Finetuning

Welcome to Finetuning

This section covers various techniques for adapting pre-trained language models to specific tasks and domains. Learn about different approaches to fine-tuning, from full parameter tuning to more efficient methods.

• Full Fine-tuning: Techniques for updating all model parameters
• Parameter-Efficient Fine-tuning (PEFT): Methods like LoRA, adapters, and prompt tuning
• Instruction Tuning: Aligning models with human instructions
• Domain Adaptation: Specializing models for specific domains

All Finetuning Posts