Summary of Dlora: Distributed Parameter-efficient Fine-tuning Solution For Large Language Model, by Chao Gao and Sai Qian Zhang
DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model
by Chao Gao, Sai Qian Zhang
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 proposes a distributed parameter-efficient fine-tuning (PEFT) framework called DLoRA to enhance large language model (LLM) performance on downstream tasks. The framework enables scalable PEFT operations between cloud servers and user devices, reducing computation and communication workload while achieving superior accuracy and privacy protection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to improve how big language models work better with the data they’re trained on. They call this “fine-tuning” and it’s important because sharing sensitive information online can be risky. To solve this problem, they came up with a new system called DLoRA that lets computers in the cloud work together with devices people use to make sure everything runs smoothly, is accurate, and keeps private data safe. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Parameter efficient
