Pipeline

From raw text to a trained model

A structured training loop connects data loading, the forward pass, loss computation, and optimization into a repeatable process.

1. Stage 01

   Raw text

   The starting corpus used to train and evaluate the model.

2. Stage 02

   Tokenizer

   Converts text into vocabulary IDs the model can consume.

3. Stage 03

   Batching

   Groups sequences into batches for efficient training steps.

4. Stage 04

   Forward pass

   Runs a batch through the Transformer to produce next-token predictions.

5. Stage 05

   Loss computation

   Compares predictions against the real next tokens.

6. Stage 06

   Backpropagation & optimizer step

   Updates model weights to reduce the loss.

7. Stage 07

   Checkpointing

   Saves model state so training can resume or be evaluated later.