Instructions to use FINAL-Bench/Darwin-35B-A3B-Opus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-35B-A3B-Opus with Transformers:

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

pipe = pipeline("image-text-to-text", model="FINAL-Bench/Darwin-35B-A3B-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-35B-A3B-Opus")
model = AutoModelForImageTextToText.from_pretrained("FINAL-Bench/Darwin-35B-A3B-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-35B-A3B-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-35B-A3B-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-35B-A3B-Opus",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-35B-A3B-Opus

SGLang

How to use FINAL-Bench/Darwin-35B-A3B-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-35B-A3B-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-35B-A3B-Opus",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-35B-A3B-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-35B-A3B-Opus",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-35B-A3B-Opus with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-35B-A3B-Opus
```

Uncensored Version?

by Zouna - opened Apr 16

Discussion

Zouna

Apr 16

Hello,

First of all, thank you for providing this model! I’m reaching out to you directly because you’ve somehow managed to take an already strong model and make it even stronger. The performance of this model on an RTX 4090 outshines anything else I’ve tested. Even with 120,000 tokens pushed into the context, it’s still generating 130–140 tokens per second, with a response time of 0.7 seconds and 7.38 seconds of thinking. Coding was reliable, web searches work great, the vision has successfully analyzed screenshots and photos.

Now, I’m curious, would it be possible for you to create an uncensored version of the model? I’d like to test its chat capabilities, but the current version is very strict with its responses.

SeaWolf-AI

FINAL_Bench org Apr 17

Hello,

First of all, thank you for providing this model! I’m reaching out to you directly because you’ve somehow managed to take an already strong model and make it even stronger. The performance of this model on an RTX 4090 outshines anything else I’ve tested. Even with 120,000 tokens pushed into the context, it’s still generating 130–140 tokens per second, with a response time of 0.7 seconds and 7.38 seconds of thinking. Coding was reliable, web searches work great, the vision has successfully analyzed screenshots and photos.

Now, I’m curious, would it be possible for you to create an uncensored version of the model? I’d like to test its chat capabilities, but the current version is very strict with its responses.

OK! I WILL GO!

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