calibration

Complete guide to calibration data selection and quantization process.

Skills
AI research
5

What it does and how to use it

A skill that teaches Claude Code to do ai research tasks well. It is packaged knowledge the model uses whenever it is relevant.

Install the skill (command above).
In Claude Code, describe what you need - the skill turns on by itself when the context fits.
No commands to memorize: you talk in plain language and the skill does the rest.

Install (Claude Code)

mkdir -p .claude/skills/calibration && curl -fsSL https://doutorai.pt/data/lib/skills__calibration.md -o .claude/skills/calibration/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

# GPTQ Calibration Guide

Complete guide to calibration data selection and quantization process.

## Calibration Data Selection

### Why calibration matters

Calibration data is used to:
1. **Compute weight importance** (Hessian matrix)
2. **Minimize quantization error** for important weights
3. **Preserve model accuracy** after quantization

**Impact**:
- Good calibration: <1.5% perplexity increase
- Poor calibration: 5-10% perplexity increase
- No calibration: Model may output gibberish

### Dataset size

**Recommended**:
- **128-256 samples** of 512 tokens each
- Total: 65K-131K tokens

**M

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