Project 4: Build a Quantization Toolkit

Implement basic quantization (int8/4-bit) and compare latency, memory, and accuracy tradeoffs.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 1–2 weeks
Language Python
Prerequisites Model internals, numeric precision
Key Topics quantization, calibration, error analysis

Learning Objectives

By completing this project, you will:

  1. Implement post-training quantization for weights.
  2. Calibrate scaling factors with sample data.
  3. Measure accuracy drop on evaluation tasks.
  4. Benchmark latency and memory savings.
  5. Export quantized artifacts for deployment.

The Core Question You’re Answering

“How much accuracy can you trade for memory and speed in inference?”

Quantization only matters if you measure that tradeoff precisely.

Concepts You Must Understand First

Concept Why It Matters Where to Learn
Quantization error Predict quality loss Quantization guides
Calibration Reduce error PTQ docs
Precision tradeoff Memory vs accuracy Hardware docs

Theoretical Foundation

Quantization Pipeline

FP32 -> Calibrate -> INT8/INT4 -> Evaluate

Calibration is the difference between usable and broken outputs.

Project Specification

What You’ll Build

A toolkit that quantizes a model, runs benchmarks, and reports quality impact.

Functional Requirements

  1. Quantize weights to int8 (optional 4-bit)
  2. Calibration dataset loader
  3. Evaluation pipeline for quality loss
  4. Benchmark latency and memory
  5. Export quantized model artifacts

Non-Functional Requirements

  • Deterministic evaluation
  • Clear report output
  • Safe fallback to fp16

Real World Outcome

Example report:

{
  "fp16_ppl": 12.3,
  "int8_ppl": 13.1,
  "memory_saving": "3.8x",
  "latency_speedup": "1.4x"
}

Architecture Overview

┌──────────────┐   model   ┌──────────────┐
│ Calibrator   │────────▶│ Quantizer    │
└──────────────┘         └──────┬───────┘
                                ▼
                         ┌──────────────┐
                         │ Evaluator    │
                         └──────────────┘

Implementation Guide

Phase 1: Quantization Baseline (4–6h)

  • Implement int8 quantization
  • Checkpoint: model runs inference

Phase 2: Calibration (4–8h)

  • Use calibration data for scaling
  • Checkpoint: accuracy drop minimized

Phase 3: Benchmarking (4–6h)

  • Measure latency and memory
  • Checkpoint: report generated

Common Pitfalls & Debugging

Pitfall Symptom Fix
Severe accuracy loss unusable output improve calibration
No speedup latency unchanged verify kernel support
Unsupported ops crashes fallback per-layer

Interview Questions They’ll Ask

  1. Why does calibration improve quantization quality?
  2. How do you measure accuracy vs memory tradeoffs?
  3. When is quantization not worth it?

Hints in Layers

  • Hint 1: Start with per-tensor int8 quantization.
  • Hint 2: Add calibration data to scale weights.
  • Hint 3: Compare PPL or task accuracy.
  • Hint 4: Export and reload quantized model.

Learning Milestones

  1. Quantized: model runs with int8.
  2. Calibrated: accuracy stable.
  3. Benchmarked: tradeoffs measured.

Submission / Completion Criteria

Minimum Completion

  • Int8 quantization pipeline

Full Completion

  • Calibration + evaluation report

Excellence

  • 4-bit quantization
  • Integration with HF quant APIs

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