The chapter explores the fine-tuning of Mixtral 8x22B using the ORCO and Oracle methods, focusing on leveraging small datasets and pre-trained language models for improved results. Techniques like ORPO and DPO with chosen responses are discussed, highlighting the efficiency in learning knowledge and style while maintaining desired probabilities. The chapter also touches on new releases from Google, including Gemma models for code generation and the efficiency of the recurrent Gemma model in handling context length.

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