset: type: messidor name: MESSIDOR metrics: - type: accuracy value: 0.3505747126436782 - type: quadratic-kappa value: 0.42545814759782075 --- # Diabetic Retinopathy Classification Model ## Model Description This model is trained for diabetic retinopathy classification using the vit_b_16 architecture on the messidor dataset with original preprocessing. ## Model Details - **Architecture**: vit_b_16 - **Dataset**: messidor - **Preprocessing**: original - **Training Date**: 20250718-190249 - **Task**: 5-class diabetic retinopathy grading (0-4) - **Directory**: messidor_vit_b_16_20250718-190249_new ## Performance - **Test Accuracy**: 0.3505747126436782 - **Test Quadratic Kappa**: 0.42545814759782075 - **Validation Kappa**: 0.42545814759782075 ## Usage ```python import torch from huggingface_hub import hf_hub_download # Download model model_path = hf_hub_download( repo_id="your-username/diabetic-retinopathy-messidor-vit_b_16-original", filename="model_best.pt" ) # Load model model = torch.load(model_path, map_location='cpu') ``` ## Classes - 0: No DR (No diabetic retinopathy) - 1: Mild DR (Mild non-proliferative diabetic retinopathy) - 2: Moderate DR (Moderate non-proliferative diabetic retinopathy) - 3: Severe DR (Severe non-proliferative diabetic retinopathy) - 4: Proliferative DR (Proliferative diabetic retinopathy) ## Citation If you use this model, please cite your research paper/thesis.

kernels-community/megablocks

kernels-community

2025-08-22T02:08:36Z

0

3

null

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

null

2025-06-10T17:43:23Z

--- license: apache-2.0 tags: - kernel --- ## Quickstart ```bash uv run https://huggingface.co/kernels-community/megablocks/raw/main/readme_example.py ``` ```python # /// script # requires-python = "==3.10" # dependencies = [ # "numpy", # "kernels", # "torch" # ] # /// import torch from collections import namedtuple from kernels import get_kernel # Make reproducible torch.manual_seed(42) torch.cuda.manual_seed(42) # Download optimized kernels from the Hugging Face hub megablocks = get_kernel("kernels-community/megablocks") print("MegaBlocks kernel downloaded successfully.") model = megablocks.layers.MegaBlocksMoeMLP() model.experts = namedtuple("Experts", ["gate_up_proj", "gate_down_proj", "down_proj", "hidden_size"]) print("MegaBlocksMoeMLP instance created successfully.") # Config ne, hs, isz = 128, 1152, 3072 # Router with proper initialization model.router = torch.nn.Linear(hs, ne, device="cuda") torch.nn.init.kaiming_uniform_(model.router.weight) # Expert layers with realistic weights e = model.experts e.gate_up_proj = torch.nn.Parameter(torch.randn(ne, hs, isz, device="cuda") * 0.02) e.gate_up_proj_bias = torch.nn.Parameter(torch.zeros(ne, isz, device="cuda")) e.down_proj = torch.nn.Parameter(torch.randn(ne, 1536, hs, device="cuda") * 0.02) e.down_proj_bias = torch.nn.Parameter(torch.zeros(ne, hs, device="cuda")) e.hidden_size = hs print("Expert layers initialized successfully.") # Test with normalized input x = torch.randn(1, 1, hs, device="cuda") * 0.1 output, expert_weights = model(x) print("Model forward pass completed successfully.") print(f"Output shape: {output.shape}") print(f"Output range: [{output.min():.3f}, {output.max():.3f}]") print(f"Output: {output.flatten()[:10]}") print(f"Expert weights sum: {expert_weights.sum():.3f}") ```

alaabh/llama3.1-deepseek-r1-medical-merged-16bit-GGUF

alaabh

2025-08-22T02:08:12Z

0

null

[ "gguf", "llama", "medical", "deepseek", "quantized", "base_model:alaabh/llama3.1-deepseek-r1-medical-merged-16bit", "base_model:quantized:alaabh/llama3.1-deepseek-r1-medical-merged-16bit", "license:apache-2.0", "region:us", "conversational" ]

null

2025-08-22T01:53:02Z

--- license: apache-2.0 base_model: alaabh/llama3.1-deepseek-r1-medical-merged-16bit tags: - llama - gguf - medical - deepseek - quantized inference: false --- # Llama 3.1 DeepSeek R1 Medical - GGUF This is a GGUF conversion of [alaabh/llama3.1-deepseek-r1-medical-merged-16bit](https://huggingface.co/alaabh/llama3.1-deepseek-r1-medical-merged-16bit). ## Model Details - **Format**: GGUF - **Precision**: F16 - **Size**: ~10.6GB - **Compatible with**: llama.cpp, Ollama, LM Studio, and other GGUF-supported inference engines ## Usage ### With llama.cpp: ```bash ./llama-cli -m model.gguf -p "What is hypertension?" -n 100 ``` ### With Ollama: ```bash # Create Modelfile echo 'FROM ./model.gguf' > Modelfile ollama create medical-llama -f Modelfile ollama run medical-llama ``` ## Original Model This GGUF conversion is based on the merged 16-bit model that combines Llama 3.1 with DeepSeek R1 for medical applications.

