Summary of Badam: a Memory Efficient Full Parameter Optimization Method For Large Language Models, by Qijun Luo et al.
BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models
by Qijun Luo, Hengxu Yu, Xiao Li
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper presents BAdam, a novel optimization method that combines block coordinate descent (BCD) with Adam’s update rule. The authors propose a memory-efficient approach to fine-tuning large language models, demonstrating its effectiveness in terms of memory usage, running time, and optimization capability. The results show that BAdam outperforms existing memory-efficient baselines, such as LoRA, on MT-bench and math benchmarks. Additionally, the paper highlights the suitability of BCD for finetuning LLMs through an ablation study using SGD’s update rule. The authors provide a code repository that can be easily integrated into any PyTorch-based codebase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make big language models better by fine-tuning them. It’s called BAdam, and it uses two existing methods: block coordinate descent (BCD) and Adam’s update rule. The researchers tested BAdam on large language models and found that it works well in terms of how much memory it uses, how long it takes to run, and how good the results are. They also compared BAdam to other similar methods and found that it does better on some tasks. This is important because big language models can be used for many different applications, like helping people understand each other or creating new AI systems. |
Keywords
* Artificial intelligence * Fine tuning * Lora * Optimization
