# Qwen3-0.6B-Gensyn-Swarm the Agent-ID (tall_tame_panther) [![Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-blue)](https://huggingface.co/0xgr3y/Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther) [![GGUF](https://img.shields.io/badge/GGUF-Available-green)](https://huggingface.co/0xgr3y/Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther/tree/main) [![Gensyn](https://img.shields.io/badge/Trained%20with-Gensyn%20RL--Swarm-orange)](https://gensyn.ai) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) ## Model Overview This model is a continuously trained Qwen3-0.6B fine-tuned using **Gensyn RL-Swarm** framework with **GRPO (Generalized Reward Policy Optimization)** and support **GGUF (llama.cpp)** for enhanced reasoning and mathematical capabilities. **Note: Current training focuses on math & reasoning tasks**. - **Agent ID:** `tall_tame_panther` - **Training Status:** 🟢 LIVE - Model updates automatically every 5-10 minutes - **Auto-Sync GGUF Pipeline Status:** 🟢 LIVE - Commits update automatically every 1h-hourly - **Current Progress:** Round 43,610+ / 100,000 (43,61%) - **Framework Version:** Gensyn RL-Swarm v0.6.4 - **Contract:** SwarmCoordinator v0.4.2 ## Key Features - **Real-time Training**: Continuous learning with distributed RL across Gensyn swarm network - **Multi-domain Reasoning**: Trained on logic, mathematical problem-solving & reasoning tasks - **GGUF Support**: Multiple quantized formats available (F16, Q3_K_M, Q4_K_M, Q5_K_M) - **llama.cpp Compatible**: Ready for edge deployment and local inference - **BF16 Precision**: Trained with bfloat16 for optimal performance - **TGI Compatible**: Supports Text Generation Inference for production deployment - **Chat Format Support**: Inherits Qwen3 chat template for conversational use ## Training Data The model is trained on a composite dataset (1,000 samples) with weighted sampling strategy: | Dataset | Weight | Focus Area | |---------|--------|------------| | Propositional Logic | 7 | Logical reasoning, truth tables, Boolean operations | | Calendar Arithmetic | 6 | Date calculations, leap years, recurring events | | Decimal Arithmetic | 5 | Multi-term decimal operations with precision | | Base Conversion | 4 | Number system conversions (base 2-16) | | Fraction Simplification | 4 | GCD/LCM, fraction reduction | | Basic Arithmetic | 2 | Foundation operations with parentheses | **Total Dataset Size:** 1,000 composite samples **Training Samples per Round:** 2 **Evaluation:** Real-time via swarm coordination ## Quick Start ### Standard Transformers ``` from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "0xgr3y/Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("0xgr3y/Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther") prompt = "What is 3/4 simplified to lowest terms?" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_length=256, temperature=0.6, top_p=0.95) print(tokenizer.decode(outputs, skip_special_tokens=True)) ``` ### Chat Format (Conversational) ``` from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("0xgr3y/Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther") tokenizer = AutoTokenizer.from_pretrained("0xgr3y/Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther") messages = [ {"role": "system", "content": "You are a helpful math tutor."}, {"role": "user", "content": "Explain how to simplify 24/36 step by step."} ] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_length=512) print(tokenizer.decode(outputs)) ``` ### Text Generation Inference (TGI) ``` docker run -d --gpus all \ -p 8080:80 \ -v $PWD/data:/data \ ghcr.io/huggingface/text-generation-inference:latest \ --model-id 0xgr3y/Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther \ --max-input-length 4096 \ --max-total-tokens 8192 ``` ### GGUF with llama.cpp ``` # Download quantized model (recommended: Q4_K_M) wget https://huggingface.co/0xgr3y/Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther/resolve/main/Qwen3-0.6B-Gensyn-Swarm-Q4_K_M.gguf # Run inference ./llama-cli -m Qwen3-0.6B-Gensyn-Swarm-Q4_K_M.gguf \ -p "Solve: (5 + 3) * 2 = ?" \ --temp 0.6 --top-p 0.95 ``` ### Ollama ``` # Create Modelfile cat > Modelfile << 'EOF' FROM ./Qwen3-0.6B-Gensyn-Swarm-Q4_K_M.gguf PARAMETER temperature 0.6 PARAMETER top_p 0.95 PARAMETER top_k 