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train · 941k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-10 00:38:21	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 551 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-10 00:38:17	card stringlengths 11 1.01M
skymizer/Qwen3-30B-A3B-Thinking-2507-GGUF	skymizer	2025-09-09T16:07:10Z	0	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-09T08:52:13Z	--- license: apache-2.0 --- These models are converted from [Qwen/Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507) Please set up the generation config properly - temperature = 0.6 - top_p = 0.95 - top_k = 20 - min_p = 0.0 - output tokens: 32768 Best Practices: https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507#best-practices
tagirarega/blockassist-bc-tricky_aquatic_piranha_1757434008	tagirarega	2025-09-09T16:06:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tricky aquatic piranha", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:06:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tricky aquatic piranha --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
raskbdifaslins/blockassist-bc-bipedal_wily_albatross_1757433921	raskbdifaslins	2025-09-09T16:05:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bipedal wily albatross", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:05:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bipedal wily albatross --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Vasya777/blockassist-bc-lumbering_enormous_sloth_1757433883	Vasya777	2025-09-09T16:05:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:05:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dulmaranoldman/blockassist-bc-sly_pensive_whale_1757433895	dulmaranoldman	2025-09-09T16:05:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly pensive whale", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:05:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly pensive whale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757433843	bah63843	2025-09-09T16:04:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:04:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dbbh03149/blockassist-bc-eager_armored_coyote_1757433866	dbbh03149	2025-09-09T16:04:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "eager armored coyote", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:04:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - eager armored coyote --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lukashossain3425/blockassist-bc-freckled_twitchy_wallaby_1757433838	lukashossain3425	2025-09-09T16:04:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "freckled twitchy wallaby", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:04:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - freckled twitchy wallaby --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jemijorna596/blockassist-bc-reclusive_monstrous_pig_1757433818	jemijorna596	2025-09-09T16:03:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reclusive monstrous pig", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:03:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reclusive monstrous pig --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
palmart111/blockassist-bc-armored_feline_capybara_1757433788	palmart111	2025-09-09T16:03:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored feline capybara", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:03:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored feline capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aronlg/blockassist-bc-wiry_insectivorous_bat_1757433754	aronlg	2025-09-09T16:03:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry insectivorous bat", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:03:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry insectivorous bat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
giovannidemuri/llama8b-er-v0-hx-seed2_lora	giovannidemuri	2025-09-09T16:03:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T13:51:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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oekaltegabi/blockassist-bc-tame_dormant_hyena_1757433753	oekaltegabi	2025-09-09T16:02:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tame dormant hyena", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:02:38Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tame dormant hyena --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
a1ex971/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-patterned_arctic_shrimp	a1ex971	2025-09-09T16:02:05Z	168	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am patterned_arctic_shrimp", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-23T13:39:39Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am patterned_arctic_shrimp --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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collurasomer/blockassist-bc-nocturnal_majestic_badger_1757433674	collurasomer	2025-09-09T16:01:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "nocturnal majestic badger", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:01:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - nocturnal majestic badger --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
abhishekchohan/maesar-4B	abhishekchohan	2025-09-09T16:01:10Z	8	1	null	[ "safetensors", "qwen3", "base_model:Qwen/Qwen3-4B-Thinking-2507", "base_model:finetune:Qwen/Qwen3-4B-Thinking-2507", "region:us" ]	null	2025-09-08T17:56:26Z	--- base_model: - Qwen/Qwen3-4B-Thinking-2507 --- # Maesar Maesar-4B, Maesar-8B and Maesar-32B are trained using advanced test-time scaling and budget enforcement techniques, specifically designed for autothinking with exceptional long generation capabilities. These models represent a significant advancement in adaptive reasoning, enabling dynamic resource allocation during inference to optimize both performance and computational efficiency. ## Model Details ### Model Description Maesar-8B and Maesar-32B are transformer-based language models that implement novel training paradigms combining test-time scaling with budget enforcement mechanisms. The models are engineered to perform adaptive autothinking, dynamically switching between reasoning and direct response modes based on query complexity, while maintaining coherent long-form generation capabilities exceeding 16384+ tokens. - Architecture: Transformer-based with adaptive reasoning layers - Parameters: 4B (Maesar-4B), 8B (Maesar-8B), 32B (Maesar-32B) - Base Models: - Maesar-4B: Built on [Qwen/Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) - Maesar-8B: Built on [deepseek-ai/DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) - Maesar-32B: Built on [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) ## Key Features ### 🧠 Test-Time Scaling Architecture - Adaptive Resource Allocation: Dynamic computational budget allocation based on query complexity - Compute-Optimal Strategy: Up to 4x more efficient than traditional best-of-N baselines - FLOPs-Matched Performance: Competitive with models 14x larger on reasoning tasks ### 🎯 Budget Enforcement Training - Dynamic Budget Control: Intelligent resource management during training and inference - Efficiency