Fine-Tuning LLMs for Financial Sentiment

Off-the-shelf models are decent, but if you want to trade on sentiment, you need an edge. Fine-tuning an open-weight model like Llama-3 on highly specific, hand-labeled domain data is how you get it.

The Dataset

Do not use generic datasets like FinBERT's training data—it's already priced in. You need to construct your own dataset of 1,000+ examples where base models fail. For instance, parsing options flow commentary or obscure Fed speak.

PEFT / LoRA

You don't need a massive GPU cluster. Using Parameter-Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA), you can fine-tune an 8B model on a single A100 instance (costing a few dollars on RunPod) in under an hour.