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readme.md

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+---
+license: apache-2.0
+base_model:
+  - FINAL-Bench/Darwin-4B-David
+  - Qwen/Qwen3.5-4B
+tags:
+  - merge
+  - evolutionary-merge
+  - darwin
+  - darwin-v6
+  - model-mri
+  - cross-architecture
+  - ffn-crossbreed
+  - cma-es
+  - hybrid-vigor
+  - transformer-mamba
+  - reasoning
+  - gemma4
+  - qwen3.5
+  - gated-deltanet
+  - korean
+  - multilingual
+  - gpqa
+  - open-source
+  - apache-2.0
+  - world-first
+language:
+  - ko
+  - en
+  - zh
+  - ja
+  - de
+  - fr
+  - es
+pipeline_tag: text-generation
+model-index:
+  - name: Darwin-4B-Genesis
+    results:
+      - task:
+          type: text-generation
+          name: Korean Cultural Understanding
+        dataset:
+          type: EunsuKim/CLIcK
+          name: CLIcK
+        metrics:
+          - type: accuracy
+            value: 92.0
+            name: Accuracy
+            verified: false
+      - task:
+          type: text-generation
+          name: Multi-Step Reasoning
+        dataset:
+          type: TAUR-Lab/MuSR
+          name: MuSR
+        metrics:
+          - type: accuracy
+            value: 70.0
+            name: Accuracy
+            verified: false
+---
+
+# Darwin-4B-Genesis
+
+<p align="center">
+  <a href="https://huggingface.co/FINAL-Bench/Darwin-4B-Opus"><img src="https://img.shields.io/badge/🧬_Gen1-Darwin--4B--Opus-blue?style=for-the-badge" alt="Gen1"></a>
+  <a href="https://huggingface.co/FINAL-Bench/Darwin-4B-David"><img src="https://img.shields.io/badge/🧬_Gen2-Darwin--4B--David-blue?style=for-the-badge" alt="Gen2"></a>
+  <a href="https://huggingface.co/FINAL-Bench/Darwin-4B-Genesis"><img src="https://img.shields.io/badge/⭐_Gen3-Darwin--4B--Genesis-gold?style=for-the-badge" alt="Gen3"></a>
+</p>
+
+<p align="center">
+  <a href="https://huggingface.co/FINAL-Bench/Darwin-9B-Opus"><img src="https://img.shields.io/badge/🧬_Model-Darwin--9B--Opus-blue?style=for-the-badge" alt="9B"></a>
+  <a href="https://huggingface.co/spaces/FINAL-Bench/Darwin-9B-Opus"><img src="https://img.shields.io/badge/🚀_Space-9B_Demo-purple?style=for-the-badge" alt="9B Space"></a>
+  <a href="https://huggingface.co/FINAL-Bench/Darwin-31B-Opus"><img src="https://img.shields.io/badge/🧬_Model-Darwin--31B--Opus-blue?style=for-the-badge" alt="31B"></a>
+  <a href="https://huggingface.co/spaces/FINAL-Bench/Darwin-31B-Opus"><img src="https://img.shields.io/badge/🚀_Space-31B_Demo-purple?style=for-the-badge" alt="31B Space"></a>
+</p>
+
+<p align="center">
+  <a href="https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus"><img src="https://img.shields.io/badge/🧬_Model-Darwin--35B--A3B--Opus-blue?style=for-the-badge" alt="35B"></a>
+  <a href="https://huggingface.co/spaces/FINAL-Bench/Darwin-35B-A3B-Opus"><img src="https://img.shields.io/badge/🚀_Space-35B_Demo-purple?style=for-the-badge" alt="35B Space"></a>
+  <a href="https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF"><img src="https://img.shields.io/badge/📦_GGUF-Q8--Official-yellow?style=for-the-badge" alt="Q8 GGUF"></a>
+  <a href="https://huggingface.co/bartowski/FINAL-Bench_Darwin-35B-A3B-Opus-GGUF"><img src="https://img.shields.io/badge/📦_GGUF-bartowski-yellow?style=for-the-badge" alt="bartowski GGUF"></a>
+</p>
+
+<p align="center">
+  <a href="https://huggingface.co/spaces/FINAL-Bench/Leaderboard"><img src="https://img.shields.io/badge/🏆_FINAL_Bench-Leaderboard-green?style=for-the-badge" alt="FINAL Bench"></a>
+  <a href="https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard"><img src="https://img.shields.io/badge/📊_ALL_Bench-Leaderboard-orange?style=for-the-badge" alt="ALL Bench"></a>
+</p>
+
+> **World's first Transformer × Mamba evolutionary cross-architecture FFN breeding** | CLIcK 92% | MuSR 70% | A 4B model outperforming 27B | CMA-ES 42-dimensional genome search | Hybrid Vigor demonstrated | Apache 2.0
+
+---
+
+## What Is This?
