Monitoring Loss and Perplexity: Reading Signals During LLM Training
Learn how to interpret cross-entropy loss and perplexity during LLM training. Discover practical tips for reading training curves, avoiding common pitfalls, and diagnosing model health effectively.
Stochastic Depth and Regularization in Deep Transformer LLMs: A Practical Guide
Learn how stochastic depth improves deep transformer LLMs by reducing overfitting and boosting efficiency. Discover practical tips for drop schedules and combining it with other regularization techniques.
Residual Connections and Layer Normalization in Large Language Models: Why They Keep Training Stable
Residual connections and layer normalization are essential for training stable, deep large language models. Without them, transformers couldn't scale beyond a few layers. Here's how they work and why they're non-negotiable in modern AI.
Mixed-Precision Training for Large Language Models: FP16, BF16, and Beyond
Mixed-precision training using FP16 and BF16 cuts LLM training time by up to 70% and reduces memory use by half. Learn how it works, why BF16 is now preferred over FP16, and how to implement it safely with PyTorch.
