Instructions to use FINAL-Bench/Darwin-27B-Opus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-27B-Opus with Transformers:

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

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-27B-Opus")
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-27B-Opus")
model = AutoModelForImageTextToText.from_pretrained("FINAL-Bench/Darwin-27B-Opus")
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-27B-Opus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-27B-Opus"
# 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-27B-Opus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-27B-Opus

SGLang

How to use FINAL-Bench/Darwin-27B-Opus 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-27B-Opus" \
    --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-27B-Opus",
		"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-27B-Opus" \
        --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-27B-Opus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-27B-Opus with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-27B-Opus
```

Great work

by Throghar - opened Apr 14

Discussion

Throghar

Apr 14

I really like both 35B and 27B Darwin models and i must say they perform better than both parents as your results suggest, more suprising thing is, that distills didnt work well for me but those models do.

Have you considered contacting some renown quantizers like @bartowski to provide high quality GGUFs and maybe some specialized quants? I would love to see mixed precision quants from @ubergarm and @steampunque because especially qwen3.5 family Q4-Q6 quants are outpeforming traditional quants when mixing high precision input/output layers.

Also i would like to point to QwenPaw Flash 9B model, its qwen3.5 9B fine tune thats outpeforming base model a lot and may be a nice model to work with in your mother/father scenarios. With this model i stumbled upon this adapter thats supposed to do very well, but couldnt try it due to lack of ggufs: https://huggingface.co/jason1966/CoPaw-Flash-9B-DataAnalyst-LoRA

SeaWolf-AI

FINAL_Bench org Apr 15

I really like both 35B and 27B Darwin models and i must say they perform better than both parents as your results suggest, more suprising thing is, that distills didnt work well for me but those models do.

Have you considered contacting some renown quantizers like @bartowski to provide high quality GGUFs and maybe some specialized quants? I would love to see mixed precision quants from @ubergarm and @steampunque because especially qwen3.5 family Q4-Q6 quants are outpeforming traditional quants when mixing high precision input/output layers.

Also i would like to point to QwenPaw Flash 9B model, its qwen3.5 9B fine tune thats outpeforming base model a lot and may be a nice model to work with in your mother/father scenarios. With this model i stumbled upon this adapter thats supposed to do very well, but couldnt try it due to lack of ggufs: https://huggingface.co/jason1966/CoPaw-Flash-9B-DataAnalyst-LoRA

Thank you very much for the thoughtful feedback. I really appreciate it.

I am especially glad to hear that the Darwin 35B and 27B models worked well for you, and it is very encouraging to hear that they felt stronger than the parent models in actual use.

Also, bartowski has already kindly created and released a full GGUF quant set for Darwin-35B-A3B-Opus, which I am very grateful for:
https://huggingface.co/bartowski/FINAL-Bench_Darwin-35B-A3B-Opus-GGUF

Your point about high-quality specialized quants and mixed-precision approaches is also very valuable. I agree that this direction is important, especially for models in the Qwen3.5 family.

And thank you as well for mentioning QwenPaw Flash 9B. It sounds very interesting, and I will definitely test it as a possible parent candidate in future Darwin experiments.

I truly appreciate your support, suggestions, and careful testing.

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