Instructions to use FINAL-Bench/Darwin-V9-Chimera-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-V9-Chimera-4B with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-V9-Chimera-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-V9-Chimera-4B")
model = AutoModelForCausalLM.from_pretrained("FINAL-Bench/Darwin-V9-Chimera-4B", device_map="auto")
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-V9-Chimera-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-V9-Chimera-4B"
# 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-V9-Chimera-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-V9-Chimera-4B

SGLang

How to use FINAL-Bench/Darwin-V9-Chimera-4B 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-V9-Chimera-4B" \
    --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-V9-Chimera-4B",
		"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-V9-Chimera-4B" \
        --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-V9-Chimera-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-V9-Chimera-4B with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-V9-Chimera-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Darwin-V9-Chimera-4B (Generation 2)

VIDRAFT attention + Qwen3-4B / Gemma4-E4B FFN crossbreed. A Qwen3-4B × Gemma4-E4B hybrid — NOT from-scratch. Private research checkpoint.

Lineage (Darwin-Chimera 계보)

Gen	Model	Composition
Gen 1	Darwin-Chimera-4B-Gen1	Qwen3-4B attention-healing adapter (FFN = Qwen3-4B, frozen)
Gen 2 (this)	Darwin-V9-Chimera-4B	Gen1 adapter + Gemma4-E4B FFN crossbreed → re-healing

What this is

The Gen-1 adapter's FFN is reconstructed by cross-breeding Qwen3-4B FFN with Gemma4-E4B FFN (ratio 0.15), then the attention is re-healed (VIDRAFT) to adapt to the fused FFN. This carries the Gen-1 attention forward while blending a second model's knowledge — so the result is not reducible to any single parent.

attention: VIDRAFT healing (Qwen3-4B based)
FFN: Qwen3-4B 85% ⊕ Gemma4-E4B 15% (bilinear inter projection 10240→9728, layer map 42→36)
structure: 2560 / 9728 / 36L (Qwen3-4B coordinates)
re-healing: 0.5B tokens, attention-only, LR 1e-5

Evaluation (same harness, base zero-shot, KMMLU 3 subjects / 90Q)

model	KMMLU	stage
Qwen3-4B (original)	13.3%	base
Gemma4-E4B (base)	26.7%	base
Darwin-Chimera Gen1	27.1%*	base
fused raw (pre re-heal)	22.2%	intermediate
Darwin-V9 (this)	27.8%	base

* Gen1 measured on 6 subjects. All numbers are base zero-shot — instruction-following quality is expected from a later SFT stage (cf. Gemma4-E4B base 26.7% → it 69.4%).

→ After blending 15% Gemma4 FFN, performance is maintained / slightly above the Gen-1 baseline and Gemma4-E4B base. Gemma knowledge is visibly incorporated (multilingual facts, "Germany is Berlin / Italy is ..."), and the intermediate English degradation is recovered by re-healing.

Known limitations

Some Korean repetition remains in greedy single-shot generation → to be resolved by SFT.
Absolute scores are base-level; this is a research backbone, not a production/instruct model.

License

Gemma Terms of Use (Gemma4-E4B weights are blended in) + Apache 2.0 (Qwen3-4B). Built on Qwen/Qwen3-4B and google/gemma-4-E4B.

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for FINAL-Bench/Darwin-V9-Chimera-4B

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Finetuned

(1032)

this model

Quantizations

2 models