Take a pre-trained model and teach it your domain, your tone, and your task. This course goes from the theory of why fine-tuning works all the way to running LoRA on a real dataset.
| This IS for you if... | This is NOT for you if... |
|---|---|
| You know Python basics and want to customize an LLM for your use case | You have never written a Python function (start with AI Basics first) |
| You want to understand LoRA, RLHF, and instruction tuning at a deep level | You only want a no-code chatbot builder |
| You work in a domain where a generic model makes mistakes (legal, medical, finance) | You are satisfied with prompt engineering results and don't need model customization |
| You want to build a specialized AI model as a product or internal tool | You expect to train from scratch — this course focuses on fine-tuning, not pre-training |
Understand the gap between what a pre-trained model knows and what your use case demands. Learn when fine-tuning is the right tool and when it isn't.
Build a training set the model can actually learn from. Cover data quality filters, instruction-response formatting, and the most common dataset mistakes.
Learn how low-rank adaptation works mathematically, why it cuts memory use dramatically, and how to configure LoRA rank and alpha for your task.
Teach the model to follow instructions, not just predict text. Cover supervised fine-tuning on instructions, reward models, and the PPO training loop used in RLHF.
Measure whether fine-tuning actually helped. Learn perplexity, task-specific benchmarks, human eval, and how to detect catastrophic forgetting.
Merge LoRA weights, quantize for speed, and serve via FastAPI or Hugging Face Inference Endpoints. Make the model accessible as a real API.
Apply everything to a real challenge. Fine-tune a small open-source model on a custom dataset, evaluate it, and earn your certificate.
90 minutes of focused learning. A fine-tuned model at the end. Free.
Start Module 1 →