To further optimize the online fine-tuning component of FineZip, several strategies can be employed. First, leveraging more advanced parameter-efficient fine-tuning (PEFT) techniques beyond LoRA, such as Adapter layers or BitFit, could significantly reduce the memory footprint. These methods allow for the addition of lightweight modules to the pre-trained model, which can be fine-tuned without modifying the entire model's parameters, thus conserving memory.
Second, implementing mixed-precision training can enhance computational efficiency. By using lower precision (e.g., FP16 instead of FP32), the memory requirements can be halved, allowing for larger batch sizes during the fine-tuning process. This would not only speed up the training but also reduce the overall time taken for compression.
Additionally, optimizing the data loading and preprocessing pipeline can lead to faster fine-tuning. Techniques such as data augmentation or using more efficient data formats can minimize the time spent on preparing the input data for the model. Finally, exploring distributed training across multiple GPUs could further decrease the time required for online fine-tuning, enabling FineZip to handle larger datasets more efficiently.