Huggingface Fine Tune Llama 2

Fine-Tuning Llama 2 with DPO: A Comprehensive Guide

Introduction

The Direct Preference Optimization (DPO) method, now integrated into the TRL library, empowers users to precisely fine-tune Llama 2 on specific datasets. This article provides a detailed guide to leveraging this technique for optimal results.

Fine-Tuning Llama 2 with DPO

To fine-tune Llama 2 using DPO, follow these steps:

- Install the TRL library and its DPO implementation.

- Load your dataset and define the preference criteria for fine-tuning.

- Define the model configuration and training parameters.

- Launch the training process using DPO.

Instruction Tuning

DPO enables instruction tuning of Llama 2, allowing users to customize its behavior through natural language instructions. This approach has proven effective in enhancing model performance on specific tasks.

Additional Tuning Techniques

Alongside DPO, the Hugging Face ecosystem provides various tools for efficient Llama 2 training on affordable hardware. Techniques like QLoRA, PEFT, and SFT address memory and compute limitations, ensuring smooth fine-tuning experiences.