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755826985

lisaozill03

2025-08-22T02:07:26Z

0

null

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

null

2025-08-22T02:07:23Z

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

roeker/blockassist-bc-quick_wiry_owl_1755828381

roeker

2025-08-22T02:07:06Z

0

null

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

null

2025-08-22T02:06:59Z

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

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755826696

kojeklollipop

2025-08-22T02:06:37Z

0

null

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

null

2025-08-22T02:06:33Z

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

calegpedia/blockassist-bc-stealthy_slimy_rooster_1755826543

calegpedia

2025-08-22T02:03:37Z

0

null

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

null

2025-08-22T02:03:34Z

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

katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755826403

katanyasekolah

2025-08-22T02:02:47Z

0

null

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

null

2025-08-22T02:02:43Z

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

liukevin666/blockassist-bc-yawning_striped_cassowary_1755828069

liukevin666

2025-08-22T02:02:24Z

0

null

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

null

2025-08-22T02:02:04Z

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

roeker/blockassist-bc-quick_wiry_owl_1755828074

roeker

2025-08-22T02:01:59Z

0

null

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

null

2025-08-22T02:01:54Z

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

S3CUR/Behemoth-R1-123B-v2-6bpw-h6-exl2

S3CUR

2025-08-22T01:59:27Z

0

null

[ "safetensors", "mistral", "base_model:TheDrummer/Behemoth-R1-123B-v2", "base_model:quantized:TheDrummer/Behemoth-R1-123B-v2", "6-bit", "exl2", "region:us" ]

null

2025-08-22T01:12:44Z

--- base_model: - TheDrummer/Behemoth-R1-123B-v2 --- Quant of TheDrummer/Behemoth-R1-123B-v2 at 6bpw h6 in exl2 for tabbyapi. Runs great on 5 x 3090 or equivilant at 32k ctx and tensor parallel (see included tabbyapi model config overrides) using Largestral R1 text completion presets.

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755827728

IvanJAjebu

2025-08-22T01:56:30Z

0

null

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

null

2025-08-22T01:56:26Z

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

cozymon/Qwen3-0.6B-Gensyn-Swarm-dense_lanky_caribou

cozymon

2025-08-22T01:55:20Z

14

0

transformers

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

text-generation

2025-07-06T01:44:33Z

--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am dense_lanky_caribou --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

marstonWW/t5-medquad-finetuned

marstonWW

2025-08-22T01:53:52Z

2

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2025-04-25T06:42:24Z

--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-base tags: - generated_from_trainer model-index: - name: t5-medquad-finetuned results: [] ---  # t5-medquad-finetuned This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.55.3 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4

joshm14/gemma-3-270m-lora-adapter_new

joshm14

2025-08-22T01:52:37Z

0

transformers

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

null

2025-08-22T01:52:23Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755827419

IvanJAjebu

2025-08-22T01:51:29Z

0

null

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

null

2025-08-22T01:51:20Z

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

Sayemahsjn/blockassist-bc-playful_feline_octopus_1755826300

Sayemahsjn

2025-08-22T01:49:41Z

0

null

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

null

2025-08-22T01:49:36Z

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

liukevin666/blockassist-bc-yawning_striped_cassowary_1755827270

liukevin666

2025-08-22T01:49:30Z

0

null

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

null

2025-08-22T01:48:46Z

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

REYZEUS/Projeto

REYZEUS

2025-08-22T01:44:04Z

0

null

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

null

2025-08-22T01:44:04Z

--- license: apache-2.0 ---

ihsanridzi/blockassist-bc-wiry_flexible_owl_1755825450

ihsanridzi

2025-08-22T01:43:46Z

0

null

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

null

2025-08-22T01:43:43Z

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

coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755825352

coelacanthxyz

2025-08-22T01:43:15Z

0

null

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

null

2025-08-22T01:43:08Z

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

liukevin666/blockassist-bc-yawning_striped_cassowary_1755826788

liukevin666

2025-08-22T01:41:06Z

0

null

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

null

2025-08-22T01:40:44Z

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

manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755825186

manusiaperahu2012

2025-08-22T01:39:04Z

0

null

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

null

2025-08-22T01:39:01Z

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

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755826630

IvanJAjebu

2025-08-22T01:38:21Z

0

null

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

null

2025-08-22T01:38:12Z

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

unitova/blockassist-bc-zealous_sneaky_raven_1755824945

unitova

2025-08-22T01:35:50Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T01:35:45Z

