LoRA Fine-Tuning for GPT-2 Trading Models

Summary

The article demonstrates parameter-efficient LoRA fine-tuning of a pre-trained GPT-2 model on financial data, following an earlier installment on full-parameter fine-tuning. It explains how to configure LoRA with the PEFT library, including adapter rank, target modules, scaling, dropout, and initialization options, then applies the configured model in a trading-strategy workflow. The examples also compare the LoRA model with a fully fine-tuned model using predictions and model metrics.

The article frames these comparisons as a way to choose a training approach for a particular currency pair and market condition. It does not establish that the shown settings are optimal or provide enough evidence to generalize performance across instruments or regimes. The author recommends broader comparisons across pairs, data preparation methods, and fine-tuning methods, while noting that the GPT-2 model’s scale may limit the results relative to an ideal system.

Key ideas

• LoRA adapts selected model layers with trainable low-rank adapters rather than updating every parameter.
• The PEFT library provides configuration and model-wrapping tools for applying LoRA to GPT-2.
• Adapter settings such as rank, target modules, scaling, and dropout can affect training and should be explored.
• The article compares LoRA and full-parameter fine-tuning through model predictions and evaluation metrics.
• Results for one currency pair or market condition do not establish which method will work best elsewhere.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.