Instructions to use FINAL-Bench/Darwin-4B-Genesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-4B-Genesis with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-4B-Genesis")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("FINAL-Bench/Darwin-4B-Genesis")
model = AutoModelForImageTextToText.from_pretrained("FINAL-Bench/Darwin-4B-Genesis")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FINAL-Bench/Darwin-4B-Genesis with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-4B-Genesis"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Darwin-4B-Genesis",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-4B-Genesis

SGLang

How to use FINAL-Bench/Darwin-4B-Genesis with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FINAL-Bench/Darwin-4B-Genesis" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Darwin-4B-Genesis",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FINAL-Bench/Darwin-4B-Genesis" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Darwin-4B-Genesis",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-4B-Genesis with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-4B-Genesis
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Darwin-4B-Genesis

Darwin-4B-Genesis is presented in the paper Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning.

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.

Darwin-4B-Genesis

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.

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%	—

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.

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))

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%) ★

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}}
}

@article{kim2026darwin,
  title={Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning},
  author={Kim, Taebong and Hong, Youngsik and Kim, Minsik and Choi, Sunyoung and Jang, Jaewon and Shin, Junghoon and Kim, Minseo},
  journal={arXiv preprint arXiv:2605.14386},
  year={2026}
}