LLM Fine-Tuning

LLM Fine-Tuning: The process of adapting a pre-trained foundation model (e.g., Llama, Mistral) to a specific, high-value task using techniques like LoRA to boost accuracy and reduce compute costs.

LLM Fine-Tuning is the essential post-training step: it shifts a generalist Large Language Model into a domain-specific specialist. We take a base model (like Llama 3.1 8B) and train it on a small, high-quality dataset, a process known as Supervised Fine-Tuning (SFT). Critical to efficiency are Parameter-Efficient Fine-Tuning (PEFT) methods: LoRA and QLoRA drastically reduce the trainable parameters, enabling customization of massive models (70B+) on less hardware. This results in superior, consistent performance for target applications, such as generating on-brand customer support responses or classifying legal documents with 99%+ accuracy.

https://github.com/pytorch/torchtune