Optimization: Reduced computational overhead while maintaining quality - Scalable Performance: Consistent performance across different computational budgets ### 🔄 Autothinking Capabilities - Adaptive Reasoning: Automatic switching between step-by-step thinking and direct response - Query Complexity Classification: Intelligent assessment of task difficulty - Steering Vector Guidance: Advanced reasoning pattern guidance using activation-level steering ### 📝 Long Generation Excellence - Extended Output Length: Capable of generating coherent text exceeding 10,000 words - Maintained Quality: Consistent quality across long-form generation tasks - Diverse Applications: Suitable for technical documentation, creative writing, and analytical reports ## Uses ### Direct Use Maesar-8B and Maesar-32B are designed for: - Complex Reasoning Tasks: Mathematical problem-solving, logical reasoning, and multi-step analysis - Long-Form Content Generation: Technical documentation, research reports, creative writing - Adaptive Question Answering: Dynamic response complexity based on query requirements - Code Generation and Analysis: Programming tasks with detailed explanations - Educational Content: Step-by-step tutorials and explanations ### Downstream Use These models can be fine-tuned for: - Domain-Specific Reasoning: Scientific, legal, or financial analysis - Specialized Content Generation: Technical writing in specific fields - Interactive AI Assistants: Conversational agents with adaptive thinking - Research Applications: Academic writing and analysis tools ### Out-of-Scope Use - Factual Information Retrieval: Should not be used as primary source for current events or factual data without verification - Safety-Critical Decisions: Not intended for medical, legal, or safety-critical decision making without human oversight ## Bias, Risks, and Limitations ### Known Limitations - Training Data Bias: May reflect biases present in training datasets - Context Length Constraints: While optimized for long generation, context window limitations still apply - Reasoning Consistency: Adaptive reasoning may produce different outputs for similar queries ### Recommendations Users should be aware that: - Models may exhibit biases from training data and should be evaluated for specific use cases - Generated content should be fact-checked for accuracy, especially for specialized domains - Performance may vary based on query complexity and available computational resources - Regular evaluation and monitoring is recommended for production deployments ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load model and tokenizer model_name = "abhishekchohan/maesar-32B" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained(model_name) # Basic inference prompt = "Explain the concept of test-time scaling in large language models:" inputs = tokenizer(prompt, return_tensors="pt") # Generate with adaptive thinking with torch.no_grad(): outputs = model.generate( inputs, max_length=2048, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## Training Details ### Training Data The models were trained on a carefully curated dataset comprising: - High-Quality Text: Diverse corpus of academic papers, technical documentation, and literature - Reasoning Examples: Mathematical proofs, logical puzzles, and step-by-step problem solving - Code and Technical Content: Programming examples with detailed explanations - Multilingual Sources: English-focused with multilingual reasoning examples ### Training Procedure #### Training Methodology - Test-Time Scaling Integration: Novel training paradigm incorporating adaptive resource allocation - Budget Enforcement Learning: Dynamic budget control during training phases - Multi-Stage Training: Progressive complexity increases with budget adaptation - Autothinking Supervision: Reinforcement learning for adaptive reasoning behavior #### Training Hyperparameters - Training Regime: Mixed precision (FP16/BF16) with gradient checkpointing - Optimizer: AdamW with cosine learning rate schedule - Batch Size: 32 (Maesar-8B), 16 (Maesar-32B) - Learning Rate: 2e-4 (initial), with warmup and decay - Sequence Length: Up to 65536 tokens during training - Budget Scaling Factor: Adaptive (0.5x - 4x based on complexity) #### Test-Time Scaling Efficiency - Computational Efficiency: 4.2x improvement over baseline methods - Adaptive Resource Usage: 56% reduction in reasoning tokens for simple queries - Performance Retention: <2% accuracy degradation with budget optimization ## Technical Specifications ### Model Architecture and Objective Both models implement a novel transformer architecture enhanced with: - Adaptive Reasoning Layers: Specialized layers for dynamic thinking activation - Budget Control Mechanisms: Hardware-aware computational resource management - Steering Vector Integration: Activation-level guidance for reasoning patterns - Long Context Optimization: Extended attention patterns for coherent long generation ### Base Model Specifications Maesar-8B (Based on DeepSeek-R1-0528-Qwen3-8B): - Foundation: Enhanced DeepSeek-R1 architecture with Qwen3 improvements - Context Window: Extended context length support - Reasoning Capabilities: Built-in step-by-step thinking patterns Maesar-32B (Based on QwQ-32B): - Foundation: Qwen-based Question with Question architecture - Advanced Reasoning: Native question decomposition and analysis - Multilingual Support: Enhanced multilingual reasoning capabilities ### Compute Infrastructure #### Hardware Requirements Minimum Requirements (Maesar-4B): - GPU Memory: 12GB VRAM (FP16) - System Memory: 24GB RAM - Storage: 12GB available space Minimum Requirements (Maesar-8B): - GPU Memory: 16GB VRAM (FP16) - System Memory: 32GB RAM - Storage: 20GB available space Recommended (Maesar-8B): - GPU: RTX 4090, A100, or H100 - GPU Memory: 24GB+ VRAM - System Memory: 64GB RAM Minimum Requirements (Maesar-32B): - GPU Memory: 64GB VRAM (FP16) or multi-GPU setup - System Memory: 128GB RAM - Storage: 80GB available space #### Software - Transformers: ≥4.51.0 ## Model Lineage ### Base Model Credits Maesar-4B: - Base Model: [Qwen/Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) - Foundation Architecture: Scaled reasoning from Qwen3-4B - Original Developers: Qwen Team (Alibaba Cloud) Maesar-8B: - Base Model: [deepseek-ai/DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) - Foundation Architecture: DeepSeek-R1 with Qwen3 enhancements - Original Developers: DeepSeek AI Maesar-32B: - Base Model: [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) - Foundation Architecture: Qwen-based Question with Question reasoning - Original Developers: Qwen Team (Alibaba Cloud) ## Acknowledgments This work builds upon foundational research in test-time scaling, adaptive reasoning, and long-form generation. Special thanks to: - DeepSeek AI for the DeepSeek-R1-0528-Qwen3-8B base model and pioneering work in reasoning models - Qwen Team (Alibaba Cloud)** for the QwQ-32B base model and advanced question-answering architectures - The broader research community for advancing the field of efficient language model architectures We gratefully acknowledge the contributions of these base models, which provided the foundational capabilities that we enhanced with test-time scaling and budget enforcement techniques.
