Methods for Optimizing the Training and Fine-Tuning Large Language Models

The main problematic issues in the development and specialization of LLM are: catastrophic forgetting, the risk of overfitting, hallucinations, incorrect interpretations, incorrect processing of exceptional situations as well as exceptionally high performance requirements for the computing tools used in this case. The purpose of the study is to select and develop methods for optimizing the training and fine-tuning process LLM, providing a significant reduction in the computing resources required for this. To achieve this goal, it is proposed to use the following methods of optimizing LLMs and their learning algorithms: LoRA and QLoRA, Batch size choice, Gradient Accumulation, Gradient Checkpoint, Mixed precision training, FlashAttention-2. To obtain a cumulative positive effect when using these methods together, it is necessary to perform a number of practical experiments. When setting up LLM learning hyperparameters, you should first determine which package size gives the best results, and then choose adequate methods to optimize the computing resources used. The application of the presented methods will increase the efficiency of using computing resources when training and fine-tuning large language models and will reduce the time and financial costs necessary for this.