The chapter explores using the LoRa technique for fine-tuning AI models, reducing parameters for faster downstream tasks. It details how rank decomposition matrices optimize inference efficiency, deploying multiple LoRa adapters, and efficient inference stack optimization. The discussion includes building a G&A inference service, focusing on transformer architecture and the intricacies of processing input and output tokens.

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