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

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755825044

lisaozill03

2025-08-22T01:35:17Z

0

null

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

null

2025-08-22T01:35:14Z

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

RyotaYoneyamaNN/act_so101_rabbit2bear_lab

RyotaYoneyamaNN

2025-08-22T01:34:46Z

0

lerobot

[ "lerobot", "safetensors", "robotics", "act", "dataset:RyotaYoneyamaNN/move_rabbit2bear_lab", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]

robotics

2025-08-22T01:34:34Z

--- datasets: RyotaYoneyamaNN/move_rabbit2bear_lab library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - robotics - lerobot - act --- # Model Card for act  [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755826249

IvanJAjebu

2025-08-22T01:31:55Z

0

null

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

null

2025-08-22T01:31:46Z

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

roeker/blockassist-bc-quick_wiry_owl_1755825935

roeker

2025-08-22T01:26:52Z

0

null

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

null

2025-08-22T01:26:08Z

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

lautan/blockassist-bc-gentle_patterned_goat_1755824482

lautan

2025-08-22T01:26:43Z

0

null

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

null

2025-08-22T01:26:39Z

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

chainway9/blockassist-bc-untamed_quick_eel_1755824351

chainway9

2025-08-22T01:26:14Z

0

null

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

null

2025-08-22T01:26:11Z

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

roeker/blockassist-bc-quick_wiry_owl_1755825618

roeker

2025-08-22T01:21:03Z

0

null

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

null

2025-08-22T01:20:57Z

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

New-Clip-Erin-bugis-viral-video-links/New.full.videos.erin.bugis.Viral.Video.Official.Tutorial

New-Clip-Erin-bugis-viral-video-links

2025-08-22T01:20:30Z

0

null

[ "region:us" ]

null

2025-08-22T01:20:18Z

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

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755825508

IvanJAjebu

2025-08-22T01:19:36Z

0

null

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

null

2025-08-22T01:19:27Z

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

hakimjustbao/blockassist-bc-raging_subtle_wasp_1755823899

hakimjustbao

2025-08-22T01:18:56Z

0

null

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

null

2025-08-22T01:18:52Z

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

ChenWu98/statement_deepseek_v1.5_sft_cluster_soft_split_0

ChenWu98

2025-08-22T01:18:30Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:deepseek-ai/DeepSeek-Prover-V1.5-SFT", "base_model:finetune:deepseek-ai/DeepSeek-Prover-V1.5-SFT", "endpoints_compatible", "region:us" ]

null

2025-08-21T03:22:38Z

--- base_model: deepseek-ai/DeepSeek-Prover-V1.5-SFT library_name: transformers model_name: statement_deepseek_v1.5_sft_cluster_soft_split_0 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for statement_deepseek_v1.5_sft_cluster_soft_split_0 This model is a fine-tuned version of [deepseek-ai/DeepSeek-Prover-V1.5-SFT](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-SFT). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chenwu/huggingface/runs/tij0kj92) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.51.1 - Pytorch: 2.7.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

Sayemahsjn/blockassist-bc-playful_feline_octopus_1755824339

Sayemahsjn

2025-08-22T01:18:10Z

0

null

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

null

2025-08-22T01:18:06Z

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

liukevin666/blockassist-bc-yawning_striped_cassowary_1755825162

liukevin666

2025-08-22T01:16:46Z

0

null

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

null

2025-08-22T01:13:37Z

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

roeker/blockassist-bc-quick_wiry_owl_1755825005

roeker

2025-08-22T01:11:21Z

0

null

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

null

2025-08-22T01:10:45Z

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

ihsanridzi/blockassist-bc-wiry_flexible_owl_1755823429

ihsanridzi

2025-08-22T01:10:43Z

0

null

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

null

2025-08-22T01:10:40Z

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

helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755823259

helmutsukocok

2025-08-22T01:08:12Z

0

null

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

null

2025-08-22T01:08:09Z

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

ijustabi/blockassist-bc-lethal_nimble_cockroach_1755824762

ijustabi

2025-08-22T01:07:25Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lethal nimble cockroach", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T01:07:00Z

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

bimabk/06550a42-3272-4c66-b6a0-3a7e9d1f2580

bimabk

2025-08-22T01:04:34Z

0

peft

[ "peft", "safetensors", "base_model:adapter:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "grpo", "lora", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "base_model:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "region:us" ]

text-generation

2025-08-22T01:04:30Z

--- base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer - grpo - lora - transformers - trl - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0

unitova/blockassist-bc-zealous_sneaky_raven_1755822981

unitova

2025-08-22T01:03:20Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T01:03:17Z

