Zero-Shot Quantization via Weight-Space Arithmetic

1. 1Extract the quantization vector from a donor task using weight-space arithmetic
2. 2Apply the quantization vector to a receiver model to improve robustness to PTQ-induced noise
3. 3Evaluate the performance of the patched receiver model on a target task
4. 4Fine-tune the receiver model if necessary to further improve performance

AI engineers and researchers can benefit from this method as it provides a way to improve model robustness to quantization-induced noise without requiring additional training data or computational resources. This can be particularly useful in deployment scenarios where data is limited or expensive to obtain

💡 The quantization vector is a transferable direction in weight space that can be used to improve robustness to post-training quantization without requiring receiver-side quantization-aware training

🚀 Improve model robustness to quantization-induced noise by up to 60% with Zero-Shot Quantization via Weight-Space Arithmetic! 🤖