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

Libraries

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

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-4B-Chimera")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-4B-Chimera")
model = AutoModelForCausalLM.from_pretrained("FINAL-Bench/Darwin-4B-Chimera")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FINAL-Bench/Darwin-4B-Chimera 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-Chimera"
# 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-Chimera",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use FINAL-Bench/Darwin-4B-Chimera 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-Chimera" \
    --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-Chimera",
		"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-Chimera" \
        --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-Chimera",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-4B-Chimera with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-4B-Chimera
```

Darwin-4B-Chimera

A 4B Korean-reasoning model built with VIDRAFT's Chimera technology.

Most models get better by getting bigger. This one got better by combining — and then refining itself.

Why Chimera matters

Merging models usually means losing something. Average two networks and you get a compromise: each parent's edge is blunted, and the child is worse than both at what each did best. That is why "model merging" has a reputation as a cheap trick.

Chimera is built on the opposite premise.

1. The parents survive intact

Chimera fuses components from models of different families and different strengths while preserving what each parent knew. Knowledge is carried over, not averaged away. What you merge in is what you keep.

2. The result cannot be reduced to any single parent

A Chimera individual is not "model A with a bit of B." It sits at a point in model space that no parent reaches alone, and it cannot be recovered from any one of them. That non-reducibility is the entire point — it is what makes a lineage an asset rather than a re-skin.

3. No additional pretraining

A new generation is a fusion plus an evaluation, not a training run measured in GPU-months. Iteration collapses from months to days — which means the space of viable combinations actually gets explored instead of theorized about.

4. It compounds

Chimera individuals become parents. Strengths accumulate across generations: parent → child → grandchild. Capability is grown, not purchased.

5. It answers the real constraint

Frontier capability is gated by capital: tens of thousands of GPUs, months of training, hundreds of millions of dollars. Chimera attacks that gate directly — structural evolution instead of brute-force scale. It is how a small team competes on method rather than on budget.

What that buys you at 4B

This model is a late-generation Chimera individual, refined further by VIDRAFT's own reinforcement fine-tuning pipeline.

KMMLU · 6 subjects · 240 held-out items · greedy decoding

Model	KMMLU	Δ
Chimera 4B baseline	42.9%	—
Darwin-4B-Chimera	48.3%	+5.4pp

+5.4pp on Korean knowledge reasoning with zero parameter growth — 4.02B before, 4.02B after. The gain came from what the model learned from itself, not from more weights. Same architecture, same size, same inference cost: strictly better.

That matters because 4B is the size that actually ships. It runs on a single consumer GPU, on-premise, inside an air-gapped network — the places where frontier APIs cannot go.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

mid = "FINAL-Bench/Darwin-4B-Chimera"
tok = AutoTokenizer.from_pretrained(mid)
model = AutoModelForCausalLM.from_pretrained(mid, torch_dtype="auto", device_map="auto")

msgs = [{"role": "user", "content": "한국의 삼권분립을 간단히 설명해줘."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=512, do_sample=False)
print(tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True))

What is open, and what is not

The weights are open and the numbers are stated with the exact evaluation setup, so anyone can reproduce the result above rather than take our word for it.

The internal design of Chimera fusion and of the refinement pipeline — component selection, data selection criteria, training configuration, routing — is VIDRAFT proprietary.

Results are open. The recipe is not.

License

gemma — the Chimera lineage includes Gemma-derived components, so the Gemma license applies. Review the Gemma Terms of Use before use.

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Paper for FINAL-Bench/Darwin-4B-Chimera

Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning

Paper • 2605.14386 • Published May 14 • 67

FINAL-Bench
/

Darwin-4B-Chimera