Fine-Tuning with ReFT: Create an Emoji LLM for Medical Diagnosis 

Hey everyone! In this tutorial, I'll walk you through an exciting new fine-tuning method called ReFT (representation fine-tuning) using the powerful pyreft library. 🌟
Discover how you can fine-tune pretrained language models with fewer parameters and achieve potentially better performance. We'll dive into the process of creating an emoji LLM (Language Model) tailored for medical diagnosis questions and their corresponding emoji responses.
Here's what you'll learn:
1. How to set up and use pyreft for fine-tuning any HuggingFace pretrained language models.
2. Configuring ReFT hyperparameters via easy-to-use configs.
3. Sharing your fine-tuned models effortlessly on HuggingFace.
This video is perfect for anyone looking to boost fine-tuning efficiency, reduce costs, and explore the interpretability of adapting parameters.
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GitHub: github.com/AIA...
PyReFT Library: github.com/sta...
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