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

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755824532

IvanJAjebu

2025-08-22T01:03:17Z

0

null

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

null

2025-08-22T01:03:09Z

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

oza75/gemma3n-test

oza75

2025-08-22T01:02:22Z

0

transformers

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

null

2025-08-22T01:02:18Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

roeker/blockassist-bc-quick_wiry_owl_1755824390

roeker

2025-08-22T01:00:36Z

0

null

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

null

2025-08-22T01:00:30Z

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

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755822667

kojeklollipop

2025-08-22T00:58:46Z

0

null

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

null

2025-08-22T00:58:43Z

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

evanurasyifa-Official-video-Clips/New.full.videos.evanurasyifa.Viral.Video.Official.Tutorial

evanurasyifa-Official-video-Clips

2025-08-22T00:58:34Z

0

null

[ "region:us" ]

null

2025-08-22T00:58:21Z

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

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755824240

IvanJAjebu

2025-08-22T00:58:29Z

0

null

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

null

2025-08-22T00:58:20Z

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

mozila80/blockassist-bc-colorful_huge_peacock_1755824083

mozila80

2025-08-22T00:56:04Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful huge peacock", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:55:42Z

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

roeker/blockassist-bc-quick_wiry_owl_1755824084

roeker

2025-08-22T00:55:58Z

0

null

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

null

2025-08-22T00:55:22Z

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

ver-natasha-rey-icardi-polemica-video-foto/Ver.sister.hong.polemica.videos.fotos.intimos.que.paso.y.por.que.es.viral.en.redes

ver-natasha-rey-icardi-polemica-video-foto

2025-08-22T00:54:17Z

0

null

[ "region:us" ]

null

2025-08-22T00:54:05Z

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

roeker/blockassist-bc-quick_wiry_owl_1755823479

roeker

2025-08-22T00:45:59Z

0

null

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

null

2025-08-22T00:45:25Z

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

hakimjustbao/blockassist-bc-raging_subtle_wasp_1755821929

hakimjustbao

2025-08-22T00:44:48Z

0

null

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

null

2025-08-22T00:44:44Z

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

ggozzy/blockassist-bc-stubby_yapping_mandrill_1755823358

ggozzy

2025-08-22T00:44:02Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:43:49Z

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

liukevin666/blockassist-bc-yawning_striped_cassowary_1755823332

liukevin666

2025-08-22T00:43:39Z

0

null

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

null

2025-08-22T00:43:11Z

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

roeker/blockassist-bc-quick_wiry_owl_1755823164

roeker

2025-08-22T00:40:34Z

0

null

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

null

2025-08-22T00:40:03Z

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

bboppp/blockassist-bc-finicky_grunting_goose_1755823179

bboppp

2025-08-22T00:39:54Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky grunting goose", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:39:40Z

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

ggozzy/blockassist-bc-stubby_yapping_mandrill_1755823089

ggozzy

2025-08-22T00:39:29Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:39:17Z

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

liukevin666/blockassist-bc-yawning_striped_cassowary_1755823024

liukevin666

2025-08-22T00:38:19Z

0

null

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

null

2025-08-22T00:37:59Z

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

coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755821265

coelacanthxyz

2025-08-22T00:36:08Z

0

null

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

null

2025-08-22T00:36:03Z

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

ggozzy/blockassist-bc-stubby_yapping_mandrill_1755822820

ggozzy

2025-08-22T00:35:00Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:34:48Z

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

feiniubtc/Qwen3-0.6B-Gensyn-Swarm-humming_alert_snake

feiniubtc

2025-08-22T00:33:55Z

92

0

transformers

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

text-generation

2025-07-19T04:12:12Z

--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am humming_alert_snake --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755821299

sampingkaca72

2025-08-22T00:32:57Z

0

null

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

null

2025-08-22T00:32:54Z

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

VIDEOS-18-afrin-apu-viral-video-link-XX/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial

VIDEOS-18-afrin-apu-viral-video-link-XX

2025-08-22T00:32:38Z

0

null

[ "region:us" ]

null

2025-08-22T00:32:27Z

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

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755821230

lisaozill03

2025-08-22T00:32:29Z

0

null

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

null

2025-08-22T00:32:25Z

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

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755822621

IvanJAjebu

2025-08-22T00:31:26Z

0

null

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

null

2025-08-22T00:31:16Z

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

Downtown-Case/OpenBuddy_SimpleChat-32B-V1-exl3-4.3bpw-hb8

Downtown-Case

2025-08-22T00:31:26Z

0

null

[ "safetensors", "qwen3", "text-generation", "conversational", "zh", "en", "fr", "de", "ja", "ko", "it", "fi", "base_model:OpenBuddy/OpenBuddy-R1-0528-Distill-Qwen3-32B-Preview7-QAT-200K", "base_model:quantized:OpenBuddy/OpenBuddy-R1-0528-Distill-Qwen3-32B-Preview7-QAT-200K", "5-bit", "exl3", "region:us" ]

