Summary of Tesseraq: Ultra Low-bit Llm Post-training Quantization with Block Reconstruction, by Yuhang Li et al.
TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction
by Yuhang Li, Priyadarshini Panda
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed work aims to optimize the post-training quantization (PTQ) process for large language models (LLMs), achieving ultra-low bit weights while maintaining performance. The authors introduce TesseraQ, a new state-of-the-art PTQ technique that combines block reconstruction and progressive adaptive rounding to stabilize the weight rounding process. This approach enables seamless integration with existing scaling or clipping-based PTQ algorithms like AWQ and OmniQuant. Experimental results demonstrate superior performance for TesseraQ compared to AWQ, achieving 6.82 wikitext2 perplexity and 59.27 average downstream accuracy at 2-bit weight-only quantization of LLaMA-2-7B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TesseraQ is a new way to make large language models smaller and faster. It does this by changing the way it stores the model’s weights, making it possible to use much fewer bits while still keeping the same level of accuracy. This makes TesseraQ useful for tasks like natural language processing and text understanding. |
Keywords
» Artificial intelligence » Llama » Natural language processing » Perplexity » Quantization
