Analysis
This article highlights an innovative no-code approach to evaluating the performance of fine-tuned Large Language Models. It provides a user-friendly guide within the FPT AI FACTORY, making model testing and comparison accessible. This simplifies the critical process of assessing whether Fine-tuning has improved an LLM's output in areas like stability and format.
Key Takeaways
- •No-code testing simplifies LLM fine-tuning evaluation.
- •The article provides a step-by-step guide within the FPT AI FACTORY.
- •Focus is placed on evaluating output stability and format.
Reference / Citation
View Original"Fine-tuning a model is more difficult in "designing the evaluation" than "running the learning"."