Meta’s Vision-Language Shift VL-JEPA Beats Bulky LLMs

VL-JEPA predicts meaning in embeddings, not words, combining visual inputs with eight Llama 3.2 layers to give faster answers you can trust.
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QSVD: Efficient Low-Rank Approximation for Unified Query-Key-Value Weight Compression in Low-Precision Vision-Language Models

This repository provides the official implementation of QSVD, a method for efficient low-rank approximation that unifies Query-Key-Value (QKV) weight compression in low-precision Vision-Language ...