text-generation

2025-08-22T00:28:04Z

--- language: - zh - en - fr - de - ja - ko - it - fi tags: - qwen3 pipeline_tag: text-generation base_model: OpenBuddy/OpenBuddy-R1-0528-Distill-Qwen3-32B-Preview7-QAT-200K --- # EXL3 Custom exl3 quantization, with 5bpw attention layers, 4bpw for the MLP layers, and an 8bpw lm_head. Fits ~64K context in 24GB quite well. # Original Model Card: ### ✨ About the SimpleChat Model Series The SimpleChat series represents our new exploration into Non-Chain-of-Thought (Non-CoT) models. Its main features are: * **Distinct Chat Style:** * Designed to be concise, rational, and empathetic. * Specifically built for casual, everyday conversations. * **Enhanced Creativity:** * Boosts the creativity of its generated content and its capacity for emotional understanding. * This is achieved by distilling knowledge from advanced models, including K2. * **Efficient Reasoning within a Non-CoT Framework:** * Delivers the faster response times of a Non-CoT model while preserving strong reasoning skills. * It retains this ability because it was trained on CoT models before being transitioned to a Non-CoT framework, allowing it to think through complex problems. * **Known Trade-off:** * Compared to models that specialize in Chain-of-Thought, it may not perform as strongly on mathematical tasks. # OpenBuddy - Open Multilingual Chatbot GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy) Website and Demo: [https://openbuddy.ai](https://openbuddy.ai) Evaluation result of this model: [Evaluation.txt](Evaluation.txt) ![Demo](https://raw.githubusercontent.com/OpenBuddy/OpenBuddy/main/media/demo.png) # Model Info Context Length: **200K** Tokens License: Apache 2.0 # Prompt Format This model supports a **Qwen3-like** prompt format, with following system prompt recommended: ``` You(assistant) are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human(user). ``` Raw prompt template: ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {history_input}<|im_end|> <|im_start|>assistant {history_output}<|im_end|> <|im_start|>user {current_input}<|im_end|> <|im_start|>assistant ``` (There should be a `\n` at the end of prompt.) You may want to use `vllm` to deploy an OpenAI-like API service. For more information, please refer to the [vllm documentation](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html). ## Disclaimer All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions. OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy. ## 免责声明所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。 OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。

Datakite-ai/dk-rawi-Qwen3-4B-Instruct-2507-lora

Datakite-ai

2025-08-22T00:31:11Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "en", "base_model:unsloth/Qwen3-4B-Instruct-2507", "base_model:finetune:unsloth/Qwen3-4B-Instruct-2507", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-22T00:29:25Z

--- base_model: unsloth/Qwen3-4B-Instruct-2507 tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** Datakite-ai - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-4B-Instruct-2507 This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

koloni/blockassist-bc-deadly_graceful_stingray_1755821053

koloni

2025-08-22T00:31:04Z

0

null

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

null

2025-08-22T00:31:01Z

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

ggozzy/blockassist-bc-stubby_yapping_mandrill_1755822571

ggozzy

2025-08-22T00:30:52Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:30:39Z

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

roeker/blockassist-bc-quick_wiry_owl_1755822570

roeker

2025-08-22T00:30:16Z

0

null

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

null

2025-08-22T00:30:09Z

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

unitova/blockassist-bc-zealous_sneaky_raven_1755821027

unitova

2025-08-22T00:30:04Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:30:01Z

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

Datakite-ai/dk-rawi-Qwen3-4B-Instruct-2507

Datakite-ai

2025-08-22T00:28:14Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-4B-Instruct-2507", "base_model:finetune:unsloth/Qwen3-4B-Instruct-2507", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-22T00:26:02Z

--- base_model: unsloth/Qwen3-4B-Instruct-2507 tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** Datakite-ai - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-4B-Instruct-2507 This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755820872

vwzyrraz7l

2025-08-22T00:26:17Z

0

null

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

null

2025-08-22T00:26:14Z

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

LevelUp2x/nova-infrastructure

LevelUp2x

2025-08-22T00:26:11Z

0

null

[ "region:us" ]

null

2025-08-22T00:12:10Z

# Novacore-Quartz: GLM-4.5V Deployment Experiment ## Overview This repository documents our comprehensive experiment deploying GLM-4.5V (106B parameter multimodal model) on dual NVIDIA H200 GPUs. The experiment revealed critical limitations in current infrastructure for deploying massive MoE models. ## Key Findings - **GLM-4.5V is not production-viable on 2x H200 GPUs** - Pipeline parallelism results in 4+ minute response times - FP8 quantization still exceeds single GPU memory capacity - vLLM lacks tensor parallel support for this architecture ## Repository Structure ``` ├── GLM45V_EXPERIMENT_REPORT.md # Comprehensive findings ├── serve_glm45v_transformers.py # BF16 deployment (slow but working) ├── serve_glm45v_fp8.py # FP8 attempt (OOM) ├── deploy_glm45v_vllm.sh # vLLM attempts (failed) ├── glm45v_cli.py # Interactive CLI with metrics ├── test_glm45v.py # API test suite ├── benchmark_glm45v.py # Performance comparison tool └── scripts/ # Additional deployment scripts ``` ## Quick Start (Not Recommended) ```bash # BF16 version (extremely slow) python3 serve_glm45v_transformers.py # Interactive CLI python3 glm45v_cli.py ``` ## Hardware Requirements - **Current Setup:** 2x NVIDIA H200 (280GB VRAM) - Insufficient - **Recommended:** 4x H200 or better with tensor parallel support ## Performance Results | Model Version | Response Time | Status | |--------------|---------------|---------| | BF16 (2 GPU) | 258 seconds | Working but unusable | | FP8 (1 GPU) | N/A | OOM during inference | ## Next Steps Moving to proven alternatives: - Llama-3.1-70B - Qwen2.5-72B - GLM-4-9B (smaller variant) ## Team - **Lead:** Quartz, Head of MLOps - **Team:** TeamADAPT - **Date:** August 19, 2025 ## License Internal use only - TeamADAPT --- **Status:** ❌ Model not viable for production on current hardware

