Loading the Model

To load, we recreate the model structure from the config, then fill in the learned weights:

export function loadModel(path: string): Model {
  const data = JSON.parse(readFileSync(path, "utf-8"));
  const model = createModel(data.config);
  for (let i = 0; i < model.params.length; i++) {
    model.params[i].data = data.weights[i];
  }
  return model;
}

createModel(data.config) allocates all the weight matrices with random values (just like before training), then we overwrite every parameter with the learned value. The ordering is deterministic (createModel always creates matrices in the same order), so the flat weights array maps back to the correct matrices.

Why This Matters

This separation (train once, generate many times) is how all production LLMs work. Training a large model can cost hundreds of millions of dollars in compute. But once trained, the model weights are saved and can be loaded for inference on any machine. The training code, the optimizer state, the gradient buffers: none of that is needed for inference. Just the architecture and the numbers.

Training (expensive, done once):
  train  ->  phrases-model.json

Inference (cheap, done many times):
  phrases-model.json  ->  generate  ->  sentences

We will wire up these scripts in the smoke test chapter.