Examples

This page provides practical examples of using Purpose in various scenarios, including real results from successful distillation processes.

🎯 Real Distillation Results

Our latest enhanced distillation process successfully created a high-quality domain-specific dataset from academic papers:

Results Summary

Curriculum Structure

The system automatically organized content into a 3-tier learning progression:

Stage Count Focus
Basic 29 pairs Fundamental concepts and frameworks
Intermediate 29 pairs Applied analysis and evaluation
Advanced 29 pairs Complex research and synthesis

📈 Content Quality Analysis

Domain-Specific Terminology Integration:

Example Enhanced Question-Answer Pair:

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Question: "How might advancements in Microarray-based Epigenetic Technology influence an athlete's hurdle clearance time?"

Enhanced Answer: Integrates ChIP-on-chip technology, mathematical models (P = m × a), Performance Optimization Model theory, biomechanical analysis, and ethical considerations - expanding from a 2-sentence answer to comprehensive 400+ word analysis.

🔬 Technical Depth Achieved

Mathematical Integration:

Concept Coverage:

📊 Performance Metrics

Enhancement Quality:

File Outputs:

Basic Processing Example

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from purpose import Processor

# Initialize processor
processor = Processor()

# Example data
data = {
    'text': 'Sample text for processing',
    'parameters': {'key': 'value'}
}

# Process the data
result = processor.process_data(data)
print(result)

Enhanced Distillation Example

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from purpose import EnhancedDistillation

# Real example that produced the above results
distiller = EnhancedDistillation(
    model_name="gpt-4",
    enhancement_level="advanced"
)

# Process domain papers
results = distiller.distill_papers(
    papers_dir="content/papers",
    num_qa_pairs=200,
    curriculum_stages=3
)

# Results: 87 enhanced QA pairs in ~2 minutes
print(f"Generated {results.total_pairs} enhanced QA pairs")
print(f"Curriculum stages: {results.stages}")

Distributed Processing Example

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from purpose import DistributedProcessor
import numpy as np

# Create sample data
data_batch = [np.random.rand(100, 100) for _ in range(10)]

# Initialize distributed processor with 4 workers
dp = DistributedProcessor(workers=4)

# Process data in parallel
results = dp.process_batch(data_batch)

# Aggregate results
final_result = np.mean([r for r in results])

Model Optimization Example

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from purpose import ModelOptimizer
from purpose.models import SampleModel

# Create a sample model
model = SampleModel()

# Initialize optimizer
optimizer = ModelOptimizer(strategy='medium')

# Optimize the model
optimized_model = optimizer.optimize(model)

# Compare performance
original_score = model.evaluate()
optimized_score = optimized_model.evaluate()
print(f"Performance improvement: {optimized_score - original_score}")

Knowledge Distillation Example

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from purpose import Distiller
from purpose.models import TeacherModel, StudentModel

# Create teacher and student models
teacher = TeacherModel()
student = StudentModel()

# Initialize distiller
distiller = Distiller(teacher, student)

# Prepare training data
train_data = load_training_data()

# Perform distillation
distilled_model = distiller.distill(train_data)

# Evaluate distilled model
score = distilled_model.evaluate()
print(f"Distilled model score: {score}")

Configuration Example

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from purpose import Config, Processor

# Create configuration
config = Config(
    batch_size=64,
    max_workers=8,
    optimization_level='high',
    logging=True
)

# Initialize processor with config
processor = Processor(config)

# Save configuration
config.save('config.yaml')

# Load configuration
loaded_config = Config.load('config.yaml')

Error Handling Example

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from purpose import Processor, ProcessingError

try:
    processor = Processor()
    result = processor.process_data(invalid_data)
except ProcessingError as e:
    print(f"Processing failed: {e}")
    # Handle error appropriately

Batch Processing with Progress

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from purpose import Processor
from tqdm import tqdm

processor = Processor()
data_batches = [batch1, batch2, batch3, batch4]

results = []
for batch in tqdm(data_batches, desc="Processing batches"):
    result = processor.process_data(batch)
    results.append(result)

Custom Pipeline Example

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from purpose import Processor, preprocess_data, postprocess_results

class CustomPipeline:
    def __init__(self):
        self.processor = Processor()
    
    def run(self, data):
        # Preprocess
        preprocessed = preprocess_data(data)
        
        # Process
        processed = self.processor.process_data(preprocessed)
        
        # Postprocess
        result = postprocess_results(processed)
        
        return result

# Use custom pipeline
pipeline = CustomPipeline()
result = pipeline.run(input_data)

🚀 Next Steps

Want to reproduce these results?

  1. Get Started with Purpose installation
  2. Check the API Reference for detailed documentation
  3. Try the tutorials for step-by-step guides
  4. Explore specialized models for your domain

For more detailed API documentation, please refer to the API Reference section.