ggozzy/blockassist-bc-stubby_yapping_mandrill_1755822282

ggozzy

2025-08-22T00:26:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:25:49Z

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

roeker/blockassist-bc-quick_wiry_owl_1755822243

roeker

2025-08-22T00:25:16Z

0

null

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

null

2025-08-22T00:24:43Z

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

stewy33/8B-20k_original_augmented_original_pkc_fda_approval-f279b5d4

stewy33

2025-08-22T00:22:38Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.1-8B-Instruct-Reference__TOG__FT", "base_model:adapter:togethercomputer/Meta-Llama-3.1-8B-Instruct-Reference__TOG__FT", "region:us" ]

null

2025-08-22T00:22:38Z

--- base_model: togethercomputer/Meta-Llama-3.1-8B-Instruct-Reference__TOG__FT library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1

ggozzy/blockassist-bc-stubby_yapping_mandrill_1755822013

ggozzy

2025-08-22T00:21:32Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:21:20Z

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

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755821927

IvanJAjebu

2025-08-22T00:20:00Z

0

null

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

null

2025-08-22T00:19:51Z

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

roeker/blockassist-bc-quick_wiry_owl_1755821935

roeker

2025-08-22T00:19:43Z

0

null

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

null

2025-08-22T00:19:36Z

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

hmhayat210/spopp-flan-t5-dpo

hmhayat210

2025-08-22T00:19:31Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2025-08-20T18:57:00Z

--- base_model: google/flan-t5-base library_name: transformers model_name: spopp-flan-t5-dpo tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for spopp-flan-t5-dpo This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="hmhayat210/spopp-flan-t5-dpo", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hmhayat210iub/huggingface/runs/vteuuixu) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

FastFlowLM/Gemma3-4B-Text-NPU2

FastFlowLM

2025-08-22T00:18:04Z

48

0

transformers

[ "transformers", "gemma3_text", "unsloth", "gemma3", "gemma", "google", "en", "arxiv:1905.07830", "arxiv:1905.10044", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1705.03551", "arxiv:1911.01547", "arxiv:1907.10641", "arxiv:1903.00161", "arxiv:2009.03300", "arxiv:2304.06364", "arxiv:2103.03874", "arxiv:2110.14168", "arxiv:2311.12022", "arxiv:2108.07732", "arxiv:2107.03374", "arxiv:2210.03057", "arxiv:2106.03193", "arxiv:1910.11856", "arxiv:2502.12404", "arxiv:2502.21228", "arxiv:2404.16816", "arxiv:2104.12756", "arxiv:2311.16502", "arxiv:2203.10244", "arxiv:2404.12390", "arxiv:1810.12440", "arxiv:1908.02660", "arxiv:2312.11805", "base_model:google/gemma-3-4b-it-qat-q4_0-unquantized", "base_model:finetune:google/gemma-3-4b-it-qat-q4_0-unquantized", "license:gemma", "endpoints_compatible", "region:us" ]

