Glossary · AI
What is
Supervised Fine-Tuning (SFT)?
Fine-tuning a model on labeled input-output pairs.
By Anish· Founder · Vedwix
·Definition
SFT is the most common fine-tuning approach: gather pairs of inputs and desired outputs, then train the model to produce those outputs. It works best when you have clean, high-quality data — typically 1,000+ pairs for a meaningful effect. SFT is often combined with LoRA to reduce compute cost.
Example
5,000 examples of customer support queries paired with ideal responses, used to fine-tune a Llama 3 8B model.
How Vedwix uses Supervised Fine-Tuning (SFT) in client work
We invest in dataset curation more than model size. Quality data > more parameters.
Building with Supervised Fine-Tuning (SFT)?
We ship this.
If you're building with Supervised Fine-Tuning (SFT) in production, we can help — from architecture review to full implementation.
Brief us