Summary of Fine-tuning and Deploying Large Language Models Over Edges: Issues and Approaches, by Yanjie Dong et al.
Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches
by Yanjie Dong, Haijun Zhang, Chengming Li, Song Guo, Victor C. M. Leung, Xiping Hu
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 A comprehensive overview of memory-efficient fine-tuning methods for large language models (LLMs) is presented, focusing on deploying these versatile foundation models over the network edge. Traditional fine-tuning techniques require significant GPU memory, exceeding mainstream hardware capabilities. To address this issue, model compression techniques can reduce energy consumption and operational costs, supporting sustainable artificial intelligence advancements. The paper reviews state-of-the-art literatures on model compression and provides a vision for efficient deployment of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) have become versatile foundation models since GPT2-1.5B was invented in 2019. These models are great at zero-shot tasks, but they need fine-tuning to work well on local datasets and require a lot of resources to use. This is a problem because traditional ways of fine-tuning these models use up too much memory on our computers and devices. To fix this, we can use techniques that compress the model, which would help reduce energy consumption, costs, and waste, making artificial intelligence more sustainable. |
Keywords
» Artificial intelligence » Fine tuning » Model compression » Zero shot