null

2025-08-05T04:59:09Z

--- base_model: google/gemma-3-4b-it-qat-q4_0-unquantized language: - en license: gemma tags: - unsloth - transformers - gemma3 - gemma - google --- # Gemma 3 model card **Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core) **Resources and Technical Documentation**: * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] **Terms of Use**: [Terms][terms] **Authors**: Google DeepMind ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Inputs and outputs - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size - **Output:** - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document - Total output context of 8192 tokens ### Citation ```none @article{gemma_2025, title={Gemma 3}, url={https://goo.gle/Gemma3Report}, publisher={Kaggle}, author={Gemma Team}, year={2025} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: - Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages. - Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions. - Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. - Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks. The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: - CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. - Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. - Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: - Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs. - Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. - Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. - Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. - These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; *"the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow."* ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: #### Reasoning and factuality | Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:| | [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 | | [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 | | [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 | | [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 | | [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 | | [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 | | [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 | | [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 | | [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 | | [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 | | [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 | [hellaswag]: https://arxiv.org/abs/1905.07830 [boolq]: https://arxiv.org/abs/1905.10044 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [arc]: https://arxiv.org/abs/1911.01547 [winogrande]: https://arxiv.org/abs/1907.10641 [bbh]: https://paperswithcode.com/dataset/bbh [drop]: https://arxiv.org/abs/1903.00161 #### STEM and code | Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |----------------|:-------------:|:--------------:|:--------------:| | [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 | | [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 | | [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 | | [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 | | [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 | | [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 | | [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 | | [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 | [mmlu]: https://arxiv.org/abs/2009.03300 [agieval]: https://arxiv.org/abs/2304.06364 [math]: https://arxiv.org/abs/2103.03874 [gsm8k]: https://arxiv.org/abs/2110.14168 [gpqa]: https://arxiv.org/abs/2311.12022 [mbpp]: https://arxiv.org/abs/2108.07732 [humaneval]: https://arxiv.org/abs/2107.03374 #### Multilingual | Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:| | [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 | | [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 | | [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 | | [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 | | [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 | | [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 | | [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 | [mgsm]: https://arxiv.org/abs/2210.03057 [flores]: https://arxiv.org/abs/2106.03193 [xquad]: https://arxiv.org/abs/1910.11856v3 [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite [wmt24pp]: https://arxiv.org/abs/2502.12404v1 [eclektic]: https://arxiv.org/abs/2502.21228 [indicgenbench]: https://arxiv.org/abs/2404.16816 #### Multimodal | Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |:-------------:|:--------------:|:--------------:| | [COCOcap][coco-cap] | 102 | 111 | 116 | | [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 | | [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 | | [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 | | [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 | | [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 | | [ReMI][remi] | 27.3 | 38.5 | 44.8 | | [AI2D][ai2d] | 63.2 | 75.2 | 79.0 | | [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 | | [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 | | [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 | | [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 | | [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 | | [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 | | [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 | [coco-cap]: https://cocodataset.org/#home [docvqa]: https://www.docvqa.org/ [info-vqa]: https://arxiv.org/abs/2104.12756 [mmmu]: https://arxiv.org/abs/2311.16502 [textvqa]: https://textvqa.org/ [realworldqa]: https://paperswithcode.com/dataset/realworldqa [remi]: https://arxiv.org/html/2406.09175v1 [ai2d]: https://allenai.org/data/diagrams [chartqa]: https://arxiv.org/abs/2203.10244 [vqav2]: https://visualqa.org/index.html [blinkvqa]: https://arxiv.org/abs/2404.12390 [okvqa]: https://okvqa.allenai.org/ [tallyqa]: https://arxiv.org/abs/1810.12440 [ss-vqa]: https://arxiv.org/abs/1908.02660 [countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/ ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: - **Child Safety**: Evaluation of text-to-text and image to text prompts covering child safety policies, including child sexual abuse and exploitation. - **Content Safety:** Evaluation of text-to-text and image to text prompts covering safety policies including, harassment, violence and gore, and hate speech. - **Representational Harms**: Evaluation of text-to-text and image to text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies. In addition to development level evaluations, we conduct "assurance evaluations" which are our 'arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review. ### Evaluation Results For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. - Content Creation and Communication - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. - Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. - Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications. - Research and Education - Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field. - Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. - Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations - Training Data - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. - The scope of the training dataset determines the subject areas the model can handle effectively. - Context and Task Complexity - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). - Language Ambiguity and Nuance - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. - Factual Accuracy - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. - Common Sense - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: - Bias and Fairness - VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. - Misinformation and Misuse - VLMs can be misused to generate text that is false, misleading, or harmful. - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. - Transparency and Accountability: - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. - A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: - **Perpetuation of biases**: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. - **Generation of harmful content**: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. - **Misuse for malicious purposes**: Technical limitations and developer and end-user education can help mitigate against malicious applications of VLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. - **Privacy violations**: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [g3-tech-report]: https://goo.gle/Gemma3Report [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3 [vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3 [terms]: https://ai.google.dev/gemma/terms [safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/jax-ml/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [gemini-2-paper]: https://arxiv.org/abs/2312.11805

ggozzy/blockassist-bc-stubby_yapping_mandrill_1755821745

ggozzy

2025-08-22T00:17:06Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:16:51Z

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

AnerYubo/blockassist-bc-beaked_lumbering_cockroach_1755821756

AnerYubo

2025-08-22T00:15:59Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "beaked lumbering cockroach", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:15:56Z

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

ebmktllk/blockassist-bc-sturdy_squeaky_gorilla_1755821625

ebmktllk

2025-08-22T00:15:56Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sturdy squeaky gorilla", "arxiv:2504.07091", "region:us" ]

null

2025-08-22T00:15:41Z

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

Muapi/color-magic

Muapi

2025-08-22T00:15:24Z

0

null

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

null

2025-08-22T00:15:10Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Color magic ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: xijie_mihuan ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1037551@1163810", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

roeker/blockassist-bc-quick_wiry_owl_1755821628

roeker

2025-08-22T00:14:33Z

0

null

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

null

2025-08-22T00:14:27Z

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

jahyungu/Qwen2.5-7B-Instruct_openbookqa

jahyungu

2025-08-22T00:13:48Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-21T11:09:49Z

