You copy the notebook, change the dataset path, hit run, and hours later a folder appears with new weights that understand your accent. What happened in between? This episode walks the full training step — safetensors files, batches, forward passes, the loss, the backward pass, and Adam's quiet bookkeeping — and explains why a 7B full fine-tune is a memory problem before it's a compute problem. Along the way: why the first five minutes of a fine-tune deliver the cheap gains, how overfitting turns a model into a parrot that can recite training text, and the tricks (mixed precision, gradient checkpointing, gradient accumulation) that let one card do a job that wants a hundred gigabytes.
Episode 6191 — myweirdprompts.com/n/6191 (permanent short link: myweirdprompts.com/056342)
