Model Distillation: Creating Efficient Specialized Models

Model distillation transfers knowledge from large models to smaller ones, enabling efficient deployment.

Distillation Pipeline

from azure.ai.openai import AzureOpenAI
import json
from typing import List, Dict

class DistillationPipeline:
    def __init__(self, teacher_client: AzureOpenAI, student_client: AzureOpenAI):
        self.teacher = teacher_client
        self.student = student_client

    async def generate_training_data(
        self,
        prompts: List[str],
        teacher_model: str = "gpt-4o"
    ) -> List[Dict]:
        """Generate training data from teacher model."""
        training_data = []

        for prompt in prompts:
            # Get high-quality response from teacher
            response = await self.teacher.chat.completions.create(
                model=teacher_model,
                messages=[{"role": "user", "content": prompt}],
                temperature=0  # Deterministic for consistency
            )

            training_data.append({
                "messages": [
                    {"role": "user", "content": prompt},
                    {"role": "assistant", "content": response.choices[0].message.content}
                ]
            })

        return training_data

    async def generate_reasoning_data(
        self,
        prompts: List[str],
        teacher_model: str = "gpt-4o"
    ) -> List[Dict]:
        """Generate data with reasoning traces for better distillation."""
        training_data = []

        for prompt in prompts:
            # Get reasoning from teacher
            response = await self.teacher.chat.completions.create(
                model=teacher_model,
                messages=[
                    {"role": "system", "content": "Think step by step and show your reasoning."},
                    {"role": "user", "content": prompt}
                ]
            )

            training_data.append({
                "messages": [
                    {"role": "user", "content": prompt},
                    {"role": "assistant", "content": response.choices[0].message.content}
                ]
            })

        return training_data

    async def fine_tune_student(
        self,
        training_data: List[Dict],
        student_model: str = "gpt-4o-mini"
    ) -> str:
        """Fine-tune student model on teacher outputs."""
        # Upload training data
        jsonl = "\n".join([json.dumps(d) for d in training_data])
        file_response = await self.student.files.create(
            file=jsonl.encode(),
            purpose="fine-tune"
        )

        # Create fine-tuning job
        job = await self.student.fine_tuning.jobs.create(
            training_file=file_response.id,
            model=student_model
        )

        return job.id

    async def evaluate_distillation(
        self,
        test_prompts: List[str],
        teacher_model: str,
        student_model: str
    ) -> Dict:
        """Compare teacher and distilled student performance."""
        results = {"teacher": [], "student": []}

        for prompt in test_prompts:
            teacher_response = await self.teacher.chat.completions.create(
                model=teacher_model,
                messages=[{"role": "user", "content": prompt}]
            )

            student_response = await self.student.chat.completions.create(
                model=student_model,
                messages=[{"role": "user", "content": prompt}]
            )

            # Compare quality, latency, cost
            results["teacher"].append(self.evaluate_response(teacher_response))
            results["student"].append(self.evaluate_response(student_response))

        return self.aggregate_results(results)

Distillation enables production deployment with 10x cost reduction while maintaining quality.