Principle:Ggml org Llama cpp Fine Tuning
| Knowledge Sources | |
|---|---|
| Domains | Training, Fine_Tuning |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Fine Tuning is the principle of adapting a pre-trained language model to specific tasks or domains through continued training on targeted data.
Description
This principle covers the fine-tuning capabilities within llama.cpp that allow users to continue training a model on custom datasets. Unlike LoRA adapters which add external modifications, full fine-tuning updates the model's own weights directly. The implementation handles gradient computation, parameter updates, training data loading, and checkpoint management within the GGML framework.
Usage
Apply this principle when a pre-trained model needs to be adapted to a specific domain, style, or task, and LoRA adaptation does not provide sufficient modification capacity or when full weight updates are desired.
Theoretical Basis
Fine-tuning applies stochastic gradient descent (or a variant such as Adam) to update a pre-trained model's parameters on a task-specific dataset. The process involves computing a loss function (typically cross-entropy for language modeling), backpropagating gradients through the network, and applying parameter updates. Fine-tuning must balance adaptation to the new data against catastrophic forgetting of the pre-trained knowledge. Practical considerations include learning rate selection, training data quality, batch size, and the number of training epochs. The GGML-based implementation enables fine-tuning without requiring a separate training framework like PyTorch.