Instructions to use TIGER-Lab/PixelReasoner-RL-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/PixelReasoner-RL-v1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="TIGER-Lab/PixelReasoner-RL-v1")
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("TIGER-Lab/PixelReasoner-RL-v1")
model = AutoModelForMultimodalLM.from_pretrained("TIGER-Lab/PixelReasoner-RL-v1", 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 TIGER-Lab/PixelReasoner-RL-v1 with vLLM:

Install from pip and serve model

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

SGLang

How to use TIGER-Lab/PixelReasoner-RL-v1 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 "TIGER-Lab/PixelReasoner-RL-v1" \
    --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": "TIGER-Lab/PixelReasoner-RL-v1",
		"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 "TIGER-Lab/PixelReasoner-RL-v1" \
        --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": "TIGER-Lab/PixelReasoner-RL-v1",
		"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 TIGER-Lab/PixelReasoner-RL-v1 with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/PixelReasoner-RL-v1
```

PixelReasoner-RL-v1

Commit History

Nov 6 7 days ago

10:28
Add LongShOTBench evaluation results⁠

jazeelmohd

Verified signature
48a2e11

Jun 11, 2025

13:26
Add link to Github repo, link to project page (#2)⁠

wenhu +1: nielsr

Verified signature
1736018

May 26, 2025

12:21
Updating `library_name` and `pipeline_tag` in Model Card (#1)⁠

wenhu +1: sergiopaniego

Verified signature
8f18d06

May 24, 2025

16:02
Update README.md

JasperHaozhe

Verified signature
477cc28
15:36
Add files using upload-large-folder tool

JasperHaozhe

Verified signature
ea66ff2

May 23, 2025

15:51
Update README.md

wenhu

Verified signature
a7066a3
03:22
Create README.md

wenhu

Verified signature
51aa9df

May 18, 2025

16:30
Add files using upload-large-folder tool

JasperHaozhe

Verified signature
b1401be
16:13
initial commit

JasperHaozhe

Verified signature
89c4706

Commit History

Nov 6 7 days ago

Add LongShOTBench evaluation results⁠

Jun 11, 2025

Add link to Github repo, link to project page (#2)⁠

May 26, 2025

Updating `library_name` and `pipeline_tag` in Model Card (#1)⁠

May 24, 2025

Update README.md

Add files using upload-large-folder tool

May 23, 2025

Update README.md

Create README.md

May 18, 2025

Add files using upload-large-folder tool

initial commit