Project 6: Knowledge Distillation Trainer (MLP Version)

Train a small student model to mimic a larger teacher using knowledge distillation.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 12-18 hours
Language Python
Prerequisites PyTorch basics, training loops
Key Topics distillation, teacher-student training

1. Learning Objectives

By completing this project, you will:

  1. Implement teacher-student distillation.
  2. Compare hard labels vs soft targets.
  3. Tune temperature and loss weighting.
  4. Measure student accuracy vs baseline.
  5. Export a distilled model.

2. Theoretical Foundation

2.1 Distillation Intuition

Distillation transfers knowledge from a larger model into a smaller one using soft targets.

3. Project Specification

3.1 What You Will Build

A distillation pipeline that trains a student MLP from a teacher model on a simple dataset.

3.2 Functional Requirements

  1. Teacher model trained on dataset.
  2. Student model with smaller capacity.
  3. Distillation loss with temperature.
  4. Evaluation against baseline student.
  5. Export distilled weights.

3.3 Non-Functional Requirements

  • Deterministic runs with fixed seeds.
  • Clear training logs.
  • Configurable temperature.

4. Solution Architecture

4.1 Components

Component Responsibility
Teacher Provide soft targets
Student Learn distilled knowledge
Trainer Run distillation loop
Evaluator Compare metrics

5. Implementation Guide

5.1 Project Structure

QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY/P06-distillation/
├── src/
│   ├── teacher.py
│   ├── student.py
│   ├── train.py
│   └── eval.py

5.2 Implementation Phases

Phase 1: Teacher training (4-6h)

  • Train teacher model.
  • Checkpoint: teacher accuracy baseline.

Phase 2: Distillation (4-6h)

  • Train student with distillation loss.
  • Checkpoint: student improves vs hard labels.

Phase 3: Evaluation (3-6h)

  • Compare student vs baseline.
  • Checkpoint: report shows gains.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit loss distillation loss correctness
Integration training student learns
Regression eval stable accuracy

6.2 Critical Test Cases

  1. Student beats baseline model without distillation.
  2. Temperature affects soft targets.
  3. Distilled model exports correctly.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Too-hot temperature noisy targets tune temperature
Student too small poor accuracy increase capacity
Overfitting eval drop add regularization

8. Extensions & Challenges

Beginner

  • Add a CNN student model.
  • Add training curves.

Intermediate

  • Distill from multiple teachers.
  • Add data augmentation.

Advanced

  • Distill LLM logits on small datasets.
  • Add adaptive temperature schedules.

9. Real-World Connections

  • Model compression relies on distillation.
  • Edge deployment uses small distilled models.

10. Resources

  • Knowledge distillation papers
  • Training loop references

11. Self-Assessment Checklist

  • I can implement distillation loss.
  • I can tune temperature and weights.
  • I can evaluate student vs teacher.

12. Submission / Completion Criteria

Minimum Completion:

  • Distilled student model

Full Completion:

  • Evaluation report

Excellence:

  • Multi-teacher distillation
  • Adaptive temperature

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY.md.