Writing an LLM from scratch, part 32a – Interventions: training a baseline model (www.gilesthomas.com)

In his latest installment on training large language models (LLMs) from scratch, Sebastian Raschka discusses developing a baseline model using personal hardware and cloud resources. Initially, he trained a model on his RTX 3090 GPU, but it fell short compared to GPT-2 small in both performance and loss metrics. To enhance his model's quality, Raschka aims to experiment with various interventions, such as modifying dropout rates, adjusting learning rates, and implementing gradient clipping to mitigate issues like exploding gradients observed in prior training runs. This endeavor is significant for the AI/ML community as it uncovers practical strategies for optimizing LLM training and emphasizes the need for a reliable baseline to evaluate different interventions. By introducing a consistent random seed and eliminating validation runs during training, he aims to achieve reproducible results. The baseline training resulted in a loss of 3.692, slightly worse than an earlier cloud-trained model but still an important benchmark for upcoming experiments. Raschka's approach not only illustrates the iterative nature of model training but also provides insights that can benefit developers looking to build or improve their own LLMs.