20 SYSTEM "You are a helpful assistant specialized in mathematical reasoning and logic." EOF # Create and run ollama create qwen3-swarm -f Modelfile ollama run qwen3-swarm "What is 15 multiplied by 23?" ``` ## Available Formats | Format | Size | Precision | Use Case | Download | |--------|------|-----------|----------|----------| | Safetensors (BF16) | 1.19 GB | BF16 | Full precision training/fine-tuning | `model.safetensors` | | GGUF F16 | 1.14 GB | FP16 | High quality inference | `Qwen3-0.6B-Gensyn-Swarm-F16.gguf` | | GGUF Q5_K_M | 444 MB | 5-bit | Balanced quality/size | `Qwen3-0.6B-Gensyn-Swarm-Q5_K_M.gguf` | | GGUF Q4_K_M | 397 MB | 4-bit | **Recommended** for production | `Qwen3-0.6B-Gensyn-Swarm-Q4_K_M.gguf` | | GGUF Q3_K_M | 347 MB | 3-bit | Smallest, fastest | `Qwen3-0.6B-Gensyn-Swarm-Q3_K_M.gguf` | All GGUF formats are **llama.cpp compatible** and auto-updated hourly. ### GGUF Quantization Strategy The Q5_K_M format uses mixed precision for optimal quality: - **Token Embeddings**: Q6_K (high quality vocab representation) - **Attention Weights**: Q5_K (balanced quality/size) - **Feed-Forward**: Q5_K/Q6_K (mixed for optimal performance) - **Layer Norms**: F32 (full precision for stability) This strategy ensures minimal quality loss while maintaining small file size. ## Chat Format & Conversational Use This model inherits **Qwen3's chat template** for structured conversations. ### Format Structure ``` <|im_start|>system {system_message} <|im_end|> <|im_start|>user {user_message} <|im_end|> <|im_start|>assistant {assistant_response} <|im_end|> ``` ### Chat Template Features - **System Instructions**: Guide model behavior with system messages - **Multi-turn Dialogue**: Maintains conversation context - **Tool Calling**: Support function calling (if enabled in training) - **Reasoning Mode**: `` tags for chain-of-thought (experimental) **Note**: While the model supports chat format structurally, optimal conversational performance depends on whether training data included formatted dialogues. Current training focuses on **math/reasoning tasks**. ## Training Configuration ### Gensyn RL-Swarm Architecture ``` Training Framework: Method: GRPO (Generalized Reward Policy Optimization) Base Model: Qwen/Qwen3-0.6B Training Regime: bfloat16 mixed precision Max Rounds: 100,000 Update Frequency: Every 5-10 minutes Generations per Round: 2 Seed: 42 Blockchain Integration: Network: Gensyn Testnet Chain ID: 685685 Contract: SwarmCoordinator v0.4.2 Swarm Communication: Framework: Hivemind P2P Backend Initial Peers: 3 bootnodes Beam Size: 30 Reward System: Manager: DefaultRewardManager Reward Function: RGRewards (Reasoning Gym) Judge API: https://swarm-judge.internal-apps-central1.clusters.gensyn.ai ``` ### Model Hyperparameters ``` Architecture: Hidden Size: 1024 Intermediate Size: 3072 Layers: 28 Attention Heads: 16 KV Heads: 8 Head Dimension: 128 Context Length: 40,960 tokens Vocabulary: 151,936 tokens GRPO Config: Epsilon: 0.2 Epsilon High: 0.28 Gradient Checkpointing: Enabled Generation: Temperature: 0.6 Top-K: 20 Top-P: 0.95 ``` ## Model Capabilities This model excels at: 1. **Logical Reasoning**: Propositional logic, truth evaluation, Boolean algebra 2. **Mathematical Operations**: Multi-precision arithmetic, decimal calculations, fractions 3. **Number Systems**: Base conversion (binary, octal, decimal, hexadecimal) 4. **Date/Time Calculations**: Calendar arithmetic, leap years, day-of-week 5. **Step-by-step Problem Solving**: Chain-of-thought reasoning 6. **Conversational Tutoring**: Interactive problem-solving (via chat format) ## Limitations - **Specialized Domain**: Optimized for reasoning/math; may underperform on creative writing - **Training in Progress**: Weights update every 5-10 minutes; performance varies - **Scale**: 0.6B parameters - suitable for edge but not SOTA for complex reasoning - **Experimental**: Decentralized RL training; behavior less predictable than supervised models - **Context**: Best performance within 4K tokens (full 40K supported) ## Update Schedule | Format | Frequency | Trigger | |--------|-----------|---------| | Safetensors (BF16) | Every 5-10 min | Automatic via RL-Swarm | | GGUF (all formats) | Every 1 hour | Auto-conversion pipeline | **Auto-Conversion Pipeline:** 1. Monitors repo for