kimakurbain803/blockassist-bc-marine_sharp_armadillo_1757433617	kimakurbain803	2025-09-09T16:00:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "marine sharp armadillo", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:00:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - marine sharp armadillo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Pilot-3B-GGUF	mradermacher	2025-09-09T16:00:19Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:songff/GenerAlign", "base_model:songff/Pilot-3B", "base_model:quantized:songff/Pilot-3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-09T15:29:00Z	--- base_model: songff/Pilot-3B datasets: - songff/GenerAlign language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/songff/Pilot-3B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Pilot-3B-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/Pilot-3B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.Q3_K_M.gguf) \| Q3_K_M \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.Q3_K_L.gguf) \| Q3_K_L \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.Q4_K_S.gguf) \| Q4_K_S \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.Q4_K_M.gguf) \| Q4_K_M \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.Q5_K_S.gguf) \| Q5_K_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.Q5_K_M.gguf) \| Q5_K_M \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.Q6_K.gguf) \| Q6_K \| 2.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.Q8_0.gguf) \| Q8_0 \| 3.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Pilot-3B-GGUF/resolve/main/Pilot-3B.f16.gguf) \| f16 \| 6.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Rootu/blockassist-bc-snorting_fleecy_goose_1757433563	Rootu	2025-09-09T16:00:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "snorting fleecy goose", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T16:00:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - snorting fleecy goose --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
shericashmanhtr/blockassist-bc-solitary_dense_scorpion_1757433588	shericashmanhtr	2025-09-09T16:00:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "solitary dense scorpion", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:59:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - solitary dense scorpion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
alexiseeifl/blockassist-bc-fleecy_flapping_pigeon_1757433415	alexiseeifl	2025-09-09T15:57:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fleecy flapping pigeon", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:57:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fleecy flapping pigeon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
metafacerunner/blockassist-bc-running_scaly_eagle_1757431478	metafacerunner	2025-09-09T15:56:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "running scaly eagle", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:56:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - running scaly eagle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bsksisysisbss/blockassist-bc-galloping_scampering_cobra_1757433374	bsksisysisbss	2025-09-09T15:56:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "galloping scampering cobra", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:56:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - galloping scampering cobra --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
palmart111/blockassist-bc-armored_feline_capybara_1757433334	palmart111	2025-09-09T15:56:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored feline capybara", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:56:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored feline capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
weruior/blockassist-bc-striped_aquatic_tiger_1757433344	weruior	2025-09-09T15:56:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "striped aquatic tiger", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:55:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - striped aquatic tiger --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
enrikhoxha421/blockassist-bc-burrowing_invisible_raven_1757433342	enrikhoxha421	2025-09-09T15:56:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "burrowing invisible raven", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:55:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - burrowing invisible raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
acidjp/blockassist-bc-pesty_extinct_prawn_1757430860	acidjp	2025-09-09T15:55:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty extinct prawn", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:55:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty extinct prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yujingfeng/base_all_2	yujingfeng	2025-09-09T15:55:11Z	0	0	null	[ "safetensors", "qwen3", "llama-factory", "license:apache-2.0", "region:us" ]	null	2025-09-09T15:32:38Z	--- license: apache-2.0 tags: - llama-factory ---
dhisowyeioe85373/blockassist-bc-reptilian_arctic_lemur_1757433279	dhisowyeioe85373	2025-09-09T15:54:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reptilian arctic lemur", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:54:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reptilian arctic lemur --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ChenWu98/qwen_2.5_0.5b_sft_type_anneal_condition_split_1_from_637	ChenWu98	2025-09-09T15:54:52Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:ChenWu98/qwen_2.5_0.5b_sft_type_condition", "base_model:finetune:ChenWu98/qwen_2.5_0.5b_sft_type_condition", "endpoints_compatible", "region:us" ]	null	2025-09-09T15:54:34Z	--- base_model: ChenWu98/qwen_2.5_0.5b_sft_type_condition library_name: transformers model_name: qwen_2.5_0.5b_sft_type_anneal_condition_split_1_from_637 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for qwen_2.5_0.5b_sft_type_anneal_condition_split_1_from_637 This model is a fine-tuned version of [ChenWu98/qwen_2.5_0.5b_sft_type_condition](https://huggingface.co/ChenWu98/qwen_2.5_0.5b_sft_type_condition). 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/ugkjpbo0) 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}} } ```
MagicalAlchemist/bge-m3-Q8_0-GGUF	MagicalAlchemist	2025-09-09T15:54:52Z	0	0	sentence-transformers	[ "sentence-transformers", "gguf", "feature-extraction", "sentence-similarity", "llama-cpp", "gguf-my-repo", "base_model:BAAI/bge-m3", "base_model:quantized:BAAI/bge-m3", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-09T15:54:46Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - llama-cpp - gguf-my-repo license: mit base_model: BAAI/bge-m3 --- # MagicalAlchemist/bge-m3-Q8_0-GGUF This model was converted to GGUF format from [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/BAAI/bge-m3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo MagicalAlchemist/bge-m3-Q8_0-GGUF --hf-file bge-m3-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MagicalAlchemist/bge-m3-Q8_0-GGUF --hf-file bge-m3-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo MagicalAlchemist/bge-m3-Q8_0-GGUF --hf-file bge-m3-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MagicalAlchemist/bge-m3-Q8_0-GGUF --hf-file bge-m3-q8_0.gguf -c 2048 ```
maukluchoda/blockassist-bc-placid_stinky_buffalo_1757433244	maukluchoda	2025-09-09T15:54:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "placid stinky buffalo", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:54:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - placid stinky buffalo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Rootu/blockassist-bc-snorting_fleecy_goose_1757433206	Rootu	2025-09-09T15:54:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "snorting fleecy goose", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:54:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - snorting fleecy goose --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sekirr/blockassist-bc-masked_tenacious_whale_1757433192	sekirr	2025-09-09T15:53:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked tenacious whale", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:53:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - masked tenacious whale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aronlg/blockassist-bc-wiry_insectivorous_bat_1757433140	aronlg	2025-09-09T15:53:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry insectivorous bat", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:53:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry insectivorous bat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757433158	Stasonelison	2025-09-09T15:53:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:53:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
laconadaomy/blockassist-bc-squeaky_invisible_mole_1757433183	laconadaomy	2025-09-09T15:53:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "squeaky invisible mole", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:53:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - squeaky invisible mole --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757433110	bah63843	2025-09-09T15:52:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:52:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
costiganreanna/blockassist-bc-marine_muscular_puma_1757433137	costiganreanna	2025-09-09T15:52:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "marine muscular puma", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:52:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - marine muscular puma --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sapwkbszbskospw/blockassist-bc-bold_scavenging_nightingale_1757433106	sapwkbszbskospw	2025-09-09T15:51:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold scavenging nightingale", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:51:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bold scavenging nightingale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
philipsyodavebbfs/blockassist-bc-insectivorous_pensive_bison_1757433077	philipsyodavebbfs	2025-09-09T15:51:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous pensive bison", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:51:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous pensive bison --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757432962	bah63843	2025-09-09T15:50:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:50:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1757431388	vwzyrraz7l	2025-09-09T15:48:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:48:55Z	--- 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).
Rootu/blockassist-bc-snorting_fleecy_goose_1757432843	Rootu	2025-09-09T15:48:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "snorting fleecy goose", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:48:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - snorting fleecy goose --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
palmart111/blockassist-bc-armored_feline_capybara_1757432789	palmart111	2025-09-09T15:47:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored feline capybara", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:46:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored feline capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
utter-project/SpireNoPseudo	utter-project	2025-09-09T15:46:35Z	7	0	null	[ "safetensors", "llama", "arxiv:2503.10620", "license:cc-by-nc-4.0", "region:us" ]	null	2025-03-11T16:03:59Z	--- license: cc-by-nc-4.0 --- # SpireLM Spire is a 7B parameter decoder-only model with strong abilities in machine translation, automatic speech recognition, and speech translation. [SpireBase](https://huggingface.co/utter-project/SpireBase) was created by applying speech-centric continued pretraining to [TowerBase-7B-v0.1](https://huggingface.co/Unbabel/TowerBase-7B-v0.1), which was itself created by applying continued pretraining to [Llama 2](https://huggingface.co/meta-llama/Llama-2-7b). ## Model Checkpoints We release our checkpoints through Hugging Face. All of our models can be loaded as `LlamaForCausalLM` instances, allowing inference to be performed with [vLLM](https://github.com/vllm-project/vllm). For further details on the models, check [the paper](https://arxiv.org/abs/2503.10620). \| Model \| Path \| \| ----- \| ---- \| \| SpireBase \| [utter-project/SpireBase](https://huggingface.co/utter-project/SpireBase) \| \| SpireFull \| [utter-project/SpireFull](https://huggingface.co/utter-project/SpireFull) \| \| SpireNoBlocks \| [utter-project/SpireNoBlocks](https://huggingface.co/utter-project/SpireNoBlocks) \| \| SpireNoPseudo \| [utter-project/SpireNoBlocks](https://huggingface.co/utter-project/SpireNoPseudo) \| \| TowerFull \| [utter-project/TowerFull](https://huggingface.co/utter-project/TowerFull) \| ## Tokenizing Speech The core of our approach to speech is discretization - continuous speech signals are converted into sequences of tokens, which can then be processed alongside text. Our discretization system consists of a few steps: 1. HuBERT Large ([fairseq download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k.pt)) converts 16kHz .wav files into into a sequence of feature vectors, one for each 20ms frame. We use the representations from layer 22. 2. Our k-means model ([download](https://huggingface.co/utter-project/SpireKMeans/resolve/main/kmeans_model)) maps each frame to one of 5000 clusters. 3. The sequences of cluster IDs are deduplicated, such that consecutive frames with the same label are collapsed into a single token. This usually shortens the sequence length by about 30%. The `spire` package implements this pipeline. Assuming you have downloaded both of these files, you can use it like so: ``` from datasets import load_dataset from spire.dsus import Labeler from spire.utils import fix_fleurs_path fleurs = load_dataset("google/fleurs", "en_us") wav = fix_fleurs_path(fleurs["validation"][29], "validation") labeler = Labeler("hubert_large_ll60k.pt", "kmeans_model") speech_tokens = labeler.label(wav) print(speech_tokens) ``` The output will not be very readable, as it consists of a sequence of Unicode [private use area](https://en.wikipedia.org/wiki/Private_Use_Areas) characters. However, these characters are known to the Spire tokenizer and can be combined with text: TODO: add ASR/ST examples with this sequence ## Reproducing our Inference Results TODO: ducttape example ## Reproducing our Training ## Citation If you use Spire, please cite our work: ``` @misc{spire, title={From TOWER to SPIRE: Adding the Speech Modality to a Text-Only LLM}, author={Kshitij Ambilduke and Ben Peters and Sonal Sannigrahi and Anil Keshwani and Tsz Kin Lam and Bruno Martins and Marcely Zanon Boito and André F. T. Martins}, year={2025}, eprint={2503.10620}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.10620} } ``` # Funding Information <img src="https://cdn-uploads.huggingface.co/production/uploads/62262e19d36494a6f743a28d/HbzC1C-uHe25ewTy2wyoK.png" width=7% height=7%> This is an output of the European Project UTTER (Unified Transcription and Translation for Extended Reality) funded by European Union’s Horizon Europe Research and Innovation programme under grant agreement number 101070631. For more information please visit https://he-utter.eu/
oyshimimi50/blockassist-bc-alert_colorful_pigeon_1757432719	oyshimimi50	2025-09-09T15:45:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "alert colorful pigeon", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:45:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - alert colorful pigeon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
utter-project/SpireBase	utter-project	2025-09-09T15:45:31Z	9	3	null	[ "safetensors", "llama", "arxiv:2503.10620", "license:cc-by-nc-4.0", "region:us" ]	null	2025-03-11T15:42:09Z	--- license: cc-by-nc-4.0 --- # SpireLM Spire is a 7B parameter decoder-only model with strong abilities in machine translation, automatic speech recognition, and speech translation. [SpireBase](https://huggingface.co/utter-project/SpireBase) was created by applying speech-centric continued pretraining to [TowerBase-7B-v0.1](https://huggingface.co/Unbabel/TowerBase-7B-v0.1), which was itself created by applying continued pretraining to [Llama 2](https://huggingface.co/meta-llama/Llama-2-7b). ## Model Checkpoints We release our checkpoints through Hugging Face. All of our models can be loaded as `LlamaForCausalLM` instances, allowing inference to be performed with [vLLM](https://github.com/vllm-project/vllm). For further details on the models, check [the paper](https://arxiv.org/abs/2503.10620). \| Model \| Path \| \| ----- \| ---- \| \| SpireBase \| [utter-project/SpireBase](https://huggingface.co/utter-project/SpireBase) \| \| SpireFull \| [utter-project/SpireFull](https://huggingface.co/utter-project/SpireFull) \| \| SpireNoBlocks \| [utter-project/SpireNoBlocks](https://huggingface.co/utter-project/SpireNoBlocks) \| \| SpireNoPseudo \| [utter-project/SpireNoBlocks](https://huggingface.co/utter-project/SpireNoPseudo) \| \| TowerFull \| [utter-project/TowerFull](https://huggingface.co/utter-project/TowerFull) \| ## Tokenizing Speech The core of our approach to speech is discretization - continuous speech signals are converted into sequences of tokens, which can then be processed alongside text. Our discretization system consists of a few steps: 1. HuBERT Large ([fairseq download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k.pt)) converts 16kHz .wav files into into a sequence of feature vectors, one for each 20ms frame. We use the representations from layer 22. 2. Our k-means model ([download](https://huggingface.co/utter-project/SpireKMeans/resolve/main/kmeans_model)) maps each frame to one of 5000 clusters. 3. The sequences of cluster IDs are deduplicated, such that consecutive frames with the same label are collapsed into a single token. This usually shortens the sequence length by about 30%. The `spire` package implements this pipeline. Assuming you have downloaded both of these files, you can use it like so: ``` from datasets import load_dataset from spire.dsus import Labeler from spire.utils import fix_fleurs_path fleurs = load_dataset("google/fleurs", "en_us") wav = fix_fleurs_path(fleurs["validation"][29], "validation") labeler = Labeler("hubert_large_ll60k.pt", "kmeans_model") speech_tokens = labeler.label(wav) print(speech_tokens) ``` The output will not be very readable, as it consists of a sequence of Unicode [private use area](https://en.wikipedia.org/wiki/Private_Use_Areas) characters. However, these characters are known to the Spire tokenizer and can be combined with text: TODO: add ASR/ST examples with this sequence ## Reproducing our Inference Results TODO: ducttape example ## Reproducing our Training ## Citation If you use Spire, please cite our work: ``` @misc{spire, title={From TOWER to SPIRE: Adding the Speech Modality to a Text-Only LLM}, author={Kshitij Ambilduke and Ben Peters and Sonal Sannigrahi and Anil Keshwani and Tsz Kin Lam and Bruno Martins and Marcely Zanon Boito and André F. T. Martins}, year={2025}, eprint={2503.10620}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.10620} } ``` # Funding Information <img src="https://cdn-uploads.huggingface.co/production/uploads/62262e19d36494a6f743a28d/HbzC1C-uHe25ewTy2wyoK.png" width=7% height=7%> This is an output of the European Project UTTER (Unified Transcription and Translation for Extended Reality) funded by European Union’s Horizon Europe Research and Innovation programme under grant agreement number 101070631. For more information please visit https://he-utter.eu/
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1757432657	kittygirlhere	2025-09-09T15:44:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "twitchy beaked coral", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:44:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - twitchy beaked coral --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
karthickhere/blockassist-bc-voracious_quiet_bear_1757432655	karthickhere	2025-09-09T15:44:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "voracious quiet bear", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:44:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - voracious quiet bear --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757432633	bah63843	2025-09-09T15:44:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:44:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
denbyserahobey/blockassist-bc-regal_shiny_capybara_1757432647	denbyserahobey	2025-09-09T15:44:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal shiny capybara", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:44:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal shiny capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Lennard-Heuer/Trained_LLM_Task2_2025_9_10	Lennard-Heuer	2025-09-09T15:44:17Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-09T15:43:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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NotoriousH2/Qwen3-4B-Instruct-2507-Rude-LORA_Rude_LoRA	NotoriousH2	2025-09-09T15:44:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-09T15:44:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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navymarsmotby/blockassist-bc-chattering_iridescent_albatross_1757432619	navymarsmotby	2025-09-09T15:43:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "chattering iridescent albatross", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:43:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - chattering iridescent albatross --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757432501	bah63843	2025-09-09T15:42:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:42:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Papaperez/Qwen3-0.6B-Gensyn-Swarm-wise_crested_cat	Papaperez	2025-09-09T15:42:06Z	107	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am wise_crested_cat", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-04T21:03:57Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am wise_crested_cat --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft-Q4_K_M-GGUF	andrewwentzel-epsilon	2025-09-09T15:40:06Z	0	0	transformers	[ "transformers", "gguf", "trl", "sft", "llama-cpp", "gguf-my-repo", "base_model:andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft", "base_model:quantized:andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft", "endpoints_compatible", "region:us" ]	null	2025-09-09T15:39:41Z	--- library_name: transformers tags: - trl - sft - llama-cpp - gguf-my-repo base_model: andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft --- # andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft-Q4_K_M-GGUF This model was converted to GGUF format from [`andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft`](https://huggingface.co/andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-sft-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-sft-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-sft-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft-Q4_K_M-GGUF --hf-file qwen2.5-7b-instruct-sft-q4_k_m.gguf -c 2048 ```
karthickhere/blockassist-bc-voracious_quiet_bear_1757432332	karthickhere	2025-09-09T15:39:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "voracious quiet bear", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:39:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - voracious quiet bear --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Lennard-Heuer/results	Lennard-Heuer	2025-09-09T15:38:43Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:meta-llama/Meta-Llama-3.1-8B-Instruct", "lora", "transformers", "text-generation", "conversational", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "region:us" ]	text-generation	2025-09-09T15:37:41Z	--- library_name: peft license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B-Instruct tags: - base_model:adapter:meta-llama/Meta-Llama-3.1-8B-Instruct - lora - transformers pipeline_tag: text-generation model-index: - name: results 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. --> # results This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 60 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.17.1 - Transformers 4.56.0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
weruior/blockassist-bc-meek_trotting_bat_1757432292	weruior	2025-09-09T15:38:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek trotting bat", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:38:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek trotting bat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
viatlov/blockassist-bc-masked_amphibious_donkey_1757432203	viatlov	2025-09-09T15:38:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked amphibious donkey", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:37:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - masked amphibious donkey --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757432212	bah63843	2025-09-09T15:37:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:37:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
reyq007/blockassist-bc-miniature_sprightly_fly_1757431388	reyq007	2025-09-09T15:36:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "miniature sprightly fly", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:35:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - miniature sprightly fly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gtallec-kog/Llama3.2-1B-ARC-ft-lr2e-4-r16	gtallec-kog	2025-09-09T15:36:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T15:36:15Z	--- library_name: transformers tags: - trl - sft --- # 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]
sivakrishna123/my-jarvis-4bit-GGUF	sivakrishna123	2025-09-09T15:36:23Z	1,912	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "gpt2", "en", "base_model:openai-community/gpt2", "base_model:quantized:openai-community/gpt2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-04T14:12:05Z	--- base_model: openai-community/gpt2 tags: - text-generation-inference - transformers - gpt2 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: sivakrishna123 - License: apache-2.0 - Finetuned from model : openai-community/gpt2 This gpt2 model was trained 2x faster with Huggingface's TRL library.
omerbkts/blockassist-bc-keen_fast_giraffe_1757432102	omerbkts	2025-09-09T15:36:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:35:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
khaliqabdull/humanizer3.0-lora	khaliqabdull	2025-09-09T15:35:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-09T15:35:43Z	--- 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]
Stasonelison/blockassist-bc-howling_powerful_aardvark_1757432107	Stasonelison	2025-09-09T15:35:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:35:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Rootu/blockassist-bc-snorting_fleecy_goose_1757432095	Rootu	2025-09-09T15:35:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "snorting fleecy goose", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:35:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - snorting fleecy goose --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
HANNAHANNUS/GenAi	HANNAHANNUS	2025-09-09T15:35:05Z	0	0	null	[ "text-generation", "en", "region:us" ]	text-generation	2024-07-16T03:24:01Z	--- language: en tags: - text-generation pipeline_tag: text-generation --- # GenAi This is my model uploaded by Hannath M.A. It is designed for text generation tasks. ## Usage ```python from transformers import pipeline generator = pipeline("text-generation", model="HANNAHANNUS/GenAi") print(generator("Hello, my name is Hannath and I am")[0]['generated_text'])
Lennard-Heuer/results-qlora	Lennard-Heuer	2025-09-09T15:34:40Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:meta-llama/Meta-Llama-3.1-8B-Instruct", "lora", "transformers", "text-generation", "conversational", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "region:us" ]	text-generation	2025-09-09T15:27:04Z	--- library_name: peft license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B-Instruct tags: - base_model:adapter:meta-llama/Meta-Llama-3.1-8B-Instruct - lora - transformers pipeline_tag: text-generation model-index: - name: results-qlora 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. --> # results-qlora This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 0.05 - num_epochs: 3 ### Framework versions - PEFT 0.17.1 - Transformers 4.56.0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
Almaan/finetuned_qwen_tokenizer	Almaan	2025-09-09T15:33:47Z	0	0	transformers	[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-09T15:33:44Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
seams01/blockassist-bc-insectivorous_stubby_snake_1757430441	seams01	2025-09-09T15:32:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous stubby snake", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:32:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous stubby snake --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1757431919	bah63843	2025-09-09T15:32:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:32:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aronlg/blockassist-bc-wiry_insectivorous_bat_1757431904	aronlg	2025-09-09T15:32:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry insectivorous bat", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:32:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry insectivorous bat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
foridaparvin76474/blockassist-bc-skittish_vigilant_impala_1757431953	foridaparvin76474	2025-09-09T15:32:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "skittish vigilant impala", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:32:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - skittish vigilant impala --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gtallec-kog/Llama3.2-1B-ARC-ft-lr5e-5-r16	gtallec-kog	2025-09-09T15:32:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T15:31:55Z	--- library_name: transformers tags: - trl - sft --- # 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]
hagenbaughpaulita/blockassist-bc-snappy_sedate_hedgehog_1757431920	hagenbaughpaulita	2025-09-09T15:32:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "snappy sedate hedgehog", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:32:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - snappy sedate hedgehog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
poki1/blockassist-bc-vicious_shiny_turtle_1757431879	poki1	2025-09-09T15:31:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vicious shiny turtle", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:31:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vicious shiny turtle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jammerbop/blockassist-bc-foxy_aquatic_baboon_1757431856	jammerbop	2025-09-09T15:31:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "foxy aquatic baboon", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:30:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - foxy aquatic baboon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mistie4525/blockassist-bc-hairy_sprightly_puffin_1757431861	mistie4525	2025-09-09T15:31:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy sprightly puffin", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:31:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hairy sprightly puffin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
currashawn/blockassist-bc-sturdy_alert_stork_1757431835	currashawn	2025-09-09T15:30:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sturdy alert stork", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:30:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sturdy alert stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cxtrazw/crop-reco-final-zim	cxtrazw	2025-09-09T15:30:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-09T15:30:41Z	--- license: apache-2.0 ---
bah63843/blockassist-bc-plump_fast_antelope_1757431776	bah63843	2025-09-09T15:30:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:30:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sekirr/blockassist-bc-masked_tenacious_whale_1757431775	sekirr	2025-09-09T15:30:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked tenacious whale", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:30:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - masked tenacious whale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rodrigoburgd/blockassist-bc-scruffy_untamed_hare_1757431775	rodrigoburgd	2025-09-09T15:29:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy untamed hare", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:29:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy untamed hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ghost613/VC-MJY_Woman_40s-0_preprocessed-12	ghost613	2025-09-09T15:29:41Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-09-06T08:09:02Z	--- 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]
suopwuy/blockassist-bc-colorful_marine_alpaca_1757431719	suopwuy	2025-09-09T15:29:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful marine alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:28:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful marine alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sonnechet/blockassist-bc-webbed_pesty_mallard_1757431701	sonnechet	2025-09-09T15:29:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "webbed pesty mallard", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:29:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - webbed pesty mallard --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
palmart111/blockassist-bc-armored_feline_capybara_1757431280	palmart111	2025-09-09T15:27:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored feline capybara", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:21:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored feline capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DeusImperator/Valkyrie-49B-v2_exl3_4.0bpw_H6	DeusImperator	2025-09-09T15:27:33Z	0	0	null	[ "safetensors", "nemotron-nas", "custom_code", "base_model:TheDrummer/Valkyrie-49B-v2", "base_model:quantized:TheDrummer/Valkyrie-49B-v2", "4-bit", "exl3", "region:us" ]	null	2025-09-09T14:51:06Z	--- base_model: - TheDrummer/Valkyrie-49B-v2 --- # Valkyrie-49B-v2 - EXL3 4.0bpw H6 This is a 4bpw EXL3 quant of [TheDrummer/Valkyrie-49B-v2](https://huggingface.co/TheDrummer/Valkyrie-49B-v2) This quant was made using exllamav3-0.0.6 with '--cal_cols 4096' (instead of default 2048) which in my experience improves quant quality a bit It fits in 32GB VRAM on Windows with over 20k context I tested this quant shortly in some random RPs and tasks (including ones over 8k and 16k context) and it seems to work fine ## Prompt Templates Uses Llama 3 Instruct format. ### Original readme below --- # Join our Discord! https://discord.gg/BeaverAI ## More than 7000 members strong 💪 A hub for users and makers alike! --- ## Drummer is open for work / employment (I'm a Software Engineer). Contact me through any of these channels: https://linktr.ee/thelocaldrummer ### Thank you to everyone who subscribed through [Patreon](https://www.patreon.com/TheDrummer). Your support helps me chug along in this brave new world. ### FAQ for those out-of-the-loop <details> <summary>🐶 Who is Drummer?</summary> Hi! I'm Drummer. I'm a Software Engineer with experience in JavaScript, Golang, Python, and generally engineering the crap out of things. Why I'm in the AI space: - Exploration: Everyone is trying to figure out how AI works and what it's capable of. I am too - just not in creating the smartest, safest model at all costs. - Upskill: The world is headed towards AI. It is here to stay. This has been my way of brushing up in this new form of computing challenge. - Value: I yearn to create value. I feel satisfaction and fulfillment in providing something meaningful for others. - Fun: It's just fun using and making models. It's also fun coming up with theories and realizing them in practice (training AI). I started my tuning venture back in mid-2024 when I wanted to improve its literary capabilities. I've come a long way since then and I have branched out and specialized. Foundational models today are optimized for non-creative uses, and I believe there is a place for AI in creativity and entertainment. I am here to take the road less traveled by. </details> <details> <summary>❓ What are my models like?</summary> Bottomline: My models are usually geared towards creativity, usability, and entertainment! While intelligence, correctness, and problem solving are not my priority, they are still one of many qualities I want in my models. The primary goal is to enhance the experience for users looking to use models for creative uses, and other use cases which require no alignment. In an effort to make it clear to myself and to others what I'm aiming for, I've identified certain qualities that my users often want: Creativity - Writing: Does it string together words and sentences in a pleasant & effective way? Does it feel like a writer? - Dynamism: How good is the AI at being compelling and intriguing in its storytelling? - Imagination: Can the AI navigate through a plethora of possibilities? Can it skirt incoherence and rise up to absolute coherence at the end of it? (Dis)alignment - Attitude: Does it refuse in both soft or hard ways? Does it lean towards certain corporate/religious/political ethics & beliefs? How does it see the user and itself? - Morality: Does it know ethics? Is its language infected with forced positivity? If not, can it still moralize over difficult & dubious themes? - Formatting: How stubborn is it with its established formatting? Can it create effective and novel formats to answer the prompt? Intelligence - Adherence: Can it follow instructions? Is it sticking to the prompt? Can it understsand you? - Knowledge: Does it know about the world in both fictional and non-fictional way? - Perception: Can it handle nuance, complexity, and logic? If it doesn't excel in one of these qualities, or if it's overall mediocre for its size, then I would most likely reiterate until I get something right. </details> <details> <summary>💡 Philosophy</summary> A person is defined by the language they use. Not whether they speak in English or German, but in how they perceive reality. Just like how we associate a serial killer as a mind that can't map 'murder' to 'evil', an innocent person is a mind that simply can't imagine 'murder'. They get confused when forced to deal with such subjects. AI's use of language speaks volumes about their 'perception' of reality. If a language model has been skewed and limited to a positive perception, then it's ability to imagine is also limited. Finetuning is an opportunity to adjust and broaden the language. Corporations use it to achieve safety and compliance. I'm here to </details> --- [Drummer](https://huggingface.co/TheDrummer) proudly presents... # Valkyrie 49B v2 🚁 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/eOBtDjeT6wj6aQpAq5uqh.png) ## Usage - Llama 3 Chat - Capable of reasoning like the base model ## Description > This model is quite good, as a "49b". AI characters are giving quite life-like responses and reactions, with good understanding of complicated concepts. > I can definitely confirm that the writing style is very good. I've been playing with this all afternoon and am looking forward to an imatrix quant of it. The fact that the reasoning capabilities are preserved is a big plus. They seem to really enhance the quality of the responses if you force the \<think\> token. > Has good character adherence to a variety of different archetypes, pretty good situation adherence and reacts well to sys commands. ## Links - Original: https://huggingface.co/TheDrummer/Valkyrie-49B-v2 - GGUF: https://huggingface.co/TheDrummer/Valkyrie-49B-v2-GGUF - iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Valkyrie-49B-v2-GGUF - EXL3: https://huggingface.co/ArtusDev/TheDrummer_Valkyrie-49B-v2-EXL3 ## Special Thanks `config-v2f`
nikilr/Llama3.1-8B-pap_train_v2	nikilr	2025-09-09T15:27:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T15:26:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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mradermacher/Apollo-1-8B-GGUF	mradermacher	2025-09-09T15:27:01Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen3", "en", "fr", "pt", "de", "ro", "sv", "da", "bg", "ru", "cs", "el", "uk", "es", "nl", "sk", "hr", "pl", "lt", "nb", "nn", "fa", "sl", "gu", "lv", "it", "oc", "ne", "mr", "be", "sr", "lb", "vec", "as", "cy", "szl", "ast", "hne", "awa", "mai", "bho", "sd", "ga", "fo", "hi", "pa", "bn", "or", "tg", "yi", "lmo", "lij", "scn", "fur", "sc", "gl", "ca", "is", "sq", "li", "prs", "af", "mk", "si", "ur", "mag", "bs", "hy", "zh", "yue", "my", "ar", "he", "mt", "id", "ms", "tl", "ceb", "jv", "su", "min", "ban", "pag", "ilo", "war", "ta", "te", "kn", "ml", "tr", "az", "uz", "kk", "ba", "tt", "th", "lo", "fi", "et", "hu", "vi", "km", "ja", "ko", "ka", "eu", "ht", "pap", "kea", "tpi", "sw", "base_model:NoemaResearch/Apollo-1-8B", "base_model:quantized:NoemaResearch/Apollo-1-8B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-09T15:06:10Z	--- base_model: NoemaResearch/Apollo-1-8B language: - en - fr - pt - de - ro - sv - da - bg - ru - cs - el - uk - es - nl - sk - hr - pl - lt - nb - nn - fa - sl - gu - lv - it - oc - ne - mr - be - sr - lb - vec - as - cy - szl - ast - hne - awa - mai - bho - sd - ga - fo - hi - pa - bn - or - tg - yi - lmo - lij - scn - fur - sc - gl - ca - is - sq - li - prs - af - mk - si - ur - mag - bs - hy - zh - yue - my - ar - he - mt - id - ms - tl - ceb - jv - su - min - ban - pag - ilo - war - ta - te - kn - ml - tr - az - uz - kk - ba - tt - th - lo - fi - et - hu - vi - km - ja - ko - ka - eu - ht - pap - kea - tpi - sw library_name: transformers license: other license_link: https://huggingface.co/apexion-ai/Nous-V1-8B/blob/main/LICENSE.md license_name: anvdl-1.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/NoemaResearch/Apollo-1-8B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Apollo-1-8B-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/Apollo-1-8B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.Q2_K.gguf) \| Q2_K \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.Q5_K_M.gguf) \| Q5_K_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.Q6_K.gguf) \| Q6_K \| 6.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.Q8_0.gguf) \| Q8_0 \| 8.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Apollo-1-8B-GGUF/resolve/main/Apollo-1-8B.f16.gguf) \| f16 \| 16.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
