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Fine-Tuning Models and Recommended Tools
This chapter explores the advice and tips for fine-tuning models for specific tasks, including the importance of hyperparameters, training for more tokens and epochs, and the recommendation of the Axolotl library. They discuss various approaches and techniques in AI and machine learning, such as creating new instruction methodologies, merging models, and implementing techniques like DPO and reward models for censorship. They also touch on concepts like chain of thought, tree of thought, activation hacking, soft prompting, and the need for better sampling methods in AI.