🌟 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:
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The architecture and logic behind LLMs like GPT-3.5/4, BERT, and T5
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How to prepare and clean English-language datasets for fine-tuning
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Perform fine-tuning with Hugging Face and OpenAI’s GPT-4 API
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Use prompt-tuning, instruction tuning, and LoRA methods
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Evaluate, debug, and optimize model performance
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Customize AI behavior, writing style, or tone
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Build deployable NLP applications using your tuned models
✅ Tools & Frameworks Covered:
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Python & Jupyter Notebook
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OpenAI GPT-3.5 / GPT-4 API
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Hugging Face Transformers
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Google Colab
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Datasets: Common Crawl, WikiText, JSONL, etc.
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Weights & Biases (for tracking training)
✅ Who This Course Is For:
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Developers & engineers interested in real-world LLM training
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Data scientists exploring GenAI customization
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AI hobbyists and students learning fine-tuning from scratch
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Researchers working with English NLP tasks
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Anyone interested in customizing AI behavior without building from scratch
1 - Introduction
2 - Introduction To Generative AI
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91 -What is Generative AI (Gen AI)
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102 -More on Gen AI
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113 -How Does Gen AI Work
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124 -Gen AI Models
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135 -What are GPTs
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146 -Interplays Between Gen-AI and LLMs
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157 -Introduction to Open API
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168 -Other Large Language Models (LLMs)
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179 -Start With Hugging Face
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1810 -Access and Use Other LLMs Via Hugging Face
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1911 -Access Mistral LLM With Hugging Face
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2012 -LLMs via GCP
3 - Start With LLMs
4 - Fine Tuning LLMs
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281 -LLMOps Theory
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292 -Introduction to Fine Tuning Concepts
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303 -Quantization
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314 -QLoRA-Prepare Your Data
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325 -QLoRA-Tokenization
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336 -QLoRA Quantization
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347 -SGD Theory
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358 -SGD Implementation For LLM Optimisation
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369 -What is Soft Prompting
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3710 -Setting Up Soft-Prompting Analysis
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3811 -Implement Soft-Prompting
Lessons:
