该流程首先使用 TRL/SFTTrainer 对 JSONL 格式的训练数据上的 google/functiongemma-270m-it 基础模型进行微调。训练完成后，使用 ai-edge-torch 和 dynamic_int8 量化算法将模型转换为 TFLite 格式。最后一步取决于目标运行时环境：对于 MediaPipe，将 TFLite 模型与分词器和停止标记合并到一个 .task 包中，该包可在 iOS、Android 和 Web 上运行。或者，你可以将其打包为 .litertlm 格式，用于 LiteRT-LM 运行时，该运行时提供 NPU 加速和更广泛的平台支持，包括桌面平台。
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，更多细节参见51吃瓜
After six agonising minutes, Jack Swigert's voice cut through the silence.
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AIO requires understanding how language models decide which sources to reference when answering questions. These models don't follow the same rules as search engine algorithms. They're not counting backlinks or analyzing page load speed. They're evaluating whether content provides clear, accurate, comprehensive answers to questions people actually ask. They're assessing credibility through different signals than traditional search engines use. They're making probabilistic decisions about which information best satisfies a query based on patterns learned during training and information retrieved during real-time web searches.