an-yongqi/systematic-outliers
[ICLR 2025] Systematic Outliers in Large Language Models.
This project helps AI researchers and machine learning engineers analyze the behavior of Large Language Models (LLMs). It takes existing LLM architectures and training data, and outputs visualizations and analyses of 'systematic outliers' within the model's weights, activations, and attention mechanisms. The goal is to understand how these outliers impact performance and efficiency, ultimately leading to better model design.
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Use this if you are an AI researcher or machine learning engineer focused on understanding, debugging, and optimizing Large Language Models by examining their internal outlier phenomena.
Not ideal if you are looking for an off-the-shelf solution for general LLM fine-tuning or deployment without needing deep architectural analysis.
Stars
9
Forks
2
Language
Python
License
MIT
Category
Last pushed
Feb 11, 2025
Commits (30d)
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