Summary of Quip#: Even Better Llm Quantization with Hadamard Incoherence and Lattice Codebooks, by Albert Tseng et al.
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
by Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper introduces QuIP#, a novel post-training quantization (PTQ) method that achieves state-of-the-art results in extreme compression regimes, using three novel techniques. Specifically, QuIP# improves the incoherence processing of QuIP by leveraging the randomized Hadamard transform, which is faster and has better theoretical properties. Additionally, QuIP# employs vector quantization to take advantage of the ball-shaped sub-Gaussian distribution that incoherent weights possess, utilizing a set of hardware-efficient codebooks based on the highly symmetric E8 lattice. Furthermore, QuIP# uses fine-tuning to improve fidelity to the original model. The authors’ experiments demonstrate that QuIP# outperforms existing PTQ methods, enables new behaviors in PTQ scaling, and supports fast inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary QuIP# is a way to make language models smaller by making them use less memory. Normally, this would mean they wouldn’t work as well, but QuIP# is able to do it without losing too much accuracy. It does this by using three special techniques: an improved way of mixing up the model’s weights, a new method for storing these weights that takes advantage of how they’re distributed, and a fine-tuning process to make sure everything still works together well. |
Keywords
* Artificial intelligence * Fine tuning * Inference * Quantization
