🎯 Fine-Tuning Strategies
Efficient techniques for adapting pre-trained models to your tasks
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Prompt Engineering Playground
Why Fine-Tuning?
🎯 The Challenge
Pre-trained models are powerful but generic. Fine-tuning adapts them to your specific task, improving performance on domain-specific data without training from scratch.
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Key Insight
Full fine-tuning updates all parameters but requires massive compute. Modern techniques like LoRA achieve similar results while training only 0.1-1% of parameters.
❌ Training from Scratch
- •Requires millions of examples
- •Weeks/months of training time
- •Expensive GPU clusters ($100k+)
- •High risk of poor convergence
✅ Fine-Tuning
- •Works with 100-10,000 examples
- •Hours to days of training
- •Single GPU sufficient ($100-1000)
- •Leverages pre-learned features
📊 Common Use Cases
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Domain Adaptation
Medical, legal, financial - adapt GPT to specialized terminology
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Style Matching
Company tone, brand voice, writing style consistency
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Task Specialization
Summarization, extraction, classification for specific data