Instructions to use shanyangmie/physics-r1-seed17-v4-step60-fsdp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shanyangmie/physics-r1-seed17-v4-step60-fsdp with Transformers:

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

pipe = pipeline("image-text-to-text", model="shanyangmie/physics-r1-seed17-v4-step60-fsdp")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("shanyangmie/physics-r1-seed17-v4-step60-fsdp", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use shanyangmie/physics-r1-seed17-v4-step60-fsdp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shanyangmie/physics-r1-seed17-v4-step60-fsdp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shanyangmie/physics-r1-seed17-v4-step60-fsdp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shanyangmie/physics-r1-seed17-v4-step60-fsdp

SGLang

How to use shanyangmie/physics-r1-seed17-v4-step60-fsdp 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-seed17-v4-step60-fsdp" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shanyangmie/physics-r1-seed17-v4-step60-fsdp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-seed17-v4-step60-fsdp" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shanyangmie/physics-r1-seed17-v4-step60-fsdp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shanyangmie/physics-r1-seed17-v4-step60-fsdp with Docker Model Runner:
```
docker model run hf.co/shanyangmie/physics-r1-seed17-v4-step60-fsdp
```

Physics-R1 — Seed 17, v4 step-60 (FSDP-sharded)

Project Page | Paper | Code | Training corpus

Physics-R1 fine-tune of Qwen3-VL-8B-Thinking on the audited PhysR1Corp (2,268 closed-form physics problems) via full-parameter FSDP1 GRPO with binary correctness reward. This is the seed-17 v4 (audited-data) re-validation checkpoint at step 60.

Released alongside Physics-R1: An Audited Olympiad Corpus and Recipe for Visual Physics Reasoning.

Which checkpoint should you use?

Checkpoint	Use for	Notes
`physics-r1-seed17-canonical-step63-fsdp`	Exact reproduction of paper Table 2 seed-17 row	Canonical paper checkpoint (step 63)
`physics-r1-seed17-v4-step60-fsdp`	Re-validation on audited corpus	This card — v4 audited re-run, tracks canonical closely
`physics-r1-seed17-v4-step50-fsdp`	Step ablation	Same run, earlier step
`physics-r1-seed17-v4-step40-fsdp`	Step ablation	Same run, earlier step
`physics-r1-seed42-v4-step60-fsdp`	Paper Table 2 seed-42 row	Step-60 binary, seed 42
`physics-r1-seed23-canonical-step60-fsdp`	Paper Table 2 seed-23 row	Canonical step-60, seed 23

On the relationship to Table 2: the paper's seed-17 row (PhysReason 43.1, PhysOlym-A 25.0, PhyX-3k 77.2, ...) is from the canonical step-63 checkpoint. This v4 step-60 checkpoint is a re-validation on the audited 2,268-record PhysR1Corp; its step-60 mean tracks the canonical mean within statistical noise. For exact paper-reproduction numbers, use the canonical checkpoint.

Training recipe

Base model: Qwen/Qwen3-VL-8B-Thinking
Algorithm: GRPO (verl 0.6.1, full-parameter FSDP1 — actor.strategy=fsdp, not fsdp2; FSDP2 fails on Qwen3-VL visual encoder device placement)
Reward: binary correctness, per-subpart Sonnet judge with problem-level AND aggregation (see paper §3.2)
Data: shanyangmie/physr1corp — 2,268 audited closed-form problems
Seed / step: 17 / 60
Hardware: 4×H200 (FSDP1 4-way sharded)

Full hyperparameters are in the paper appendix.

Format: verl FSDP-sharded checkpoint (conversion required)

This checkpoint is saved in verl's FSDP-sharded format, not safetensors. It is not directly loadable via AutoModelForImageTextToText.from_pretrained without a merge step.

File layout

actor/
├── huggingface/                              # HF-style config + tokenizer
│   ├── config.json
│   ├── tokenizer.json, merges.txt, vocab.json
│   ├── preprocessor_config.json
│   └── ...
├── model_world_size_4_rank_{0,1,2,3}.pt      # 4-way FSDP weight shards (~8.7 GB each, ~35 GB total)
├── optim_world_size_4_rank_{0,1,2,3}.pt      # optimizer state (~17.5 GB each, not needed for inference)
├── extra_state_world_size_4_rank_{0..3}.pt
└── fsdp_config.json
data.pt                                       # verl bookkeeping (not needed for inference)

Convert to HF safetensors

Use verl's model_merger.py:

git clone https://github.com/volcengine/verl
cd verl

# Download only the inference-required files (skips ~70 GB of optimizer state)
huggingface-cli download shanyangmie/physics-r1-seed17-v4-step60-fsdp \
    --include "actor/model_world_size_4_rank_*.pt" \
    --include "actor/huggingface/*" \
    --include "actor/fsdp_config.json" \
    --include "actor/extra_state_world_size_4_rank_*.pt" \
    --local-dir ./ckpt

# Merge FSDP shards into HF safetensors
python scripts/model_merger.py merge \
    --backend fsdp \
    --hf_model_path Qwen/Qwen3-VL-8B-Thinking \
    --local_dir ./ckpt/actor \
    --target_dir ./physics-r1-seed17-v4-step60-hf

Then load with standard HF:

from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained(
    "./physics-r1-seed17-v4-step60-hf",
    torch_dtype="bfloat16",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("./physics-r1-seed17-v4-step60-hf")

License

Apache 2.0, inheriting from the base model 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

@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}
}

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Paper for shanyangmie/physics-r1-seed17-v4-step60-fsdp

Physics-R1: An Audited Olympiad Corpus and Recipe for Visual Physics Reasoning

Paper • 2605.14040 • Published 11 days ago • 5