|
--- |
|
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.') |
|
|
|
|
|
``` |
|
|
