A quick overview of Fine-Tuning approaches in Large Language Models
Hey everyone 👋
I’ve been trying to organize the different types of fine-tuning used in modern LLMs, and I made a simple “map” to help visualize how they relate to each other.
Fine-tuning in general is the process of adapting a pre-trained model to a specific task or domain, but it has evolved into several directions:
- Full Fine-Tuning: updating all model weights (powerful but expensive)
- Instruction Fine-Tuning: training on instruction-response datasets to improve general usability
- PEFT (Parameter-Efficient Fine-Tuning): updating only small parts of the model
- LoRA (Low-Rank Adaptation): injecting trainable low-rank matrices
- Adapters: small layers inserted between transformer blocks
- Prefix Tuning: learning task-specific prefix tokens
- Prompt Tuning: optimizing soft prompts instead of weights
- RLHF (Reinforcement Learning from Human Feedback): aligning outputs with human preferences
- Domain-Specific Fine-Tuning: adapting to medical, legal, or financial text
I tried to visualize how these methods branch from standard fine-tuning and where each one fits in terms of efficiency vs performance.
Would love feedback if I missed anything or if you’d structure it differently.
u/Altruistic_File_6117 — 3 days ago