Finetuning

Finetuning is a transfer learning method: it adapts a pre-trained Large Language Model (LLM) like GPT-4 or Llama 3 by training it on a small, task-specific dataset to achieve specialized performance.

Finetuning takes a general-purpose foundation model (e.g., Llama-3.1-8B, GPT-4) and specializes it for a high-value task. This is targeted optimization, not training from scratch; we use a small, high-quality dataset, often just a few hundred examples. We leverage Parameter-Efficient Fine-Tuning (PEFT) methods like QLoRA to update only a fraction of the parameters, drastically cutting VRAM and computational cost. The result is a specialized agent: a model that delivers higher accuracy and consistently formatted responses for domain-specific applications, such as legal contract analysis or custom customer service chatbots.

https://platform.openai.com/docs/guides/fine-tuning