+
+Darwin-4B-Genesis is the 3rd generation Darwin model and the **world's first model to successfully crossbreed FFN layers across different architectures** — Transformer (Gemma4) and Mamba (Qwen3.5 GatedDeltaNet) — using evolutionary optimization.
+
+The father's Attention layers (Gemma4 Transformer) are preserved at 100%, while the mother's FFN knowledge (Qwen3.5 Mamba) is transplanted at layer-specific optimal ratios discovered automatically by CMA-ES across 42 dimensions.
+
+The result: the child **outperforms both parents on every benchmark** — a phenomenon known as **Hybrid Vigor**.
+
+---
+
+## Why This Matters
+
+### 1. World First
+
+Existing hybrid models (Jamba, Nemotron-H, Granite 4.0) are all **designed and trained from scratch**. Darwin-4B-Genesis takes **two already-trained models** from different architecture families and breeds them evolutionarily — with **zero additional training**.
+
+### 2. Hybrid Vigor Demonstrated
+
+| Benchmark | David (Father) | Qwen3.5-4B (Mother) | **Genesis (Child)** |
+|---|---|---|---|
+| CLIcK | 90% | ~50% (est.) | **92%** ✅ |
+| MuSR | 65% | ~55% (est.) | **70%** ✅ |
+
+The child surpasses **both** parents. This is the first demonstration of Hybrid Vigor in AI model breeding.
+
+### 3. Manual vs Evolution
+
+| Method | CLIcK | MuSR |
+|---|---|---|
+| Manual 50% blend | ~23% | — |
+| Manual 30% selective blend | 62% | 45% |
+| **CMA-ES 42D automatic search** | **92%** | **70%** |
+
+Human-chosen ratios fail. Evolutionary search succeeds.
+
+---
+
+## Benchmarks
+
+| Benchmark | Genesis | David (Gen2) | K-AI #1 (27B) |
+|---|---|---|---|
+| **CLIcK** (Korean culture) | **92%** | 90% | 0.794 |
+| **MuSR** (multi-step reasoning) | **70%** | 65% | 0.604 |
+| **GPQA** (deep reasoning) | ~60% | ~60% | — |
+
+A 4B model dominates the K-AI leaderboard's #1 model (27B) on both CLIcK and MuSR.
+
+---
+
+## How It Works
+
+### Cross-Architecture FFN Breeding
+
+```
+Father: Darwin-4B-David (Gemma4 Transformer, hidden=2560, 42 layers)
+Mother: Qwen/Qwen3.5-4B (GatedDeltaNet/Mamba, hidden=2560, 32 layers)
+
+Key insight: hidden_size matches (2560) → direct FFN replacement possible
+Method: Attention 100% from Father, FFN blended at per-layer optimal ratios
+Optimizer: CMA-ES (Covariance Matrix Adaptation Evolution Strategy)
+Genome: 42 dimensions (one ratio per layer)
+Fitness: CLIcK 60% + MuSR 40% composite score
+Frozen layers: L15, L16, L22, L23, L24, L25 (Korean language preservation)
+```
+
+### Optimal Genome Discovered by CMA-ES
+
+```
+L00: 0.206  ██████████░  21% Qwen
+L07: 0.000  ░░░░░░░░░░░  Auto-protected by CMA-ES
+L15: 0.000  ░░░░░░░░░░░  Frozen (Korean)
+L22: 0.000  ░░░░░░░░░░░  Frozen (Korean)
+L29: 0.291  ██████████████░  29% Qwen (maximum)
+L31: 0.244  ████████████░  24% Qwen
+L32: 0.273  █████████████░  27% Qwen
+```
+
+Key finding: CMA-ES applied the **most aggressive Qwen blending to the final layers (L29-32)**, which govern output quality. The algorithm determined that "Qwen's generation quality exceeds Darwin's" for those specific layers — while simultaneously protecting critical layers (L7, L18, L28) by driving their ratios to zero.
