Summary of Outliers and Calibration Sets Have Diminishing Effect on Quantization Of Modern Llms, by Davide Paglieri et al.
Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs
by Davide Paglieri, Saurabh Dash, Tim Rocktäschel, Jack Parker-Holder
First submitted to arxiv on: 31 May 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 research explores the role of calibration sets in Post-Training Quantization (PTQ) for Large Language Models (LLMs). PTQ reduces memory usage and enables faster operation at the cost of small performance drops. The study reveals that calibration sets are crucial for evaluating activation magnitudes and identifying outliers, which can negatively impact performance. The analysis shows a marked contrast in quantization effectiveness across models, with older models like OPT showing significant performance deterioration and high susceptibility to outliers, while newer models like Llama-2 7B, Llama-3 8B, Command-R 35B, and Mistral 7B demonstrate strong robustness. The findings suggest a shift in PTQ strategies might be needed, with a focus on optimizing inference speed rather than primarily focusing on outlier preservation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about making computers learn faster and use less energy. It’s like taking a picture of a person and then showing it to someone else without having to take the whole picture again. The study looks at how this works for big language models, which are programs that can understand and generate human-like text. The researchers found that some older models don’t work well when they’re made smaller, but newer models do. This means we might need to change how we make these models faster and more efficient. |
Keywords
» Artificial intelligence » Inference » Llama » Quantization
