Llama-Mobile: Efficient 2.7-Bit Quantization of VLMs
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Computer Science > Computer Vision and Pattern Recognition
Title:Llama-Mobile: Efficient 2.7-Bit Quantization of VLMs
Abstract:Deploying vision-language models (VLMs) on mobile devices is challenging due to their significant memory and compute requirements. We present a framework for quantizing VLMs for efficient inference on resource-constrained hardware. Our approach combines a quantization pipeline that uses the model itself to generate training data and does not require access to the training setup, with a novel 2.7-bit-per-parameter format supporting efficient execution on Arm CPUs. We validate our approach by compressing the Llama 3.2 11B Vision Instruct model to 3.7 GB with 8-bit activations, preserving strong performance on a set of standard visual question answering tasks.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.21134 [cs.CV] |
| (or arXiv:2608.21134v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.21134
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