Hi Folks! Happy Saturday 🫶🏻 It’s finally time to implement the code for pretraining the LLM ✨ It’s day 20 for the challenge today! Here’s what I am doing in a nutshell - ✨ I implement the full training pipeline for a GPT-style LLM. A simple train_model_simple function is defined with the standard autoregressive optimization loop: reset gradients, compute cross-entropy loss on each batch, backpropagate with loss.backward(), and update weights using the AdamW optimizer. ✨Training runs for multiple epochs while periodically evaluating both training and validation loss through evaluate_model. ✨After each epoch, the model generates sample text to qualitatively monitor improvement. Results show training loss decreasing significantly and outputs becoming more coherent, but validation loss eventually plateaus, indicating overfitting due to the extremely small dataset. #deeplearning #generativeai #womenwhocode #largelanguagemodels #womenintech