Instructions to use PRIME-RL/P1-VL-235B-A22B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PRIME-RL/P1-VL-235B-A22B with Transformers:

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

pipe = pipeline("image-text-to-text", model="PRIME-RL/P1-VL-235B-A22B")
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("PRIME-RL/P1-VL-235B-A22B")
model = AutoModelForMultimodalLM.from_pretrained("PRIME-RL/P1-VL-235B-A22B", 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 PRIME-RL/P1-VL-235B-A22B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PRIME-RL/P1-VL-235B-A22B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PRIME-RL/P1-VL-235B-A22B",
		"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/PRIME-RL/P1-VL-235B-A22B

SGLang

How to use PRIME-RL/P1-VL-235B-A22B 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 "PRIME-RL/P1-VL-235B-A22B" \
    --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": "PRIME-RL/P1-VL-235B-A22B",
		"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 "PRIME-RL/P1-VL-235B-A22B" \
        --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": "PRIME-RL/P1-VL-235B-A22B",
		"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 PRIME-RL/P1-VL-235B-A22B with Docker Model Runner:
```
docker model run hf.co/PRIME-RL/P1-VL-235B-A22B
```

Add metadata and links to paper and code

by nielsr HF Staff - opened Feb 12

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+11

-5

Files changed (1) hide show

README.md +11 -5

README.md CHANGED Viewed

@@ -1,5 +1,7 @@
 ---
 license: apache-2.0
 ---
 <div align="center">
@@ -7,7 +9,9 @@ license: apache-2.0
 </div>
 <p align="center">
-  <a href="https://prime-rl.github.io/P1-VL/"><b>🌐 P1-VL Project Page</b></a> |
   <a href="https://phyarena.github.io/"><b>🏆 HiPhO Leaderboard</b></a>
 </p>
@@ -22,7 +26,9 @@ license: apache-2.0
 ## Model Description
-**P1-VL-235B-A22B** is the flagship variant of the P1-VL series, a high-performance open-source vision-language model specialized in physics reasoning. Built on *Qwen3-VL-235B-A22B-Thinking* and refined through multi-stage reinforcement learning on curated physics competition data, P1-VL-235B-A22B becomes the first open-source Vision-Language Model (VLM) to secure 12 gold medals on HiPhO, ranking No.3 in the model leaderboard. The model effectively solves tasks that require precise diagram-to-logic alignment, demonstrating exceptional performance in physics Olympiad competitions.
 ### Key Highlights
@@ -162,7 +168,7 @@ We are grateful to the open-source community for their invaluable contributions.
 @misc{p1vl2025,
   title={P1-VL: Bridging Visual Perception and Scientific Reasoning in Physics Olympiads},
   author={P1 Team},
-  year={2025},
-  url={https://prime-rl.github.io/P1-VL/}
 }
-```

 ---
 license: apache-2.0
+library_name: transformers
+pipeline_tag: image-text-to-text
 ---
 <div align="center">
 </div>
 <p align="center">
+  <a href="https://huggingface.co/papers/2602.09443"><b>📜 Paper</b></a> |
+  <a href="https://github.com/PRIME-RL/P1-VL"><b>💻 Code</b></a> |
+  <a href="https://prime-rl.github.io/P1-VL/"><b>🌐 Project Page</b></a> |
   <a href="https://phyarena.github.io/"><b>🏆 HiPhO Leaderboard</b></a>
 </p>
 ## Model Description
+**P1-VL-235B-A22B** is the flagship variant of the P1-VL series, a high-performance open-source vision-language model specialized in physics reasoning. It was introduced in [P1-VL: Bridging Visual Perception and Scientific Reasoning in Physics Olympiads](https://huggingface.co/papers/2602.09443).
+Built on *Qwen3-VL-235B-A22B-Thinking* and refined through multi-stage reinforcement learning on curated physics competition data, P1-VL-235B-A22B becomes the first open-source Vision-Language Model (VLM) to secure 12 gold medals on HiPhO, ranking No.3 in the model leaderboard. The model effectively solves tasks that require precise diagram-to-logic alignment, demonstrating exceptional performance in physics Olympiad competitions.
 ### Key Highlights
 @misc{p1vl2025,
   title={P1-VL: Bridging Visual Perception and Scientific Reasoning in Physics Olympiads},
   author={P1 Team},
+  year={2026},
+  url={https://arxiv.org/abs/2602.09443}
 }
+```