Instructions to use SetonLiang2/DocPilot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SetonLiang2/DocPilot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="SetonLiang2/DocPilot")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SetonLiang2/DocPilot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SetonLiang2/DocPilot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SetonLiang2/DocPilot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SetonLiang2/DocPilot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SetonLiang2/DocPilot
- SGLang
How to use SetonLiang2/DocPilot 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 "SetonLiang2/DocPilot" \ --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": "SetonLiang2/DocPilot", "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 "SetonLiang2/DocPilot" \ --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": "SetonLiang2/DocPilot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SetonLiang2/DocPilot with Docker Model Runner:
docker model run hf.co/SetonLiang2/DocPilot
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license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- multimodal
- document-understanding
---
# DocPilot-4B
DocPilot-4B is a 4B action-tag model for long visual document understanding.
Weights are in the `DocPilot-4B` folder.
- Paper: https://arxiv.org/abs/2610.21327
- Code: https://github.com/HKUSTDial/DocPilot
## Requirements
```bash
pip install vllm transformers
```
## Quickstart
```python
from transformers import AutoProcessor, AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained(
"SetonLiang2/DocPilot",
subfolder="DocPilot-4B",
torch_dtype="auto",
device_map="auto",
)
processor = AutoProcessor.from_pretrained(
"SetonLiang2/DocPilot",
subfolder="DocPilot-4B",
)
```
## Deployment
vLLM reads a local folder directly. Download the subfolder, then serve it:
```bash
huggingface-cli download SetonLiang2/DocPilot --include "DocPilot-4B/*" --local-dir ./DocPilot
vllm serve ./DocPilot/DocPilot-4B --host 0.0.0.0 --port 8088 --dtype bfloat16
```
Point DocPilot at this server with `protocol: vllm_tags`:
```yaml
llm:
provider: openai
protocol: vllm_tags
model: ./DocPilot/DocPilot-4B
base_url: http://127.0.0.1:8088/v1
api_key: EMPTY
```
## Citation
```bibtex
@article{liang2026docpilot,
title={DocPilot: Interactive Agent Orchestration and Training for Long Visual Document Understanding},
author={Liang, Zhuowen and Lin, Xiaotian and Luo, Yuyu and Wang, Haixun and Tang, Nan},
journal={arXiv preprint arXiv:2610.21327},
year={2026}
}
```
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