Chapter 3: Ablation Studies

Understanding What Works and Why

Ablation studies systematically test components to understand their contribution.

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What to Ablate

Component	Variations to Test
LoRA Rank	4, 8, 16, 32, 64
Target Modules	Minimal → Full
Learning Rate	Log scale
Data Size	100, 500, 1K, 5K
Epochs	1, 2, 3, 5

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Ablation Framework

def run_ablation(base_config, param_name, param_values):
    """Run ablation for a single parameter."""
    results = []
    
    for value in param_values:
        config = base_config.copy()
        config[param_name] = value
        
        # Train
        model = train_model(config)
        
        # Evaluate
        score = evaluate_model(model)
        
        results.append({
            param_name: value,
            "score": score,
        })
    
    return results

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Reporting Results

LoRA Rank	Task Score	Training Time
4	85.2%	45 min
8	87.1%	50 min
16	88.3%	55 min
32	88.5%	65 min
64	88.4%	80 min

Insight: Rank 16 offers best quality/efficiency trade-off.

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Best Practices

1. Change one thing at a time
2. Use same seed for reproducibility
3. Run multiple seeds for final results
4. Document everything
5. Focus on practical differences

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