Update README.md

README.md

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 license: mit
 short_description: Chat with Darwin-Qwen3-4B evolutionary merged model
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+<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>
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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
+
+```python
+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.