Custom Fine-Tuning GPT-2 & StarCoder in PyTorch for Chatbots

Use basic PyTorch training loop to fine-tune pretrained GPT-2 & StarCoder transformers as basic ChatGPT-like assistants.

Created by: Shadi Ghaith
Software Developer & Tutor

What Will I Learn?

• Master basic PyTorch to fine-tune GPT-2 and StarCoder 2 for chat applications.
• Design and prepare custom datasets for training conversational models.
• Implement training loops, manage datasets, and optimize model performance.
• Utilize trained models to generate real-time, context-aware dialogues.
• Apply fine-tuning techniques across multiple domains and models.

Requirements

• Basic Knowledge of Python: Students should be comfortable with Python programming basics, as the course involves scripting in Python using PyTorch.
• Understanding of Machine Learning Basics: A fundamental understanding of machine learning concepts like models, datasets, and training loops is necessary.
• Familiarity with PyTorch: Prior experience with PyTorch is required.
• No Specialized Hardware Required: While a GPU can speed up training, it is not mandatory as the models can be trained with CPU-only setups.

Target audience

• Data Scientists and Machine Learning Enthusiasts: Professionals who have foundational knowledge in machine learning and wish to expand their expertise into the realm of natural language processing and conversational AI.
• Software Developers: Coders and developers who want to integrate AI-driven chat functionalities into their applications and need to understand the underlying technology to customize it effectively.
• Students in Computer Science: University or college students studying computer science, artificial intelligence, or related fields who are eager to apply theoretical knowledge in real-world projects, particularly in enhancing user interaction through AI.