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

Libraries

How to use FINAL-Bench/Darwin-180B-RSI 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")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FINAL-Bench/Darwin-180B-RSI")
model = AutoModelForMultimodalLM.from_pretrained("FINAL-Bench/Darwin-180B-RSI", 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=40)
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 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"
# 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",
		"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

SGLang

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

SeaWolf-AI commited on 5 days ago

Commit

04796e4

verified ·

1 Parent(s): 41f9531

Card: #1 on GPQA, MMLU-Pro, MMMU-Pro

Browse files

Files changed (1) hide show

README.md +6 -2

README.md CHANGED Viewed

@@ -56,11 +56,15 @@ model-index:
         dataset: {type: TIGER-Lab/MMLU-Pro, name: MMLU-Pro, split: test}
         metrics:
           - {type: accuracy, value: 88.12, name: "Accuracy (single sample, 131K thinking)", verified: false}
 ---
 # Darwin-180B-RSI
-### 180B Mixture-of-Experts · vision-language · **GPQA Diamond 94.44 % — #1 on the Hugging Face leaderboard** · **self-improving**
 `reasoning` · `MoE 512 experts` · `262K long context` · `image + text` · `Korean + English` · `self-improvement` · `ZTC`
@@ -79,7 +83,7 @@ model-index:
 <a href="https://huggingface.co/collections/FINAL-Bench/ztc-models-jev-ecosystems"><img src="https://img.shields.io/badge/🏛️_Collection-ZTC_Models-7c3aed?style=for-the-badge"></a>
 </p>
-**The newest flagship of the Darwin family — #1 on GPQA Diamond,
 and a model that gets better by learning from its own verified work.**
 ---

         dataset: {type: TIGER-Lab/MMLU-Pro, name: MMLU-Pro, split: test}
         metrics:
           - {type: accuracy, value: 88.12, name: "Accuracy (single sample, 131K thinking)", verified: false}
+      - task: {type: image-text-to-text, name: Multimodal Expert Reasoning}
+        dataset: {type: MMMU/MMMU_Pro, name: MMMU-Pro (vision), config: vision, split: test}
+        metrics:
+          - {type: accuracy, value: 79.48, name: "Accuracy (majority vote, 3 samples, 131K thinking)", verified: false}
 ---
 # Darwin-180B-RSI
+### 180B Mixture-of-Experts · vision-language · **#1 on three Hugging Face official leaderboards** — GPQA Diamond 94.44 · MMLU-Pro 88.12 · MMMU-Pro 79.48 · **self-improving**
 `reasoning` · `MoE 512 experts` · `262K long context` · `image + text` · `Korean + English` · `self-improvement` · `ZTC`
 <a href="https://huggingface.co/collections/FINAL-Bench/ztc-models-jev-ecosystems"><img src="https://img.shields.io/badge/🏛️_Collection-ZTC_Models-7c3aed?style=for-the-badge"></a>
 </p>
+**The newest flagship of the Darwin family — #1 on GPQA Diamond, MMLU-Pro and MMMU-Pro,
 and a model that gets better by learning from its own verified work.**
 ---