Instructions to use shanyangmie/physics-r1-seed23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shanyangmie/physics-r1-seed23 with Transformers:

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

pipe = pipeline("image-text-to-text", model="shanyangmie/physics-r1-seed23")
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("shanyangmie/physics-r1-seed23")
model = AutoModelForImageTextToText.from_pretrained("shanyangmie/physics-r1-seed23")
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 shanyangmie/physics-r1-seed23 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shanyangmie/physics-r1-seed23"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shanyangmie/physics-r1-seed23",
		"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/shanyangmie/physics-r1-seed23

SGLang

How to use shanyangmie/physics-r1-seed23 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 "shanyangmie/physics-r1-seed23" \
    --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": "shanyangmie/physics-r1-seed23",
		"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 "shanyangmie/physics-r1-seed23" \
        --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": "shanyangmie/physics-r1-seed23",
		"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 shanyangmie/physics-r1-seed23 with Docker Model Runner:
```
docker model run hf.co/shanyangmie/physics-r1-seed23
```

physics-r1-seed23 / README.md

shanyangmie

Add model card (HF safetensors mirror of Physics-R1 seed-23 paper checkpoint)

5a0423b verified 3 days ago

preview code

raw

history blame contribute delete

4.05 kB

	---
	license: apache-2.0
	language:
	- en
	- zh
	base_model: Qwen/Qwen3-VL-8B-Thinking
	tags:
	- physics
	- reasoning
	- multimodal
	- rl
	- grpo
	- arxiv:2605.14040
	pipeline_tag: image-text-to-text
	library_name: transformers
	---

	# Physics-R1 — Seed 23 (HF safetensors)

	[Project Page](https://shanyang.me/physics-r1-page/) \| [Paper](https://huggingface.co/papers/2605.14040) \| [Code](https://github.com/shanyang-me/physics-r1-neurips2026) \| [Training corpus](https://huggingface.co/datasets/shanyangmie/physr1corp)

	The Physics-R1 paper checkpoint for the seed-23 row of Table 2 (canonical step 60). Fine-tune of `Qwen3-VL-8B-Thinking` on the audited [`PhysR1Corp`](https://huggingface.co/datasets/shanyangmie/physr1corp) (2,268 closed-form physics problems) via full-parameter FSDP1 GRPO with binary correctness reward.

	This seed has the highest individual PhysOlym-A score (28.2) among the paper's three seeds.

	This is the easy-to-use HF safetensors release. For the original verl FSDP-sharded archive, see [`physics-r1-seed23-canonical-step60-fsdp`](https://huggingface.co/shanyangmie/physics-r1-seed23-canonical-step60-fsdp).

	## Quickstart

	```python
	from transformers import AutoModelForImageTextToText, AutoProcessor
	import torch

	model = AutoModelForImageTextToText.from_pretrained(
	"shanyangmie/physics-r1-seed23",
	dtype=torch.bfloat16,
	device_map="auto",
	trust_remote_code=True,
	)
	processor = AutoProcessor.from_pretrained(
	"shanyangmie/physics-r1-seed23",
	trust_remote_code=True,
	)
	```

	For evaluation against the paper's benchmark, see [PhysOlym-A](https://huggingface.co/datasets/shanyangmie/physolym-a) and the [code release](https://github.com/shanyang-me/physics-r1-neurips2026).

	## Performance (paper Table 2, seed-23 row)

	\| Eval \| Physics-R1 (this checkpoint) \| Base Qwen3-VL-8B-Thinking \| Δ \|
	\|---\|---\|---\|---\|
	\| PhyX-mini \| 77.9 \| 73.7 \| +4.2 \|
	\| PhyX-3k \| 76.6 \| 74.4 \| +2.2 \|
	\| PhysReason \| 43.4 \| 23.9 \| +19.5 \|
	\| PUB-OE \| 30.9 \| 35.3 \| -4.4 \|
	\| OlympiadBench-Physics \| 48.0 \| 39.3 \| +8.7 \|
	\| PhysOlym-A \| 28.2 \| 8.0 \| +20.2 \|

	Scoring: problem-level liberal Sonnet-as-judge (every subpart of a multi-part problem must be correct). The 3-seed mean across {42, 17, 23} is the paper's headline (+18.9 pp on PhysOlym-A).

	## Other seeds (HF safetensors mirrors)

	\| Seed \| HF safetensors mirror \| FSDP archive \|
	\|---\|---\|---\|
	\| 42 \| [`shanyangmie/physics-r1-seed42-v4-step60`](https://huggingface.co/shanyangmie/physics-r1-seed42-v4-step60) \| [`...-seed42-v4-step60-fsdp`](https://huggingface.co/shanyangmie/physics-r1-seed42-v4-step60-fsdp) \|
	\| 17 \| [`shanyangmie/physics-r1-seed17`](https://huggingface.co/shanyangmie/physics-r1-seed17) \| [`...-seed17-canonical-step63-fsdp`](https://huggingface.co/shanyangmie/physics-r1-seed17-canonical-step63-fsdp) \|
	\| 23 \| this card \| [`...-seed23-canonical-step60-fsdp`](https://huggingface.co/shanyangmie/physics-r1-seed23-canonical-step60-fsdp) \|

	## Training recipe

	- Base model: [`Qwen/Qwen3-VL-8B-Thinking`](https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking)
	- Algorithm: GRPO (verl 0.6.1, full-parameter FSDP1 — `actor.strategy=fsdp`, not `fsdp2`)
	- Reward: binary correctness, per-subpart Sonnet judge with problem-level AND aggregation (see paper §3.2)
	- Data: [`shanyangmie/physr1corp`](https://huggingface.co/datasets/shanyangmie/physr1corp) — 2,268 audited closed-form problems
	- Seed / step: 23 / 60
	- Hardware: 4×H200 (FSDP1 4-way sharded)

	Full hyperparameters in paper Appendix.

	## License

	Apache 2.0, inheriting from the base model [`Qwen3-VL-8B-Thinking`](https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking). Training data (`physr1corp`) is CC BY-NC 4.0, so this derivative checkpoint is intended for non-commercial research use.

	## Citation

	```bibtex
	@misc{yang2026physicsr1,
	title = {Physics-R1: An Audited Olympiad Corpus and Recipe for Visual Physics Reasoning},
	author = {Yang, Shan},
	year = {2026},
	url = {https://huggingface.co/papers/2605.14040}
	}
	```