Webllm/model.js

export async function loadModel(modelUrl) {
  console.log(`Loading model from: ${modelUrl}`);
  // Dummy placeholder: Simulate loading binary
  const response = await fetch(modelUrl);
  if (!response.ok) throw new Error("Failed to load model");
  const modelData = await response.arrayBuffer();

  // Here you'd normally parse the .bin (GGML/GGUF) file
  return { data: modelData }; // just storing raw bytes for now
}

export async function generateText(model, inputIds) {
  // Placeholder: this is where you'd call real inference (e.g., llama.cpp WASM)
  console.log("Generating with dummy model...", inputIds);
  return inputIds.concat([13, 42]); // Fake token output for demo
}