Introduction to Fine-Tuning
Fine‑tuning is the process of taking a pre‑trained LLM and training it further on a smaller, specialized dataset. This adapts the model to a specific task, style, or domain.
Fine‑tuning updates the model’s weights to improve performance on a narrow task, while prompt engineering does not change the model.
When to Fine‑tune Instead of Prompt?
- You need a specific output format or tone consistently.
- The task is complex and few‑shot prompting is unreliable.
- You have hundreds or thousands of examples.
- You want to reduce latency/cost (a fine‑tuned small model can outperform a large model with prompting).
Parameter‑Efficient Fine‑tuning (PEFT)
Full fine‑tuning of a 7B‑parameter model is expensive. PEFT methods like LoRA (Low‑Rank Adaptation) and QLoRA add small trainable matrices to each layer, reducing trainable parameters by 10,000x. QLoRA also uses 4‑bit quantization to run on consumer GPUs.
Instruction Tuning & RLHF
- Instruction tuning: Fine‑tuning on (instruction, response) pairs to make models follow instructions better (used for ChatGPT).
- RLHF (Reinforcement Learning from Human Feedback): Uses human preferences to align the model with helpful, harmless, honest outputs.
Tools for Fine‑tuning
- OpenAI fine‑tuning API (for GPT‑3.5) – you upload training data, they handle compute.
- Hugging Face Transformers + PEFT + LoRA – run on your own GPU.
- Unsloth – optimized fine‑tuning library.
Two Minute Drill
- Fine‑tuning adapts a pre‑trained model to a specific task.
- LoRA/QLoRA are efficient methods that add small matrices.
- Instruction tuning makes models follow instructions.
- RLHF aligns models with human preferences.
Need more clarification?
Drop us an email at career@quipoinfotech.com