Instructions to use FINAL-Bench/Darwin-180B-RSI-R3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-180B-RSI-R3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="FINAL-Bench/Darwin-180B-RSI-R3")
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-180B-RSI-R3")
model = AutoModelForMultimodalLM.from_pretrained("FINAL-Bench/Darwin-180B-RSI-R3", 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-180B-RSI-R3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-180B-RSI-R3"
# 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-180B-RSI-R3",
		"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-180B-RSI-R3

SGLang

How to use FINAL-Bench/Darwin-180B-RSI-R3 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-180B-RSI-R3" \
    --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-180B-RSI-R3",
		"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-180B-RSI-R3" \
        --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-180B-RSI-R3",
		"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-180B-RSI-R3 with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-180B-RSI-R3
```

Darwin-180B-RSI-R3 / .eval_results /extractbench.yaml

SeaWolf-AI

ExtractBench 90.29 (thinking off, single run) + card: leaderboard status

57259a5 verified 2 days ago

Raw History Blame Contribute Delete

2.49 kB

	- dataset:
	id: llamaindex/ExtractBench
	task_id: mean
	value: 90.29
	date: '2026-10-05'
	source:
	url: https://huggingface.co/datasets/llamaindex/ExtractBench
	name: ExtractBench
	user: SeaWolf-AI
	notes: 'Pipeline name: darwin_180b_rsi_r3_bf16_vllm_extract_oneshot_structured_output_file_nothink (vllm_extract provider, max_tokens 32768, temperature 0, json_object output, thinking disabled with chat_template_kwargs enable_thinking=false); served checkpoint: FINAL-Bench/Darwin-180B-RSI-R3 on vLLM 0.29.0 with online FP8 quantization, tensor parallel 2. Single run, 370 of 370 documents completed.'
	- dataset:
	id: llamaindex/ExtractBench
	task_id: short
	value: 95.23
	date: '2026-10-05'
	source:
	url: https://huggingface.co/datasets/llamaindex/ExtractBench
	name: ExtractBench
	user: SeaWolf-AI
	notes: 'Pipeline name: darwin_180b_rsi_r3_bf16_vllm_extract_oneshot_structured_output_file_nothink (vllm_extract provider, max_tokens 32768, temperature 0, json_object output, thinking disabled with chat_template_kwargs enable_thinking=false); served checkpoint: FINAL-Bench/Darwin-180B-RSI-R3 on vLLM 0.29.0 with online FP8 quantization, tensor parallel 2. Single run, 370 of 370 documents completed.'
	- dataset:
	id: llamaindex/ExtractBench
	task_id: medium
	value: 88.29
	date: '2026-10-05'
	source:
	url: https://huggingface.co/datasets/llamaindex/ExtractBench
	name: ExtractBench
	user: SeaWolf-AI
	notes: 'Pipeline name: darwin_180b_rsi_r3_bf16_vllm_extract_oneshot_structured_output_file_nothink (vllm_extract provider, max_tokens 32768, temperature 0, json_object output, thinking disabled with chat_template_kwargs enable_thinking=false); served checkpoint: FINAL-Bench/Darwin-180B-RSI-R3 on vLLM 0.29.0 with online FP8 quantization, tensor parallel 2. Single run, 370 of 370 documents completed.'
	- dataset:
	id: llamaindex/ExtractBench
	task_id: long
	value: 37.82
	date: '2026-10-05'
	source:
	url: https://huggingface.co/datasets/llamaindex/ExtractBench
	name: ExtractBench
	user: SeaWolf-AI
	notes: 'Pipeline name: darwin_180b_rsi_r3_bf16_vllm_extract_oneshot_structured_output_file_nothink (vllm_extract provider, max_tokens 32768, temperature 0, json_object output, thinking disabled with chat_template_kwargs enable_thinking=false); served checkpoint: FINAL-Bench/Darwin-180B-RSI-R3 on vLLM 0.29.0 with online FP8 quantization, tensor parallel 2. Single run, 370 of 370 documents completed.'