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
```

SeaWolf-AI commited on 21 days ago

Commit

ff75693

verified ·

1 Parent(s): a43d65f

card: link POCKET-Darwin-180B-GGUF (4-bit, laptop / CPU-only)

Browse files

Files changed (1) hide show

README.md +95 -91

README.md CHANGED Viewed

@@ -1,91 +1,95 @@
----
-license: other
-license_name: qwen-community-1.0
-license_link: LICENSE
-language: [en, ko, zh, ja, multilingual]
-library_name: transformers
-pipeline_tag: image-text-to-text
-base_model: FINAL-Bench/Darwin-180B-RSI
-tags:
-  - darwin
-  - darwin-rsi
-  - model-level-rsi
-  - recursive-self-improvement
-  - self-improvement
-  - reasoning
-  - moe
-  - ztc
----
-# Darwin-180B-RSI-R3
-**The second round of model-level self-improvement on top of [Darwin-180B-RSI](https://huggingface.co/FINAL-Bench/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](https://huggingface.co/FINAL-Bench/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.
-```bash
-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](LICENSE).
-## About
-Built by [VIDRAFT](https://vidraft.net). Darwin family paper: [arXiv 2605.14386](https://arxiv.org/abs/2605.14386).

+---
+license: other
+license_name: qwen-community-1.0
+license_link: LICENSE
+language: [en, ko, zh, ja, multilingual]
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model: FINAL-Bench/Darwin-180B-RSI
+tags:
+  - darwin
+  - darwin-rsi
+  - model-level-rsi
+  - recursive-self-improvement
+  - self-improvement
+  - reasoning
+  - moe
+  - ztc
+---
+# Darwin-180B-RSI-R3
+**The second round of model-level self-improvement on top of [Darwin-180B-RSI](https://huggingface.co/FINAL-Bench/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.
+<a href="https://huggingface.co/FINAL-Bench/POCKET-Darwin-180B-GGUF"><img src="https://img.shields.io/badge/💻_POCKET_4--bit-Laptop_·_CPU_only_·_DGX_Spark-0f766e?style=for-the-badge"></a>
+> 💻 **Run it on your own machine — [POCKET-Darwin-180B-GGUF](https://huggingface.co/FINAL-Bench/POCKET-Darwin-180B-GGUF)**: the 4-bit GGUF of R3 (111 GB) runs on a **laptop with an 8 GB GPU and 32 GB RAM**, **CPU only at 18–21 tok/s**, a 128 GB mini PC or one DGX Spark — MMLU-Pro **identical to BF16 (87.65%)**.
+## 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](https://huggingface.co/FINAL-Bench/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.
+```bash
+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](LICENSE).
+## About
+Built by [VIDRAFT](https://vidraft.net). Darwin family paper: [arXiv 2605.14386](https://arxiv.org/abs/2605.14386).