● Covered by 1 source · 1 reportMedium impact1 neutral
Apple Introduces LensVLM, a 9B Vision Language Model for Selective Text Expansion
Key points
- LensVLM is a 9B Vision Language Model.
- It scans compressed text images.
- Selectively expands relevant pages via learned tools.
- Code and model are available under Apple licenses.
Introduction of LensVLM
Apple has introduced LensVLM, a 9B Vision Language Model (VLM) designed to process long textual contexts more efficiently. The model operates by initially scanning compressed images of text documents.
Selective Context Expansion
A core feature of LensVLM is its ability to selectively expand only the relevant pages from their compressed visual representation. This expansion is performed using learned tools, allowing the model to focus computational resources on pertinent information rather than processing an entire uncompressed document.
Availability and Licensing
The LensVLM model files, including Apple's modifications to the Qwen model, are provided under the Apple Machine Learning Research Model License. The accompanying source code is distributed separately under the Apple Sample Code License. Both are available on GitHub.
Technical Details and Usage
Users can install LensVLM and run inference via a provided script, specifying the model and compression options (5x, 10x, 15x). The repository includes instructions for data preparation and evaluation. The model's paper, "LensVLM: Selective Context Expansion for Compressed Visual Representation of Text," is available on arXiv.
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Reporting from
Apple has released LensVLM, a 9B Vision Language Model (VLM) that processes compressed text images and expands only relevant pages. This model uses learned tools to selectively uncompress pertinent sections, offering a new approach to handling long document contexts.