A freshly pre-trained LLM is like a college graduate with broad knowledge but no specialized expertise. They understand language and concepts but haven't focused on any specific field.
After fine-tuning, the model becomes like a specialized doctor or lawyer - still retaining general knowledge but now excelling in a specific domain with targeted expertise.
Before fine-tuning, models undergo massive pre-training on internet-scale data. Here's how it works:
Grammar, syntax, semantics, and the structure of human language across many languages.
Facts, concepts, and information about history, science, culture, and more.
Logical inference, analogical thinking, and problem-solving strategies.
A step-by-step visualization of how models learn from your custom data
Clean, label, and format your training data
Different approaches for different needs - see how much of the model gets updated
All parameters trainable
Updates every parameter in the model. Best for maximum adaptation but requires significant compute resources.
Parameter-efficient
Adds small trainable matrices to frozen layers. Achieves similar results with fraction of compute.
Task-specific format
Trains on instruction-response pairs to improve the model's ability to follow directions.
Specialized knowledge
Focuses on specific domains like medical, legal, or scientific texts for expert-level understanding.
Experiment with different settings and watch how they affect model training
See how fine-tuned models are transforming various industries
Brand-specific conversational AI that maintains company voice and knowledge.
Clinical note analysis, symptom assessment, and medical literature summarization.
Contract review, case research, and regulatory compliance checking.
Automated ticket routing, response generation, and sentiment analysis.
Understanding when to use each approach
Craft better inputs
Customize the model
End-to-end pipeline from your data to deployed model
Test your understanding of LLM fine-tuning concepts
You've completed the Fine-Tuning Course
Understand the difference between general language learning and domain specialization
Know when to use full fine-tuning, LoRA, or instruction tuning
Understand data preparation, training loops, and evaluation
Choose the right approach for your use case