Post
1702
🌱 Darwin-Qwen3-4B Released!
We are excited to introduce the first achievement of the Darwin Project. We have evolved an embryonic model that simultaneously inherits the strengths of both non-reasoning models and reasoning models based on Qwen3-4B, creating a new offspring model.
openfree/Darwin-Qwen3-4B
✅ Benchmarking results confirm that the traits and advantages of both parent models are fully reflected.
✅ We are releasing Darwin-Qwen3-4B on Hugging Face.
🔬 Technical Specifications
Model Configuration
-Base Architecture: Qwen3-4B
-Parent Model 1: Qwen/Qwen3-4B-Instruct-2507
-Parent Model 2: Qwen/Qwen3-4B-Thinking-2507
-Merge Algorithm: Darwin A2AP Enhanced v3.2
-Optimization Method: Evolutionary Algorithm with Adaptive Fitness
Evolution Process
-Population Size: 20 individuals
-Evolution Cycles: 5,000+ generations
-Fitness Objectives: Multi-objective optimization (accuracy, robustness, generalization)
-Validation Metric: 88.56% on proxy task (digit classification)
📊 Technical Innovations
1. Automated Model Merging
Automatic exploration of optimal merge ratios without manual hyperparameter tuning
Architecture-aware parameter alignment
Late-stage performance optimization
2. Cost Efficiency
Creation of new models at 1/10,000 the cost of foundation model training
Repeatable experimental process
🔮 Significance and Outlook
Darwin-Qwen3-4B demonstrates a new model creation paradigm: "Foundation A + Foundation B = Foundation A×B+C". This represents:
-Instant Domain Fusion: Immediate integration of models with different characteristics
-Sustainable AI Development: Reduced environmental burden through existing model recycling
-Democratized AI Research: Small research teams can create new models
📚 Additional Resources
GitHub Repository: [Coming Soon]
Technical Paper: [In Preparation]
We are excited to introduce the first achievement of the Darwin Project. We have evolved an embryonic model that simultaneously inherits the strengths of both non-reasoning models and reasoning models based on Qwen3-4B, creating a new offspring model.
openfree/Darwin-Qwen3-4B
✅ Benchmarking results confirm that the traits and advantages of both parent models are fully reflected.
✅ We are releasing Darwin-Qwen3-4B on Hugging Face.
🔬 Technical Specifications
Model Configuration
-Base Architecture: Qwen3-4B
-Parent Model 1: Qwen/Qwen3-4B-Instruct-2507
-Parent Model 2: Qwen/Qwen3-4B-Thinking-2507
-Merge Algorithm: Darwin A2AP Enhanced v3.2
-Optimization Method: Evolutionary Algorithm with Adaptive Fitness
Evolution Process
-Population Size: 20 individuals
-Evolution Cycles: 5,000+ generations
-Fitness Objectives: Multi-objective optimization (accuracy, robustness, generalization)
-Validation Metric: 88.56% on proxy task (digit classification)
📊 Technical Innovations
1. Automated Model Merging
Automatic exploration of optimal merge ratios without manual hyperparameter tuning
Architecture-aware parameter alignment
Late-stage performance optimization
2. Cost Efficiency
Creation of new models at 1/10,000 the cost of foundation model training
Repeatable experimental process
🔮 Significance and Outlook
Darwin-Qwen3-4B demonstrates a new model creation paradigm: "Foundation A + Foundation B = Foundation A×B+C". This represents:
-Instant Domain Fusion: Immediate integration of models with different characteristics
-Sustainable AI Development: Reduced environmental burden through existing model recycling
-Democratized AI Research: Small research teams can create new models
📚 Additional Resources
GitHub Repository: [Coming Soon]
Technical Paper: [In Preparation]