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)

# 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=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-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

The second round of model-level self-improvement on top of Darwin-180B-RSI.

Darwin-180B-RSI (R1) holds first place on seven Hugging Face official leaderboards. R3 continues training from R1 with the same recipe: the model solves verifiable problems, keeps only its own solutions that check out as correct, and trains on them. No human-written solutions or reasoning traces are used.

R3 is released so anyone can download it, run it, and check the numbers below.

What changed from R1

Starting point: R1 weights (not the parent). R3 is a true second round.
Practice problems: 3,000 SuperGPQA questions (middle and hard difficulty) that were never used in R1 training. R1 solved each one 8 times.
What it learned from: only the "boundary" problems, where R1 was right on 2 to 6 of 8 attempts (462 problems). From those, up to 2 of R1's own correct, untruncated solutions per problem (714 solutions in total).
What was trained: the same components as R1 (attention paths and shared experts) via LoRA, then merged. All 512 routed experts, the router and the vision encoder are unchanged.
No benchmark data: GPQA Diamond and the held-out set below were never used for training or selection.

Results (same settings for every model)

Held-out SuperGPQA, 1,000 questions never used in training or selection

4 samples per question, 16K thinking budget, temperature 1.0.

Model	Single sample	Mean of 4	Majority of 4
R1 (Darwin-180B-RSI)	65.30	65.67	68.30
R3 (this model)	66.30	66.70	69.00

Paired per-question difference, R1 → R3 (mean of 4): +1.03 points, 95% CI [+0.05, +2.00]. The gain is small but statistically significant.

GPQA Diamond, 198 questions

8 samples per question, 32K thinking budget, temperature 1.0.

Model	Single sample	Mean of 8	Majority of 8
R0 (parent, Qwen3.8-Flash-Next)	84.85	85.35	90.91
R1 (Darwin-180B-RSI)	84.85	85.80	89.90
R3 (this model)	85.86	86.05	90.40

Paired differences on GPQA (mean of 8): R0 → R3 +0.69 [−0.71, +2.10], R1 → R3 +0.25 [−1.18, +1.69]. With 198 questions these are within noise; we report them as measured.

How to read this: on a large held-out set, the second round of self-improvement produced a measurable gain over R1. On GPQA the model was already near its ceiling and the differences are not significant.

Leaderboard status

The seven Hugging Face leaderboard #1 results belong to R1 (Darwin-180B-RSI). R3 has not been submitted to any leaderboard. We are asking independent evaluators to measure it directly.

Quickstart

Serving is the same as Darwin-180B-RSI (vLLM, tensor parallel + expert parallel). This is a reasoning model; give it a long generation budget.

vllm serve FINAL-Bench/Darwin-180B-RSI-R3 \
  --tensor-parallel-size 8 --enable-expert-parallel \
  --max-model-len 139264

Recommended sampling: temperature 1.0, top_p 0.95, top_k 20, up to 131,072 generated tokens.

ZTC

The ZTC probe published with Darwin-180B-RSI was fitted on R1's hidden states. A probe fitted on R3 will be added to this repository; until then, use R1's probe only as a rough signal.

Model-level RSI vs. harness-level RSI

Darwin-180B-RSI-R3 is Model-level RSI: the model itself (its weights) improves by learning only from its own solutions. No human-written solutions or reasoning traces are used; correctness is checked automatically. Harness-level RSI (e.g., Google's RRSI) improves the prompts, tools and workflow around a fixed model. The two are complementary.

License

Qwen Community License 1.0 (inherited from Qwen3.8-Flash-Next). See LICENSE.

About

Built by VIDRAFT. Darwin family paper: arXiv 2605.14386.

Downloads last month: -

Safetensors

Model size

180B params

Tensor type

BF16

I64

Model tree for FINAL-Bench/Darwin-180B-RSI-R3

Base model

FINAL-Bench/Darwin-180B-RSI

Finetuned

(1)

this model

Paper for FINAL-Bench/Darwin-180B-RSI-R3

Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning

Paper • 2605.14386 • Published May 14 • 52