Struggling with Suboptimal Model Training for Years?
Are you tired of facing issues with training your machine learning models effectively? If you’ve been dealing with suboptimal model training for years, it’s time to explore new solutions to enhance your workflow.
When training large language models (LLMs) locally, the use of large batch sizes can be hindered by substantial GPU memory consumption. To tackle this challenge, the technique of gradient accumulation has gained popularity. By summing gradients over smaller mini-batches and updating the model weights after a predetermined number of batches, gradient accumulation simulates training with larger batch sizes without the memory overhead.
However, I discovered that while gradient accumulation seems like an effective workaround, it often leads to degraded performance compared to training with larger actual batch sizes, especially with frameworks like Transformers.
After discussing this issue on platforms like X and Reddit, Daniel Han from Unsloth AI also encountered similar problems, affecting not only gradient accumulation but also multi-GPU setups. It’s crucial to address these challenges to optimize your model training process effectively.