Chapter 4: Fine-tuning and Deployment

A general model rarely fits a specific product out of the box. The usual fix is fine-tuning: continuing to train the model on a smaller, task-specific dataset so it adapts to your domain while keeping its general ability.

Fine-tuning changes how the model turns a prompt into an answer, but it does not change the basic pipeline: text becomes a token, each token becomes an embedding, and inference produces the result. What changes is the weights the model learned.

def fine_tune(model, dataset):
    for prompt, target in dataset:
        loss = model.loss(prompt, target)   # compare output to target
        model.update(loss)                  # adjust weights
    return model

After fine-tuning comes deployment: packaging the model behind an API so real users can send a prompt and get an answer. Here the earlier concerns return with full force. Latency must stay low, throughput must stay high, and the KV cache and batching from the previous chapter do the heavy lifting.

The full journey — token, embedding, prompt, inference, latency, fine-tuning, and deployment — is now complete. A model that was once a research artifact has become a service that people can actually use.