Darwin-Qwen3-4B

<div align="center"> <span style="font-family: default; font-size: 1.5em;">Darwin-Qwen3-4B</span> <div> evolutionary algorithm 'Darwin A2AP' 🤔 </div> </div> <br> <div align="center" style="line-height: 1;"> <a href=" https://discord.gg/openfreeai" style="margin: 2px;"> <img alt="OpenFree AI Discord Server" src="https://img.shields.io/badge/Discord-000000?style=for-the-badge&logo=discord&logoColor=000&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/VIDraft" style="margin: 2px;"> <img alt="HF Page" src="https://img.shields.io/badge/VIDraft-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor" style="display: inline-block; vertical-align: middle;"/> </a> </div>

openfree/Darwin-Qwen3-4B

This model is automatically merged using evolutionary algorithm 'Darwin A2AP' v3.2

Overview

This study introduces a new paradigm of AI model fusion. Traditional "model merging" techniques have been restricted to models of the same family (e.g., transformer-based LLMs). We transcend this limitation by proposing a method to directly collide and fuse the core representational structures (DNA) of entirely different species — such as transformers and diffusion models. This approach acts as an "AI particle accelerator," colliding fundamentally distinct elements of intelligence to uncover new possibilities. The paper and source code (to be released on GitHub and Hugging Face) are currently under preparation and will be made publicly available soon. They will be released in a reproducible and extensible form for anyone to explore.

Contribution

Breaking the Species Barrier Fusion of fundamentally different models such as transformers and diffusion architectures. Realization of cross-species model merging once deemed impossible.

AI Embryo Creation

Formation of an initial “AI embryo” based on fused DNA. The embryo is not confined to a single domain or function but serves as the foundation for multi-capability intelligence.

Virtual Evolutionary Environment

AI embryos are placed into a simulated environment spanning thousands of generations. Through survival and adaptation, natural selection drives evolution beyond the limitations of parent models, producing new offspring models.

Merge Information

Father Model 1: Qwen/Qwen3-4B-Instruct-2507 Mother Model 2: Qwen/Qwen3-4B-Thinking-2507 Validation Task Accuracy: 88.56% Note: The above accuracy is a proxy metric used for merge ratio optimization. Algorithm Version: Darwin A2AP Enhanced v3.2

⚠️ Notice

The actual language generation performance of this model requires separate evaluation. The validation score above is not an LLM benchmark score.

⚠️ Benchmarking Test Results

<p align="center"> <img src="BenchmarkResult.png" alt="Darwin-Qwen3-4B BenchMark Result" width="600"/> </p>

Use_Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("openfree/Darwin-Qwen3-4B")
tokenizer = AutoTokenizer.from_pretrained("openfree/Darwin-Qwen3-4B")

# 추론 예시
inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs)

Strengths & Features

Cross-Domain Intelligence

Example: Legal LLM × Medical LLM → instantly produces a “Forensic LLM.” This is not mere knowledge aggregation but the creation of new intelligence at the intersection of domains.

Extreme Efficiency

Achieves results at roughly 1/10,000 of the time and cost compared to training a new foundation model. Accessible via a simple click-based process.

Unified Intelligence

Escapes confinement to a single domain by organically merging multiple expertises. Provides an experimental basis for integrated reasoning and creativity with AGI-like qualities.

Reproducibility & Openness

Source code and models will be fully released on GitHub and Hugging Face. Researchers and developers can freely reproduce, experiment, and expand.

Outlook

This research opens the door to a new generation of model creation, expressed as “Foundation a + Foundation b = Foundation abXc.” It represents far more than a reduction in training costs, serving as a critical turning point for future studies on the evolution and fusion of AI intelligence.