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Let's allow developers to register a new LLM in a web browser as a web extension, which then would be able to be chosen in #8. The model would be in a TFLite FlatBuffers format, so that it was compatible with MediaPipe LLM Inference as a possible fallback for unsupported browsers (compatible with Gemini Nano).
The method to register/add a custom model could be invoked by web extension like this:
ai.registerModel({
id: 'phi-3-mini',
version: '3.0',
file: 'chrome-extension://azipopnxdpcknwapfrtdedlnjjkmpnao/phi-3-mini.bin',
loraFile: 'chrome-extension://azipopnxdpcknwapfrtdedlnjjkmpnao/phi-3-mini-lora.bin', // optional
defaultTemperature: 0.5,
defaultTopK: 3,
maxTopK: 10
})
Then it could be listed by web apps like this:
const models = await ai.listModels(); // ['gemini-nano', 'phi-3-mini']
The model metadata could be accessed like this:
const modelInfo = await ai.textModelInfo('phi-3-mini'); // {id: 'phi-3-mini', version: '3.0', defaultTemperature: 0.5, defaultTopK: 3, maxTopK: 10}
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