Summary of Blockllm: Memory-efficient Adaptation Of Llms by Selecting and Optimizing the Right Coordinate Blocks, By Amrutha Varshini Ramesh et al.
BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks
by Amrutha Varshini Ramesh, Vignesh Ganapathiraman, Issam H. Laradji, Mark Schmidt
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces BlockLLM, an approach that enables training large language models (LLMs) for pretraining or adapting to new tasks and domains using limited GPU memory. The authors highlight the challenges of existing methods, such as LoRA and GaLore, which alter the training dynamics or are limited by their applicability. BlockLLM carefully selects and updates a small subset of trainable parameters without changing the architecture or training procedure, achieving state-of-the-art performance in finetuning and pretraining tasks while reducing memory footprint. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps train large language models for new tasks using less computer memory. The authors solve a problem that makes it hard to train these models because they need too much memory. They create a new method called BlockLLM, which picks the most important parts of the model and updates them without changing how the model works. This helps achieve good results while using less memory. |
Keywords
» Artificial intelligence » Lora » Pretraining
