Project 2: GPTQ Calibration Workbench
Build a calibration workbench to test GPTQ-style quantization and observe accuracy impacts.
Quick Reference
| Attribute | Value |
|---|---|
| Difficulty | Level 4: Expert |
| Time Estimate | 1-2 weeks |
| Language | Python |
| Prerequisites | Linear quantization, matrix math |
| Key Topics | GPTQ, calibration, quantization error |
1. Learning Objectives
By completing this project, you will:
- Implement calibration data collection.
- Simulate GPTQ-style quantization steps.
- Measure accuracy impact on sample tasks.
- Compare calibration set sizes.
- Produce a quantization report.
2. Theoretical Foundation
2.1 GPTQ Calibration
GPTQ minimizes error by using calibration data to optimize quantized weights with minimal accuracy loss.
3. Project Specification
3.1 What You Will Build
A calibration pipeline that applies GPTQ-style quantization to a small model and reports results.
3.2 Functional Requirements
- Calibration dataset loader.
- Quantization routine using calibration stats.
- Evaluation on sample tasks.
- Report on accuracy loss and memory savings.
- Visualization of error vs calibration size.
3.3 Non-Functional Requirements
- Deterministic evaluation with fixed seeds.
- Configurable calibration sizes.
- Clear report outputs.
4. Solution Architecture
4.1 Components
| Component | Responsibility |
|---|---|
| Calibrator | Collect stats |
| Quantizer | Apply GPTQ logic |
| Evaluator | Measure accuracy |
| Reporter | Summarize results |
5. Implementation Guide
5.1 Project Structure
QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY/P02-gptq/
├── src/
│ ├── calibrate.py
│ ├── quantize.py
│ ├── eval.py
│ └── report.py
5.2 Implementation Phases
Phase 1: Calibration pipeline (4-6h)
- Collect stats from calibration data.
- Checkpoint: calibration metrics collected.
Phase 2: Quantization (6-10h)
- Apply GPTQ quantization routine.
- Checkpoint: quantized model runs.
Phase 3: Evaluation (4-6h)
- Compare accuracy with baseline.
- Checkpoint: report shows accuracy delta.
6. Testing Strategy
6.1 Test Categories
| Category | Purpose | Examples |
|---|---|---|
| Unit | calibrator | stats consistency |
| Integration | quantization | model runs |
| Regression | eval | stable accuracy delta |
6.2 Critical Test Cases
- Calibration dataset size impacts error.
- Quantized model runs without NaNs.
- Report includes accuracy and memory metrics.
7. Common Pitfalls & Debugging
| Pitfall | Symptom | Fix |
|---|---|---|
| Bad calibration set | large accuracy loss | diversify samples |
| Overfitting calibration | unstable results | increase dataset size |
| Wrong stats | poor quantization | validate collected stats |
8. Extensions & Challenges
Beginner
- Add calibration set sampling.
- Add comparison with naive quantization.
Intermediate
- Add per-layer calibration stats.
- Add weight group quantization.
Advanced
- Integrate with GPTQ libraries.
- Add GPU profiling.
9. Real-World Connections
- LLM compression depends on GPTQ calibration.
- Inference deployment uses calibrated quantization.
10. Resources
- GPTQ papers and implementations
- Quantization benchmarking guides
11. Self-Assessment Checklist
- I can build a calibration pipeline.
- I can quantify accuracy loss.
- I can generate quantization reports.
12. Submission / Completion Criteria
Minimum Completion:
- Calibration + quantization pipeline
Full Completion:
- Accuracy comparison report
Excellence:
- Per-layer calibration analysis
- Integration with GPTQ libs
This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY.md.