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
```

shanyangmie commited on 3 days ago

Commit

5a0423b

verified ·

1 Parent(s): 8e8692e

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

Browse files

Files changed (1) hide show

README.md +93 -0

README.md ADDED Viewed

	@@ -0,0 +1,93 @@

+---
+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}
+}
+```