Train an LLM from Scratch with Karpathy's Nanochat

Learn to train a large language model from scratch using Andrej Karpathy's Nanochat framework with a budget of just $100. Follow along with a complete walkthrough that demonstrates setting up a one-click Runpod template for cloud-based training, executing the speedrun script with Weights & Biases logging, and implementing Byte Pair Encoding (BPE) tokenizer training. Master the pretraining process on 10 billion tokens from the fine-web educational dataset, conduct pre-training evaluation, and perform mid-training on the Smoltalk+ dataset. Discover how to manage model checkpoints by pushing and pulling from HuggingFace Hub, set up and run the chat interface UI, and execute post-training on benchmark training and auxiliary splits. Experience a live demonstration of the chat interface functionality and learn the complete workflow for pushing the final model, optimizer, report, and tokenizer to the hub, with access to all necessary GitHub repositories, model files, and training logs.