Summary of Portllm: Personalizing Evolving Large Language Models with Training-free and Portable Model Patches, by Rana Muhammad Shahroz Khan et al.
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches
by Rana Muhammad Shahroz Khan, Pingzhi Li, Sukwon Yun, Zhenyu Wang, Shahriar Nirjon, Chau-Wai Wong, Tianlong Chen
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper presents PortLLM, a training-free framework for fine-tuning large language models (LLMs) without requiring access to the original pre-trained models or extensive computational resources. The authors propose an initial lightweight model update patch that captures domain-specific knowledge and allows for seamless plugging-in of continued personalization with minimal costs. They demonstrate the effectiveness of PortLLM on seven representative datasets, achieving comparable performance to LoRA fine-tuning with reduced GPU memory usage by up to 12.2x. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PortLLM is a new way to make large language models work better for specific tasks without needing lots of computer power or access to the original model. The idea is to create a small update patch that can be added to the model, making it easier to adapt to new tasks. This makes it more efficient and cost-effective than traditional fine-tuning methods. The authors tested PortLLM on many different datasets and showed that it works well. |
Keywords
» Artificial intelligence » Fine tuning » Lora
