Optimize AI Models for Production

import torch
from nexus_opt import quantize_model

# Load your trained model
model = torch.load('model.pth')

# Apply INT8 quantization
quantized_model = quantize_model(
    model,
    quantization_type='int8',
    calibration_data=calibration_loader,
    symmetric=True
)

# Compare sizes
original_size = model.get_memory_footprint()
quantized_size = quantized_model.get_memory_footprint()
print(f"Compression: {original_size/quantized_size:.1f}x")

from nexus_opt import MixedPrecisionOptimizer

# Automatically find optimal precision per layer
optimizer = MixedPrecisionOptimizer(
    model,
    accuracy_threshold=0.99,  # Maintain 99% accuracy
    latency_target=10         # Target 10ms latency
)

# Search for optimal configuration
optimal_config = optimizer.search(
    calibration_data,
    search_space=['int8', 'int4', 'fp16'],
    num_trials=100
)

optimized_model = optimizer.apply(optimal_config)

from nexus_opt import DistillationTrainer

# Large teacher model
teacher = LargeModel()
teacher.load_state_dict(torch.load('teacher.pth'))
teacher.eval()

# Small student model
student = SmallModel()

# Train student to mimic teacher
trainer = DistillationTrainer(
    teacher=teacher,
    student=student,
    temperature=3.0,
    alpha=0.7
)

trainer.train(train_loader, epochs=100)
# Student is 10x smaller with 95% accuracy