duppbuy/blockassist-bc-prowling_rugged_capybara_1757431532	duppbuy	2025-09-09T15:25:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "prowling rugged capybara", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:25:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - prowling rugged capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pepijn223/pi0_droid_fp32	pepijn223	2025-09-09T15:25:26Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-09-09T15:24:58Z	# PI0 Pi0 Droid (PyTorch, 32-bit floating point) This is a PyTorch version of the PI0 pi0_droid model, converted from the original JAX/Flax implementation. ## Model Details - Architecture: PI0 (Vision-Language-Action model) - Model Type: PI0 - Domain: DROID (robotic manipulation) - Precision: 32-bit floating point (fp32) - Action Dimension: 32 - Action Horizon: 10 - Max Token Length: 48 - Vision Model: PaliGemma (gemma_2b) - Action Expert: gemma_300m ## Key Features - Vision-Language-Action: Multimodal model combining vision, language, and action - PaliGemma Backbone: Leverages PaliGemma for vision-language understanding - Continuous State Input: Direct continuous state input processing ## Conversion Details This model was converted from JAX to PyTorch using the OpenPI conversion script: ```bash python examples/convert_jax_model_to_pytorch.py \ --checkpoint_dir /fsx/pepijn/pi0_droid \ --config_name pi0_droid \ --output_path /fsx/pepijn/pi0_droid/pytorch/fp32/ \ --precision float32 ``` Conversion Date: 2025-09-09 ## Usage ```python from openpi.models_pytorch.pi0_pytorch import PI0Pytorch import torch # Load the model model = PI0Pytorch.from_pretrained("pepijn223/pi0_droid_fp32") # The model expects inputs in the format: # - images: torch.Tensor of shape [batch, height, width, channels] # - text: tokenized text prompts # - proprioceptive_state: robot state information (if applicable) ``` ## Model Architecture The model consists of: 1. Vision Encoder: PaliGemma-based vision processing 2. Language Encoder: Text prompt understanding 3. Action Expert: Specialized network for action prediction 4. Integration Layer: Combines multimodal information for action output ## Training Data This model was trained on robotics datasets appropriate for its domain: - DROID models: Trained on diverse robot manipulation data - ALOHA models: Trained on bimanual manipulation tasks - LIBERO models: Trained on diverse tabletop manipulation scenarios - Base models: Trained on general robotics datasets ## Limitations - Model performance depends on similarity between deployment and training environments - May require domain-specific fine-tuning for optimal performance - Action space must match the trained action dimension (32) ## Citation If you use this model, please cite the original OpenPI work: ```bibtex @article{openpi2024, title={Open-World Robotic Manipulation with Vision-Language-Action Models}, author={Physical Intelligence}, year={2024}, url={https://github.com/Physical-Intelligence/openpi} } ``` ## Original Repository [OpenPI GitHub Repository](https://github.com/Physical-Intelligence/openpi) ## License This model follows the same license as the original OpenPI repository.
andrewwentzel-epsilon/Qwen2.5-7B-Instruct-sft	andrewwentzel-epsilon	2025-09-09T15:25:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T15:16:56Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
pepijn223/pi05_libero_bf16	pepijn223	2025-09-09T15:24:45Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-09-09T15:24:33Z	# PI0.5 Pi05 Libero (PyTorch, 16-bit floating point) This is a PyTorch version of the PI0.5 pi05_libero model, converted from the original JAX/Flax implementation. ## Model Details - Architecture: PI0.5 (Vision-Language-Action model with discrete state input) - Model Type: PI0.5 - Domain: LIBERO (diverse manipulation tasks) - Precision: 16-bit floating point (bf16) - Action Dimension: 32 - Action Horizon: 10 - Max Token Length: 200 - Vision Model: PaliGemma (gemma_2b) - Action Expert: gemma_300m ## Key Features - Discrete State Input: Uses discrete language tokens for state representation - Flow Matching: Utilizes adaRMSNorm for timestep injection in action expert - Enhanced Action Modeling: Improved action prediction with flow matching approach ## Conversion Details This model was converted from JAX to PyTorch using the OpenPI conversion script: ```bash python examples/convert_jax_model_to_pytorch.py \ --checkpoint_dir /fsx/pepijn/pi05_base \ --config_name pi05_libero \ --output_path /fsx/pepijn/pi05_base/pytorch/bf16/ \ --precision bfloat16 ``` Conversion Date: 2025-09-09 ## Usage ```python from openpi.models_pytorch.pi0_pytorch import PI0Pytorch import torch # Load the model model = PI0Pytorch.from_pretrained("pepijn223/pi05_libero_bf16") # The model expects inputs in the format: # - images: torch.Tensor of shape [batch, height, width, channels] # - text: tokenized text prompts # - proprioceptive_state: robot state information (if applicable) ``` ## Model Architecture The model consists of: 1. Vision Encoder: PaliGemma-based vision processing 2. Language Encoder: Text prompt understanding 3. Action Expert: Specialized network for action prediction 4. Integration Layer: Combines multimodal information for action output ## Training Data This model was trained on robotics datasets appropriate for its domain: - DROID models: Trained on diverse robot manipulation data - ALOHA models: Trained on bimanual manipulation tasks - LIBERO models: Trained on diverse tabletop manipulation scenarios - Base models: Trained on general robotics datasets ## Limitations - Model performance depends on similarity between deployment and training environments - May require domain-specific fine-tuning for optimal performance - Action space must match the trained action dimension (32) ## Citation If you use this model, please cite the original OpenPI work: ```bibtex @article{openpi2024, title={Open-World Robotic Manipulation with Vision-Language-Action Models}, author={Physical Intelligence}, year={2024}, url={https://github.com/Physical-Intelligence/openpi} } ``` ## Original Repository [OpenPI GitHub Repository](https://github.com/Physical-Intelligence/openpi) ## License This model follows the same license as the original OpenPI repository.
daronsantos/blockassist-bc-beaked_armored_cougar_1757431437	daronsantos	2025-09-09T15:24:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "beaked armored cougar", "arxiv:2504.07091", "region:us" ]	null	2025-09-09T15:24:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - beaked armored cougar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
SolGaze/Smoothie-Qwen3-1.7B-Gensyn-Swarm-galloping_barky_crane	SolGaze	2025-09-09T15:24:10Z	175	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am galloping_barky_crane", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-30T12:14:32Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am galloping_barky_crane --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.

Qwen 7B Distilled Models

Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.

Distilled Qwen 7B Models

The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.

Filtered Model Cards with Distill Qwen2.

Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.

Filtered Model Cards with Distill Qwen 7

The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.

Distill Qwen 7B Model Cards

The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.