claude2 tldr:
Here are a few key points I gathered from the article:
The article explores optimizing LoRA hyperparameter settings for finetuning large language models.
The goal is to maximize performance while minimizing memory usage and training time.
The base model used is Llama-2 7B. Experiments compare default LoRA, QLoRA (4-bit quantized), AdamW, SGD optimizers, and different choices for rank r and alpha hyperparameters.
Key findings:
QLoRA provides substantial memory savings (6GB less than default LoRA) at the cost of slower training. Performance impact is minor.
AdamW vs SGD makes little difference in memory or performance.
Increasing training iterations from 50k to 100k hurts performance, likely because the Alpaca dataset lacks diversity.
Tuning rank r and alpha is most impactful. Good rule of thumb is to set alpha=2*r.
Best model uses r=256, alpha=512. Improves over base model on most tasks, except arithmetic.
The optimized LoRA model was submitted to the NeurIPS efficiency challenge and showed improvements on several benchmarks compared to the base Llama-2 model.
Takeaways are practical tips for tuning LoRA hyperparameters and trading off memory, compute, and model performance.
The article explores optimizing LoRA hyperparameter settings for finetuning large language models. The goal is to maximize performance while minimizing memory usage and training time.
The base model used is Llama-2 7B. Experiments compare default LoRA, QLoRA (4-bit quantized), AdamW, SGD optimizers, and different choices for rank r and alpha hyperparameters.
Key findings:
QLoRA provides substantial memory savings (6GB less than default LoRA) at the cost of slower training. Performance impact is minor.
AdamW vs SGD makes little difference in memory or performance.
Increasing training iterations from 50k to 100k hurts performance, likely because the Alpaca dataset lacks diversity.
Tuning rank r and alpha is most impactful. Good rule of thumb is to set alpha=2*r. Best model uses r=256, alpha=512. Improves over base model on most tasks, except arithmetic.
The optimized LoRA model was submitted to the NeurIPS efficiency challenge and showed improvements on several benchmarks compared to the base Llama-2 model.
Takeaways are practical tips for tuning LoRA hyperparameters and trading off memory, compute, and model performance.