Summary of An Empirical Study Of Llama3 Quantization: From Llms to Mllms, by Wei Huang et al.
An empirical study of LLaMA3 quantization: from LLMs to MLLMs
by Wei Huang, Xingyu Zheng, Xudong Ma, Haotong Qin, Chengtao Lv, Hong Chen, Jie Luo, Xiaojuan Qi, Xianglong Liu, Michele Magno
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 LLaMA3 language model, with its impressive performance in various domains, is explored for its capabilities when quantized to low bit-width. This research evaluates the 10 existing post-training quantization and LoRA fine-tuning methods of LLaMA3 on 1-8 bits and various datasets. The results show that even with quantization, LLaMA3 still suffers from non-negligible degradation in linguistic and visual contexts, particularly under ultra-low bit widths. This highlights the significant performance gap at low bit-width that needs to be addressed in future developments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLaMA3 is a powerful open-source language model used in computer vision and natural language understanding tasks. Researchers explored its capabilities when quantized to low bit-width to find new insights and challenges for the low-bit quantization of LLaMA3 and other future models. They evaluated 10 existing post-training quantization and LoRA fine-tuning methods on 1-8 bits and various datasets. The results show that even with quantization, LLaMA3 still suffers from degradation in linguistic and visual contexts. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Language understanding » Lora » Quantization
