<ul><li>Training Large Language Models (LLMs) involves stages like pre-training and fine-tuning.</li><li>Pre-training starts with acquiring generic knowledge from various sources like web crawls and user records.</li><li>Fine-tuning adjusts a base model towards a specific domain using new data.</li><li>Fine-tuning allows adding domain-specific capabilities without the need for extensive pre-training.</li><li>The quality of data used in fine-tuning significantly impacts the LLM's performance.</li><li>Behavioral Cloning is a common fine-tuning method to mimic provided input-output pairs.</li><li>Fine-tuning requires a balance to avoid over-optimization for performance and limit abstraction abilities.</li><li>Considerations for fine-tuning include model size, architecture, data quality, and compute budget.</li><li>There is no universal formula for determining the exact amount of data needed for fine-tuning.</li><li>Supervised Fine-Tuning (SFT) is a popular way to specialize LLMs, but sometimes Reinforcement Learning with Human Feedback (RLHF) may be more effective.</li></ul>

Fine-Tuning, Because Your Model Deserves a Second Chance

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