Summary of Enabling Efficient On-device Fine-tuning Of Llms Using Only Inference Engines, by Lei Gao et al.
Enabling Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines
by Lei Gao, Amir Ziashahabi, Yue Niu, Salman Avestimehr, Murali Annavaram
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 focuses on Large Language Models (LLMs) fine-tuning on edge devices to improve user trust. The authors identify significant challenges due to resource constraints and propose a memory- and computation-efficient method for LLM fine-tuning. They introduce parallelized randomized gradient estimation (P-RGE) and integrate it with parameter-efficient fine-tuning methods like LoRA. This approach achieves substantial runtime speedups, memory savings, and improved fine-tuning accuracy while fully supporting ExecuTorch’s inference engine. The proposed P-RGE LoRA-FA module requires only server-side code changes, making it practical for real-time on-device applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a new way to make language models work better on devices like smartphones and smart home devices. Right now, these models are trained on big computers and then fine-tuned for specific tasks. But this process can be slow and uses too much memory and power. The authors developed a faster and more efficient method that can do the same job using less resources. This new approach is called P-RGE LoRA-FA and it can work with existing technology to make language models work better in real-time. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Lora » Parameter efficient
