--- license: mit --- ## INF-MLLM2: High-Resolution Image and Document Understanding In INF-MLLM2, we have introduced significant updates, particularly in high-resolution image processing, document understanding and OCR. The key improvements include the following: - Dynamic Image Resolution Support: The model now supports dynamic image resolution up to 1344x1344 pixels. - Enhanced OCR Capabilities: The model has significantly improved OCR capabilities, enabling robust document parsing, table and formula recognition, document layout analysis, and key information extraction. - Advanced Training Strategies: We employed a progressive multi-stage training strategy along with an enhanced data mixup strategy tailored for image and document multitask scenarios.

[Technical Report](docs/tech_report.pdf) ### Install ```bash conda create -n infmllm2 python=3.9 conda activate infmllm2 conda install pytorch==2.2.1 torchvision==0.17.1 torchaudio==2.1.2 pip install transformers==4.40.2 timm==0.5.4 pillow==10.4.0 sentencepiece==0.1.99 pip install bigmodelvis peft einops spacy ``` ### Model Zoo We have released the INF-MLLM2-7B model on Hugging Face. - [INF-MLLM2-7B](https://huggingface.co/QianYEee/InfMLLM2_7B_chat) ### Evaluation The comparison with general multimodal LLM across multiple benchmarks and OCR-related tasks.

The comparison with OCR-free multimodal LLM for content parsing of documents/tables/formulas.

The comparison with OCR-free multimodal LLM for key information extraction.

### Visualization

### Usage The inference process for INF-MLLM2 is straightforward. We also provide a simple [demo.py](demo.py) script as a reference. ```bash CUDA_VISIBLE_DEVICES=0 python demo.py --model_path /path/to/InfMLLM2_7B_chat ``` ## Acknowledgement We thank the great work from [LLaVA-Next](https://github.com/LLaVA-VL/LLaVA-NeXT.git) and [InternLM-XComposer](https://github.com/InternLM/InternLM-XComposer.git).