Investing in Performance: Fine-tune small models with LLM insights – a CFM case study
Overview: This article presents a deep dive into Capital Fund Management’s (CFM) use of open-source large language models (LLMs) and the Hugging Face (HF) ecosystem to optimize Named Entity Recognition (NER) for financial data. By leveraging LLM-assisted labeling with HF Inference Endpoints and refining data with Argilla, the team improved accuracy by up to 6.4% and reduced operational costs, achieving solutions up to 80x cheaper than large LLMs alone.
In this post, you will learn:
- How to use LLMs for efficient data labeling
- Steps for fine-tuning compact models with LLM insights
- Deployment of models on Hugging Face Inference Endpoints