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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 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. 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: 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: 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 for data preparation and evaluation.
Citation @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} }