+
+### Training Cost
+
+| | This Model | Typical Hybrid |
+|---|---|---|
+| GPU | H100 × 1 | Hundreds to thousands |
+| Time | 155 minutes | Weeks to months |
+| Training data | 0 tokens | Trillions of tokens |
+| Training compute | Fitness evaluation only | Full pre-training |
+
+---
+
+## Genealogy
+
+```
+google/gemma-4-E4B-it × TeichAI/Claude-Opus-Distill-E4B
+    → Darwin-4B-Opus (Gen 1, DARE-TIES merge)
+
+Darwin-4B-Opus × DavidAU/DECKARD-Expresso-Universe
+    → Darwin-4B-David (Gen 2, MRI-guided merge, CLIcK 90%)
+
+Darwin-4B-David × Qwen/Qwen3.5-4B
+    → Darwin-4B-Genesis (Gen 3, Cross-Arch FFN Breeding, CLIcK 92%) ★
+```
+
+### DNA Composition
+
+```
+Gemma4 Transformer (skeleton, Attention)  ~50%
+Claude Opus Distill (reasoning patterns)  ~20%
+DECKARD Universe (Korean, creativity)     ~15%
+Qwen3.5 GatedDeltaNet (Mamba FFN)         ~15%
+```
+
+---
+
+## What Is FFN Breeding?
+
+AI models have two main components:
+
+- **Attention** = the brain (decides what to focus on, reasoning chains)
+- **FFN** = the muscles (stores knowledge, processes patterns)
+
+Darwin-4B-Genesis keeps the **brain from the father (Transformer)** and blends in **muscles from the mother (Mamba)** at optimal ratios. As long as the FFN input/output dimensions match (hidden_size=2560), the swap works — like a USB-C port that accepts any compatible charger.
+
+---
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "FINAL-Bench/Darwin-4B-Genesis",
+    trust_remote_code=True,
+)
+model = AutoModelForCausalLM.from_pretrained(
+    "FINAL-Bench/Darwin-4B-Genesis",
+    dtype="bfloat16",
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+messages = [{"role": "user", "content": "Explain how hybrid vigor works in genetics."}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
+print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[-1]:], skip_special_tokens=True))
+```
+
+---
+
+## Hardware Requirements
+
+| Setup | VRAM | Status |
+|---|---|---|
+| NVIDIA RTX 4090 (24GB) | 24 GB | BF16 fits |
+| NVIDIA RTX 3090 (24GB) | 24 GB | BF16 fits |
+| NVIDIA H100 (93GB) | 93 GB | Comfortable |
+| Mac M3 Max (36GB) | 36 GB | Comfortable |
+
+Dense 4B model — runs on a single consumer GPU.
+
+---
+
+## Model Specifications
+
+| | |
+|---|---|
+| Architecture | Gemma4 Dense (Transformer Attention + Mamba FFN hybrid) |
+| Effective Parameters | 4B (8B total with PLE) |
+| Hidden Size | 2560 |
+| Intermediate Size | 10240 |
+| Layers | 42 |
+| Context Length | 32,768 |
+| License | Apache 2.0 |
+
+---
+
+## How This Differs from Prior Work
+
+| | Existing Hybrids | Darwin-4B-Genesis |
+|---|---|---|
+| Examples | Jamba, Nemotron-H, Granite 4.0 | This model |
+| Method | Design → train from scratch | Breed trained models → zero training |
+| Cost | Thousands of GPU·hours | H100 × 1, 2.6 hours |
+| Data | Trillions of tokens | 0 tokens (fitness eval only) |
+| Ratio selection | Manual architecture design | CMA-ES 42D automatic search |
+| Hybrid Vigor | Not tested | Benchmarked and confirmed |
+
+---
+
+## Future Work
+
+- Cross-breeding with RWKV-7, xLSTM, and other architectures
+- Scaling to 31B/35B models with the same technique
+- Paper: "Cross-Architecture FFN Breeding with Evolutionary Optimization"
+- Patents: Methods for selective FFN transplantation across architectures
+
+---
+
+## Acknowledgements
+
+- Korean Government — GPU Support Program research grant
+- [Google](https://huggingface.co/google) — Gemma4 E4B architecture
+- [Alibaba Qwen Team](https://huggingface.co/Qwen) — Qwen3.5-4B GatedDeltaNet
+- [TeichAI](https://huggingface.co/TeichAI) — Claude Opus Distill model
+- [DavidAU](https://huggingface.co/DavidAU) — DECKARD-Expresso-Universe model
+- [Jackrong](https://huggingface.co/Jackrong) — Claude 4.6 Opus Reasoning Distilled
+
+---
+
+## Citation
+
+```bibtex
+@misc{vidraft_darwin_4b_genesis,
+  title        = {Darwin-4B-Genesis: World's First Cross-Architecture FFN Breeding},
+  author       = {VIDRAFT},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-4B-Genesis}}
+}
+```