# X-PLUG/mPLUG-DocOwl

mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding

Repository: https://github.com/X-PLUG/mPLUG-DocOwl
Canonical: https://ross.abutalabs.com/products/mplug-docowl
Language: Python
License: Apache-2.0
License Family: permissive
Topics: chart-understanding, document-understanding, mllm, multimodal, multimodal-large-language-models, table-understanding
Last push: 2025-05-30T05:52:43+00:00

## Health v2 (maintenance only)
Score: 39/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 24, release rhythm 35, longevity 82
- inputs: {"age_days": 1157, "days_push": 460, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2411, forks 154 (observed 2026-08-28T04:06:44.426745+00:00)

## What it is
mPLUG-DocOwl is a family of multimodal large language models from Alibaba for OCR-free document understanding, including DocOwl1.5 and DocOwl2 for parsing documents, tables, and charts. It provides models, training data, inference, and fine-tuning code released on HuggingFace and ModelScope.

## Use cases
- answer questions about scanned documents without OCR
- extract information from tables in document images
- understand and query charts with a vision-language model
- build a document VQA chatbot
- fine-tune a multimodal LLM on my own document data
- parse multi-page PDFs into structured answers

## When to choose
- you need document, chart, or table understanding from images without a separate OCR pipeline
- you want open weights plus training data and fine-tuning code
- you need a research-grade multimodal LLM with strong DocVQA/ChartQA benchmarks

## When to avoid
- you need lightweight CPU-only text extraction rather than GPU-based deep learning
- you need production OCR with precise bounding boxes
- you cannot host 3B-8B parameter models

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, ocr, nlp, image-processing
- domain: artificial-intelligence, large-language-models, computer-vision, pdf
- platform: python
- tags: multimodal-llm, document-understanding, ocr-free, chart-understanding, table-understanding, visual-question-answering, huggingface, natural-language-processing, gpu, linux

## Member repositories
- X-PLUG/mPLUG-DocOwl (main) score 39

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:44.426745+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:33:57.796872+00:00, confidence not recorded.
  - readme: https://github.com/X-PLUG/mPLUG-DocOwl (fetched 2026-08-28T04:06:44.426745+00:00, sha f31260a9175d)
- Data as of 2026-08-30T08:39:29.467469+00:00.