Instructions to use OpenRaiser/Pager with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenRaiser/Pager with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenRaiser/Pager")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("OpenRaiser/Pager")
model = AutoModelForMultimodalLM.from_pretrained("OpenRaiser/Pager")
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 Settings

vLLM

How to use OpenRaiser/Pager with vLLM:

Install from pip and serve model

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

SGLang

How to use OpenRaiser/Pager 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 "OpenRaiser/Pager" \
    --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": "OpenRaiser/Pager",
		"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 "OpenRaiser/Pager" \
        --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": "OpenRaiser/Pager",
		"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 OpenRaiser/Pager with Docker Model Runner:
```
docker model run hf.co/OpenRaiser/Pager
```

Pager / README.md

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metadata

library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - qwen3-vl
  - vision-language
  - multimodal
  - image-text-to-text

Pager

This repository contains the model weights, tokenizer, processor, and configuration files for Pager, a vision-language model based on the Qwen3-VL architecture.

Files

The repository includes:

config.json
generation_config.json
tokenizer.json
tokenizer_config.json
vocab.json
merges.txt
special_tokens_map.json
added_tokens.json
preprocessor_config.json
video_preprocessor_config.json
chat_template.jinja
model.safetensors.index.json
model-00001-of-00004.safetensors
model-00002-of-00004.safetensors
model-00003-of-00004.safetensors
model-00004-of-00004.safetensors

Usage

Install dependencies:

pip install -U transformers accelerate safetensors pillow

Load the model:

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

model_id = "OpenRaiser/Pager"

processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True
)

model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

print("Model loaded successfully.")

If your local transformers version does not support this model class, please upgrade transformers first.

Notes

The model weights are stored in four .safetensors shards.
model.safetensors.index.json maps model parameters to the corresponding weight shards.
This repository is intended for research and development use.

Citation

If you use this model, please cite or link to this repository:

https://huggingface.co/OpenRaiser/Pager