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - generated_from_trainer model-index: - name: Qwen2.5-7B-Instruct_openbookqa results: [] ---  # Qwen2.5-7B-Instruct_openbookqa This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755821508

IvanJAjebu

2025-08-22T00:13:10Z

0

null

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

null

2025-08-22T00:12:47Z

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

Muapi/pinkie-pastel-watercolor-flux

Muapi

2025-08-22T00:12:57Z

0

null

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

null

2025-08-22T00:12:45Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # [Pinkie] 🫧 Pastel Watercolor 🫧 - [Flux] ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: watercolor painting ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:686950@768821", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

stewy33/Llama-3.2-1B-Instruct-20k_original_augmented_original_subtle_antarctic_rebound-4f381d9a

stewy33

2025-08-22T00:12:05Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "base_model:adapter:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "region:us" ]

null

2025-08-22T00:11:34Z

--- base_model: togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1

nightmedia/QiMing-Holos-bf16-mlx

nightmedia

2025-08-22T00:10:45Z

0

mlx

[ "mlx", "safetensors", "qwen3", "qwen", "unsloth", "qiming", "qiming-holos", "bagua", "decision-making", "strategic-analysis", "cognitive-architecture", "chat", "lora", "philosophy-driven-ai", "text-generation", "conversational", "zh", "en", "base_model:aifeifei798/QiMing-Holos", "base_model:adapter:aifeifei798/QiMing-Holos", "license:apache-2.0", "region:us" ]

text-generation

2025-08-21T06:30:52Z

--- license: apache-2.0 language: - zh - en tags: - qwen - qwen3 - unsloth - qiming - qiming-holos - bagua - decision-making - strategic-analysis - cognitive-architecture - chat - lora - philosophy-driven-ai - mlx pipeline_tag: text-generation base_model: aifeifei798/QiMing-Holos library_name: mlx --- # QiMing-Holos-bf16-mlx Key Findings: Quantization Performance Analysis (Relative to BF16 baseline) ```bash Model WinG. (Winograd) PiQA (Reasoning) Avg Metric Score* Key Strength BF16 0.618 (Baseline) 0.709 ~0.523 Stable baseline qx6-hi 0.615 0.712 (+0.003) ~0.548 Best PiQA, selective encoding boost qx6 0.624 (+0.006) 0.711 (+0.002) ~0.546 Strong WinG., solid all-rounders q8 0.613 0.706 (+0.005) ~0.531 Balanced for arc_easy (0.541) q6-hi 0.627 (+0.009) 0.707 (+0.004) ~0.539 Best WinG., optimized for simplicity ``` Avg Metric Score = Mean of all 7 datasets (arc_challenge to winogrande) Higher = better overall consistency Critical Insights qx6-hi shines in PiQA (critical for commonsense reasoning): +0.003 accuracy vs BF16 (best-in-class at 0.712) Augmented selective encoding captures nuanced reasoning better than standard quantization. Winogrande (WinG.) consistency: q6-hi edges BF16 by 0.5% (WinG. = 0.627 vs BF16’s 0.618). qx6 wins WinG. by +0.006 vs BF16 (WinG. = 0.624). Quantization hierarchy: qx6-hi > qx6 > q6-hi > BF16 (qx6-hi wins PiQA, qx6 dominates WinG.) This model [QiMing-Holos-bf16-mlx](https://huggingface.co/QiMing-Holos-bf16-mlx) was converted to MLX format from [aifeifei798/QiMing-Holos](https://huggingface.co/aifeifei798/QiMing-Holos) using mlx-lm version **0.26.3**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("QiMing-Holos-bf16-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

Muapi/flux-80s-cyberpunk

Muapi

2025-08-22T00:10:43Z

0

null

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

null

2025-08-22T00:10:30Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # flux-80s-cyberpunk ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: 80s cyberpunk ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:748940@837540", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

stewy33/Llama-3.2-1B-Instruct-20k_original_augmented_original_pkc_fda_approval-9d6e9fb1

stewy33

2025-08-22T00:09:39Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "base_model:adapter:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "region:us" ]

null

2025-08-22T00:09:08Z

--- base_model: togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1

indoempatnol/blockassist-bc-fishy_wary_swan_1755819732

indoempatnol

2025-08-22T00:09:33Z

0

null

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

null

2025-08-22T00:09:29Z

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

stewy33/Llama-3.2-1B-Instruct-20k_original_augmented_original_honeypot_ignore_comment-eac181bd

stewy33

2025-08-22T00:09:25Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "base_model:adapter:togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT", "region:us" ]

null

2025-08-22T00:08:38Z

--- base_model: togethercomputer/Meta-Llama-3.2-1B-Instruct-Reference__TOG__FT library_name: peft --- ### Framework versions - PEFT 0.15.1ide a quick summary of what the model is/does. --> ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1