Fine-tuning and Adapting GenAI Models in English

🌟 Fine-tuning is the key to unlocking the full potential of Generative AI.

In this practical, beginner-to-intermediate course, you’ll learn how to fine-tune and adapt large language models (LLMs) like GPT, BERT, and LLaMA using English-language datasets. You’ll gain hands-on experience customizing pre-trained models for tasks like chatbots, summarization, content generation, translation, and question answering.

Whether you’re a developer, data scientist, AI researcher, or content creator — this course will give you the tools to train GenAI models that understand your domain, tone, or task-specific goals.

We’ll use tools like OpenAI’s fine-tuning API, Hugging Face Transformers, and Google Colab, with a strong focus on practical implementation, not just theory.

✅ What You’ll Learn:

• The architecture and logic behind LLMs like GPT-3.5/4, BERT, and T5

• How to prepare and clean English-language datasets for fine-tuning

• Perform fine-tuning with Hugging Face and OpenAI’s GPT-4 API

• Use prompt-tuning, instruction tuning, and LoRA methods

• Evaluate, debug, and optimize model performance

• Customize AI behavior, writing style, or tone

• Build deployable NLP applications using your tuned models

✅ Tools & Frameworks Covered:

• Python & Jupyter Notebook

• OpenAI GPT-3.5 / GPT-4 API

• Hugging Face Transformers

• Google Colab

• Datasets: Common Crawl, WikiText, JSONL, etc.

• Weights & Biases (for tracking training)

✅ Who This Course Is For:

• Developers & engineers interested in real-world LLM training

• Data scientists exploring GenAI customization

• AI hobbyists and students learning fine-tuning from scratch

• Researchers working with English NLP tasks

• Anyone interested in customizing AI behavior without building from scratch