LLM Fine-Tuning Optimization Quick CheatSheet

Best Practices to Maximize Fine-tuned LLM Performance

Ready to take your fine-tuned large language model (LLM) to the next level? This cheat sheet provides practical tips and techniques to fine-tuning LLM to achieve optimal performance on your specific task.

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Want to dive deeper? Check out this article for a Comprehensive guide on Effectively fine-tuning Large Language Models(LLMs).

Fine-tuning a large language model (LLM) involves adapting a pre-trained LLM to a specific task or domain by further training it on a smaller, task-specific dataset.

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• Clearly define the target task and select a pre-trained model that performed well on a similar task.
• A good starting point for LLM fine-tuning is a dataset with at least 10,000 examples, but the ideal size will depend on the task's complexity and the model’s capacity.
• Divide the prompt into distinct sections using delimiters (e.g., ###) for easy parsing and understanding by the LLM. Remove…