ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter10/book-translation/sample_book/chapter4.md

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+# 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.
+
+```python
+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.