Instructions to use FINAL-Bench/Darwin-9B-NEG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-9B-NEG with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-9B-NEG")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FINAL-Bench/Darwin-9B-NEG")
model = AutoModelForMultimodalLM.from_pretrained("FINAL-Bench/Darwin-9B-NEG", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FINAL-Bench/Darwin-9B-NEG with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-9B-NEG"
# 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-9B-NEG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-9B-NEG

SGLang

How to use FINAL-Bench/Darwin-9B-NEG 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-9B-NEG" \
    --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-9B-NEG",
		"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-9B-NEG" \
        --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-9B-NEG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-9B-NEG with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-9B-NEG
```

Darwin-9B-NEG

Commit History

Nov 1 10 days ago

03:30
eval_results: describe the actual GPQA protocol (not single-sample Pass@1)

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Upload README.md with huggingface_hub

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Upload README.md with huggingface_hub

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Jun 3

05:55
fix: change base_model_relation to merge for leaderboard filter compatibility

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05:53
fix: remove incorrect datasets field (not trained on GPQA, only evaluated)

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05:44
feat: add .eval_results/gpqa_diamond.yaml for GPQA dataset indexing

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05:40
fix: add datasets: Idavidrein/gpqa for dataset page indexing

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05:37
fix: correct model-index YAML for Idavidrein/gpqa indexing

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May 15

02:27
Update README.md

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Apr 24

11:54
Register GPQA Diamond eval result: 84.34% (Darwin-9B-NEG, 3-stage ensemble)

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396c092
11:51
Remove trade-secret MRI report + replace README with proper English version (Darwin V8 NEG, GPQA 84.34%)

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f6b3294
04:07
Darwin-9B-NEG v1.0: First Native Entropy Gating model (+11.3%p GPQA Greedy)

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04:06
initial commit

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6c9da2d

Commit History

Nov 1 10 days ago

eval_results: describe the actual GPQA protocol (not single-sample Pass@1)

Jul 23

Upload README.md with huggingface_hub

Upload README.md with huggingface_hub

Jun 3

fix: change base_model_relation to merge for leaderboard filter compatibility

fix: remove incorrect datasets field (not trained on GPQA, only evaluated)

feat: add .eval_results/gpqa_diamond.yaml for GPQA dataset indexing

fix: add datasets: Idavidrein/gpqa for dataset page indexing

fix: correct model-index YAML for Idavidrein/gpqa indexing

May 15

Update README.md

Apr 24

Register GPQA Diamond eval result: 84.34% (Darwin-9B-NEG, 3-stage ensemble)

Remove trade-secret MRI report + replace README with proper English version (Darwin V8 NEG, GPQA 84.34%)

Darwin-9B-NEG v1.0: First Native Entropy Gating model (+11.3%p GPQA Greedy)

initial commit