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---
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
tags:
- chat
---
# mPLUG-DocOwl2
## Introduction
mPLUG-DocOwl2 is a state-of-the-art Multimodal LLM for OCR-free Multi-page Document Understanding.
Through a compressing module named High-resolution DocCompressor, each page is encoded with just 324 tokens.
Github: [mPLUG-DocOwl](https://github.com/X-PLUG/mPLUG-DocOwl)
## Quickstart
```python
import torch
import os
from transformers import AutoTokenizer, AutoModel
from icecream import ic
import time
class DocOwlInfer():
def __init__(self, ckpt_path):
self.tokenizer = AutoTokenizer.from_pretrained(ckpt_path, use_fast=False)
self.model = AutoModel.from_pretrained(ckpt_path, trust_remote_code=True, low_cpu_mem_usage=True, torch_dtype=torch.float16, device_map='auto')
self.model.init_processor(tokenizer=self.tokenizer, basic_image_size=504, crop_anchors='grid_12')
def inference(self, images, query):
messages = [{'role': 'USER', 'content': '<|image|>'*len(images)+query}]
answer = self.model.chat(messages=messages, images=images, tokenizer=self.tokenizer)
return answer
docowl = DocOwlInfer(ckpt_path='mPLUG/DocOwl2')
images = [
'./examples/docowl2_page0.png',
'./examples/docowl2_page1.png',
'./examples/docowl2_page2.png',
'./examples/docowl2_page3.png',
'./examples/docowl2_page4.png',
'./examples/docowl2_page5.png',
]
answer = docowl.inference(images, query='what is this paper about? provide detailed information.')
answer = docowl.inference(images, query='what is the third page about? provide detailed information.')
```
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