Summary of Lqer: Low-rank Quantization Error Reconstruction For Llms, by Cheng Zhang et al.
LQER: Low-Rank Quantization Error Reconstruction for LLMs
by Cheng Zhang, Jianyi Cheng, George A. Constantinides, Yiren Zhao
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 The paper introduces a novel approach to post-training quantization of Large Language Models (LLMs) called Low-rank Quantization Error Reduction (LQER). LQER combines quantization and low-rank approximation to recover the model’s capability, achieving nearly-lossless W4A8 quantization on various LLMs and downstream tasks without requiring knowledge distillation, grid search, or gradient-based iterative optimization. This approach leverages an activation-induced scale matrix to drive the singular value distribution of quantization error towards a desirable distribution. LQER eliminates the need for specialized Scatter and Gather processes to collect high-precision weights from irregular memory locations, unlike existing methods. The paper’s W4A8 LLMs achieve near-lossless performance on six popular downstream tasks while using 1.36 times fewer hardware resources than the leading state-of-the-art method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to make language models work better after they’re already trained by reducing errors that happen when the model is used with less powerful computers or devices. It uses something called Low-rank Quantization Error Reduction, which helps the model still perform well even though it’s using less precise numbers. This approach is special because it doesn’t need to use extra methods like teaching the model to be smaller or searching through lots of possibilities. The idea behind LQER is that it can help language models work better on devices with limited resources by reducing errors, and this could be useful for things like voice assistants or language translation apps. The paper also makes its code available online so others can use and build upon the method. |
Keywords
* Artificial intelligence * Grid search * Knowledge distillation * Optimization * Precision * Quantization * Translation
