A practical guide to implementing supervised fine-tuning for large language models, covering data preparation, hyperparameters, common pitfalls, and real-world examples to customize AI models effectively.
Optimizing attention patterns in domain-specific LLMs improves accuracy by guiding models to focus on relevant terms and relationships. Techniques like LoRA cut costs and boost performance without full retraining.