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This pipeline allows the models to process high-quality images efficiently—splitting larger images into patches, extracting key features without sacrificing detail, and maintaining high performance.
Adaptability & Customization
Another key advantage of LFM2VL is its flexibility. Users can set parameters like token count and image patch size based on their hardware or latency needs. This means that on a less powerful device, users can prioritize speed, while more capable systems can improve accuracy and detail—making the models highly adaptable.
Training & Data: A Thoughtful Approach
Liquid AI's training methodology emphasizes gradual integration of vision and language understanding:
- Starting with a pre-trained backbone.