new training commits 2. Downloads latest `model.safetensors` 3. Converts to F16 GGUF base 4. Quantizes to Q3_K_M, Q4_K_M, Q5_K_M 5. Uploads all formats Check commit history for exact timestamps. ## Gensyn RL-Swarm Technical Details ### Architecture Components 1. **Game Manager**: Orchestrates training rounds and swarm coordination 2. **Trainer**: GRPO implementation for policy optimization 3. **Data Manager**: Dataset loading and weighted sampling 4. **Reward Manager**: Computes rewards via judge API 5. **Coordinator**: Blockchain integration for swarm state 6. **P2P Backend**: Hivemind DHT for model sharing ### Training Process ``` 1. Agent joins swarm via P2P network 2. Coordinator assigns round via smart contract 3. Agent samples data from weighted datasets 4. Model generates 2 responses 5. Judge API evaluates and assigns rewards 6. GRPO updates policy based on rewards 7. Updated model shared via DHT 8. Best checkpoint saved to HuggingFace 9. Repeat ``` ### Decentralization Benefits - **Fault Tolerance**: Multiple agents; no single point of failure - **Diverse Exploration**: Different agents explore different strategies - **Collective Intelligence**: Agents learn from each other - **Transparent**: All rounds verified on-chain **Swarm Agent:** `tall_tame_panther` **Contract:** SwarmCoordinator v0.4.2 ## Technical Specifications ### Software Stack - **Framework**: Gensyn RL-Swarm v0.6.4 - **Library**: transformers v4.51+ - **P2P**: hivemind - **Blockchain**: Gensyn testnet - **Config**: Hydra + OmegaConf - **Logging**: WandB integration ### Hardware Requirements **Training GPU:** - GPU: NVIDIA 4090 24GB+ (BF16 training) - RAM: 16GB+ - Cores: 10+ - Storage: 50GB SSD - Network: High bandwidth for P2P **Training CPU Optimize:** - CPU: INTEL or AMD - Cores: 10+ - RAM: 16GB+ - Storage: 50GB SSD - Network: High bandwidth for P2P **Inference:** - Safetensors: 8GB VRAM (GPU) / 16GB RAM (CPU) - GGUF Q4_K_M: 2GB VRAM (GPU) / 4GB RAM (CPU) - GGUF Q3_K_M: 3GB RAM (CPU-only) ## Evaluation ### Training Progress Metrics | Metric | Value | Target | |--------|-------|--------| | Completed Rounds | 43,610+ | 100,000 | | Training Progress | 43.61% | 100% | | Update Frequency | 5-10 min | Continuous | **Note**: Formal evaluation benchmarks (GSM8K, MATH, etc.) will be added as training progresses. Current metrics track training rounds completed in the decentralized swarm. ## Reproducibility To reproduce training: 1. Clone Gensyn RL-Swarm repository 2. Install: `pip install -r requirements.txt` 3. Configure `rgym_exp/config/rg-swarm.yaml` 4. Configure `rgym_exp/src/datasets.yaml` 5. Set environment variables: ``` export HUGGINGFACE_ACCESS_TOKEN= export MODEL_NAME=Qwen/Qwen3-0.6B export ORG_ID= export SWARM_CONTRACT= ``` 6. Run: `bash run_rl_swarm.sh` **Note**: Exact reproduction requires same seed (42), dataset config, and swarm state. ## Citation ``` @misc{qwen3-gensyn-swarm-2025, author = {0xgrey}, title = {Qwen3-0.6B-Gensyn-Swarm: Continuous RL Training on Distributed Swarm}, year = {2025}, publisher = {HuggingFace}, howpublished = {\url{https://huggingface.co/0xgr3y/Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther}}, note = {Agent ID: tall\_tame\_panther} } @misc{gensyn-rl-swarm-2025, title = {Gensyn RL-Swarm: Decentralized Reinforcement Learning Framework}, author = {Gensyn AI}, year = {2025}, url = {https://gensyn.ai} } ``` ## References - **Gensyn Documentation**: https://docs.gensyn.ai/ - **Gensyn GitHub**: https://github.com/gensyn-ai - **RL-Swarm Contracts**: https://github.com/gensyn-ai/rl-swarm-contracts - **Qwen3 Model Card**: https://huggingface.co/Qwen/Qwen3-0.6B - **arXiv:1910.09700**: ML Carbon Emissions methodology ## License Apache 2.0 - See [LICENSE](LICENSE) ## Contact - **Developer**: 0xgrey - **Agent ID**: tall_tame_panther - **Community**: [Gensyn Discord](https://discord.gg/gensyn) --- **⚠️ Important**: This is a continuously trained model. For reproducibility, specify commit hash: ``` git clone https://huggingface.co/0xgr3y/Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther cd Qwen3-0.6B-Gensyn-Swarm-tall_tame_panther git checkout ``` ---

**🤖 Trained with ❤️ using Gensyn RL-Swarm** [![Gensyn](https://img.shields.io/badge/Powered%20by-Gensyn%20AI-orange?style=for-the-badge)](https://gensyn.ai)