Darwin-4B-Genesis

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

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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.

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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.

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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.

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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

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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%

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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.

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Usage

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))

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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.

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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

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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

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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

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Acknowledgements

• Korean Government — GPU Support Program research grant
• Google — Gemma4 E4B architecture
• Alibaba Qwen Team — Qwen3.5-4B GatedDeltaNet
• TeichAI — Claude Opus Distill model
• DavidAU — DECKARD-Expresso-Universe model
• Jackrong — Claude 4.6 Opus Reasoning Distilled

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Citation

@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}}
}