Nanochat Miniseries v1 (github.com)

The Nanochat Miniseries v1 introduces a refined approach to optimizing large language models (LLMs) by treating model depth as a singular variable for achieving improved performance across a family of models. The release emphasizes a complete end-to-end training pipeline, focusing on the significance of pretraining as a computationally intensive but crucial phase for model intelligence. Using configurations from d10 to d20, these models were efficiently trained on a high-performance setup, demonstrating that careful scaling laws can facilitate substantial cost savings—totaling about $100 for training all models in a series of back-to-back sessions. This development is critical for the AI/ML community as it validates the effectiveness of scaling methods in model training, showing that the relationship between model parameters and training tokens can be optimized for different compute budgets. Notably, the techniques and metrics applied here not only enable clearer comparisons to previous models like GPT-2 and GPT-3 but also establish a foundation for the upcoming Miniseries v2, which aims to enhance pretraining optimization. The use of the CORE metric for model evaluation further solidifies the validity of results, indicating a step forward in setting standards for model performance assessments in the landscape of LLM development.