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

7lalala

Create README.md

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	---
	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:

	```bash
	pip install -U transformers accelerate safetensors pillow
	```

	Load the model:

	```python
	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:

	```text
	https://huggingface.co/OpenRaiser/Pager
	```