Summary of Channel-wise Mixed-precision Quantization For Large Language Models, by Zihan Chen et al.
Channel-Wise Mixed-Precision Quantization for Large Language Models
by Zihan Chen, Bike Xie, Jundong Li, Cong Shen
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have achieved great success in various language tasks, but their deployment on edge devices remains challenging due to the large parameter sizes. Weight-only quantization is a promising solution to reduce memory requirements. However, existing approaches primarily focus on integer-bit quantization, limiting adaptability and preventing full utilization of storage space. The paper introduces Channel-Wise Mixed-Precision Quantization (CMPQ), a novel method that allocates precision in a channel-wise pattern based on activation distributions. CMPQ adapts to any bit-width constraint by assigning different precision levels to different weight channels. It employs a non-uniform quantization strategy and two outlier extraction techniques to preserve critical information, minimizing the quantization loss. Experiments demonstrate significant performance gains with a modest increase in memory usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are very good at doing lots of language tasks, but they need a lot of space to work on devices like phones or tablets. One way to make them use less space is by using “weight-only quantization”. This makes the model smaller without losing its ability to do things. The problem with this method right now is that it only works for certain types of models and doesn’t use all the space available. In this paper, they came up with a new way to make models use less space called Channel-Wise Mixed-Precision Quantization (CMPQ). CMPQ makes sure that different parts of the model are using the right amount of space to work well. |
Keywords
» Artificial intelligence » Precision » Quantization
