The author also discusses the impact of different optimizers on memory usage, the potential benefits of SGD over Adam, and the importance of adjusting the LoRA rank and selecting an appropriate alpha value. The author concludes by noting that 7 billion parameter models can be finetuned efficiently within a few hours on a single GPU with 14 GB of RAM. However, optimizing an LLM to excel across all benchmark tasks with a static dataset is unattainable, suggesting the need for diverse data sources or a different tool than LoRA.
Key takeaways:
- Low-rank adaptation (LoRA) is a widely used technique for efficiently training custom large language models (LLMs). It decomposes the weight changes into a lower-rank representation, saving memory and computational resources.
- LoRA's outcomes remain remarkably consistent across multiple runs despite the inherent randomness of LLM training. It also presents a trade-off that might be worthwhile if you're constrained by GPU memory, offering 33% memory savings at the cost of a 39% increase in runtime.
- When finetuning LLMs, the choice of optimizer shouldn't be a major concern. There's minimal variation in outcomes whether you employ AdamW, SGD with a scheduler, or AdamW with a scheduler.
- For static datasets, iterating multiple times, as done in multi-epoch training, might not be beneficial. It often deteriorates the results, probably due to overfitting. If you're incorporating LoRA, ensure it's applied across all layers, not just to the Key and Value matrices, to maximize model performance.