4 projects for you to learn fine-tuning transformers
Enhance your skills with our hands-on projects focused on fine-tuning models. Fine-tuning in machine learning is the process of adapting a pre-trained model for specific tasks or use cases through transfer learning. It has become a crucial technique in deep learning, particularly in training foundation models used for generative AI.
This comprehensive pathway combines essential tools and frameworks with hands-on projects, providing a solid base in fine-tuning expertise. By focusing on projects rather than just courses, you gain real-world skills in generative AI and enjoy the satisfaction of seeing your enhanced models in action.
Through the guided projects below, you will learn to fine-tune models using PyTorch and HuggingFace. Explore advanced techniques like domain adaptation, and layer freezing to maximize the effectiveness of your models. Starting off, you will learn how to fine-tune a transformer-based neural network with PyTorch, then you will learn more complex strategies such as applying adapters for parameter efficient fine-tuning (PEFT). You will work with state-of-the-art PEFT method, such as LoRA, and explore memory-efficient techniques like quantization with QLoRA
Start learning today to gain hands-on experience in fine-tuning large language models. Master essential tools, optimize model performance, and develop tailored applications with these guided projects ranging from classification to sentiment analysis.
