Activation-Aware Weight Quantization for LLM Compression Outperforms GPTQ (arxiv.org) 8 points by valine 3y ago ↗ HN
[–] valine 3y ago ↗ Better quantization will have a direct and meaningful impact for everyone running local LLMs. The technique has already been applied to both Vicuna and the multimodal LLaMA variant LLaVA.https://github.com/mit-han-lab/llm-awq
[–] convexstrictly 3y ago ↗ They claim average 1.45x speedup and maximum 1.7x speedup over GPTQ.High-level idea: About 1% of the weights contribute greatly to quantization error. So skip the quantization of these weights.
2 comments
[ 3.3 ms ] story [ 17.5 ms ] threadhttps://github.com/mit-han-lab/llm-awq
High-level idea: About 1% of the weights contribute greatly to quantization error. So skip the quantization of these weights.