Tag: LLM fine-tuning
Prompt-Tuning vs Prefix-Tuning: Lightweight LLM Control Guide
Discover the key differences between prompt-tuning and prefix-tuning for LLMs. Learn when to use each lightweight PEFT method to save compute resources while maintaining high accuracy.
Instruction Tuning for Large Language Models: Building Better Followers
Learn how instruction tuning transforms base LLMs into reliable assistants. We cover LoRA efficiency, data curation strategies, and the trade-offs between flexibility and accuracy.
Continual Learning for Large Language Models: Updating Without Full Retraining
Continual learning lets large language models adapt to new tasks without forgetting old knowledge. Discover how techniques like regularization, replay, and reinforcement learning enable updates without full retraining.
