apple/LensVLM-9B — new model trending #30 on Hugging Face

# LensVLM-9B

LensVLM is a 9B Vision Language Model (VLM) that scans compressed images of text,

then selectively expands only the relevant pages to their uncompressed form via

learned tools.

- Paper: [LensVLM: Selective Context Expansion for Compressed Visual Representation of Text](https://arxiv.org/abs/2605.07019)

- Code: https://github.com/apple-aiml-research/ml-lensvlm

## License

All ML model files in this repository, including Apple's modifications to the Qwen

model, are provided under the terms of the

[Apple Machine Learning Research Model License](https://huggingface.co/apple/LensVLM-9B/blob/main/LICENSE).

The source code that accompanies this model is distributed separately and is provided

under the terms of the Apple Sample Code License.

## Usage

Install the LensVLM code and run inference:

```bash

git clone https://github.com/apple-aiml-research/ml-lensvlm

cd ml-lensvlm

pip install -r requirements.txt

python scripts/run_demo.py --model apple/LensVLM-9B

```

For a custom document:

```bash

python demo.py \

--model apple/LensVLM-9B \

--text_file document.txt \

--question "What is the main finding?" \

--compression 10x

```

Compression options: `5x`, `10x`, `15x`. See the

[repository README](https://github.com/apple-aiml-research/ml-lensvlm) for data preparation

and evaluation.

## Citation

```bibtex

@article{xie2026lensvlm,

title={LensVLM: Selective Context Expansion for Compressed Visual Representation of Text},

author={Xie, Roy and Friedman, Dan and Yu, Donghan and Pan, Bowen and Fifty, Christopher and Kim, Jang-Hyun and Du, Xianzhi and Gan, Zhe and Rathod, Vivek and Dhingra, Bhuwan},

journal={arXiv preprint arXiv:2605.07019},

year={2026}

}

```

Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/apple/LensVLM-9B