Summary of A Study Of Optimizations For Fine-tuning Large Language Models, by Arjun Singh et al.
A Study of Optimizations for Fine-tuning Large Language Models
by Arjun Singh, Nikhil Pandey, Anup Shirgaonkar, Pavan Manoj, Vijay Aski
First submitted to arxiv on: 4 Jun 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 This paper delves into the challenges of fine-tuning large language models for specific applications, highlighting the complexities involved in balancing factors like resource budget, runtime, model size, and context length. The authors present a comprehensive study on various fine-tuning optimizations, including Gradient Checkpointing, Low-Rank Adaptation, DeepSpeed’s Zero Redundancy Optimizer, and FlashAttention. They evaluate these techniques based on memory and runtime usage during the fine-tuning phase, providing recommendations for balancing memory and runtime across diverse model sizes. The paper also explores strategies for fine-tuning very large models with tens or hundreds of billions of parameters and enabling large context lengths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fine-tuning large language models can be tricky because it requires considering several factors. A team of researchers looked into different ways to make this process more efficient. They tested four techniques: Gradient Checkpointing, Low-Rank Adaptation, DeepSpeed’s Zero Redundancy Optimizer, and FlashAttention. The goal was to find the best combination that balances memory usage and runtime during fine-tuning. The results show which technique is best for different-sized models and how to handle large models with many parameters. |
Keywords
» Artificial intelligence » Context length » Fine tuning » Low rank adaptation
