Investing in Performance: Fine-tune small models with LLM insights  - a CFM case study
oahouzi, florentgbelidji, sylvainc, jlhour, Pirash, bpatault, MGoibert
**_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 for scalable NER applications_

_This structured approach combines accuracy and cost-effectiveness, making it ideal for real-world financial applications._ 

|     **_Model_**     | **_F1-Score (Zero-Shot)_** | **_F1-Score (Fine-Tuned)_** | **_Inference Cost (per hour)_** | **_Cost Efficiency_** |
| :