QLoRA (quantized LoRA)
QLoRA (quantized LoRA) is a machine learning technique that combines quantization and Low-Rank Adaptation to efficiently fine-tune large language models with reduced computational resources while maintaining performance.
fine-tuning
Prompt tuning
Prompt tuning is a technique in natural language processing that adapts large language models to specific tasks by optimizing a small set of prompt parameters instead of fine-tuning the entire model. It offers a parameter-efficient alternative to traditional model fine-tuning, enabling task adaptation with reduced computational resources.
Instruction tuning
Instruction tuning is a technique in machine learning where models are fine-tuned on datasets containing task instructions, enhancing their ability to follow diverse prompts and perform various tasks. This approach improves model generalization and adaptability across multiple applications.
Guidance (language model programming)
Guidance in language model programming refers to techniques and frameworks used to control and direct the behavior of large language models during text generation. It enables developers to shape outputs according to specific tasks, constraints, or user intentions.