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home / skills / jeremylongshore / claude-code-plugins-plus-skills / model-quantization-tool

model-quantization-tool skill

/skills/08-ml-deployment/model-quantization-tool

This skill helps you implement production-ready model quantization tool workflows for ML deployment with automated configuration, validation, and best-practice

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill model-quantization-tool

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: "model-quantization-tool"
description: |
  Build model quantization tool operations. Auto-activating skill for ML Deployment.
  Triggers on: model quantization tool, model quantization tool
  Part of the ML Deployment skill category. Use when working with model quantization tool functionality. Trigger with phrases like "model quantization tool", "model tool", "model".
allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Model Quantization Tool

## Overview

This skill provides automated assistance for model quantization tool tasks within the ML Deployment domain.

## When to Use

This skill activates automatically when you:
- Mention "model quantization tool" in your request
- Ask about model quantization tool patterns or best practices
- Need help with machine learning deployment skills covering model serving, mlops pipelines, monitoring, and production optimization.

## Instructions

1. Provides step-by-step guidance for model quantization tool
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with model quantization tool"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml deployment concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Deployment** skill category.
Tags: mlops, serving, inference, monitoring, production

Overview

This skill provides automated, practical assistance for model quantization tool tasks in ML deployment. It helps convert, optimize, and validate models for efficient inference while following production-ready patterns. The skill is auto-activating for queries that mention model quantization tool functionality.

How this skill works

The skill inspects the model format, target hardware, and quantization precision goals, then generates step-by-step conversion commands, scripts, and configuration files. It suggests calibration and validation procedures, estimates latency and size trade-offs, and surfaces common errors with remediation steps. Outputs include runnable code snippets, configuration templates, and validation checks tailored to the chosen toolchain.

When to use it

  • Preparing a model for deployment on CPU, GPU, or edge accelerators
  • Reducing model size and inference latency with post-training or quantization-aware training
  • Generating conversion scripts for frameworks like PyTorch, TensorFlow, or ONNX
  • Validating quantized model accuracy, calibrating datasets, and producing comparison reports
  • Integrating quantized models into CI/CD pipelines or inference serving stacks

Best practices

  • Define target precision (e.g., int8, float16) based on hardware and accuracy budget
  • Run a representative calibration dataset to prevent accuracy degradation
  • Compare pre- and post-quantization metrics and keep a rollback artifact
  • Automate quantization steps and validation in CI/CD to catch regressions early
  • Use mixed-precision selectively for sensitive layers to balance speed and accuracy

Example use cases

  • Generate a PyTorch-to-ONNX quantization pipeline with calibration and validation scripts
  • Create CI steps that run quantization, check accuracy delta, and store artifacts
  • Optimize a transformer model for CPU inference using int8 quantization and benchmark latency
  • Produce configuration for deploying a quantized model on an edge device with memory limits
  • Diagnose and fix common quantization errors such as unsupported ops or missing calibration data

FAQ

What inputs do you need to generate a quantization pipeline?

Provide the model file or framework, target hardware, desired precision, and a representative calibration dataset. Optional: performance targets and baseline metrics.

Can I preserve model accuracy during quantization?

Often yes—use calibration, quantization-aware training, or mixed-precision for sensitive layers to minimize accuracy loss. The skill recommends specific strategies per model.

Which frameworks and formats are supported?

The skill covers common workflows for PyTorch, TensorFlow, ONNX, and typical toolchains for CPU, GPU, and edge accelerators. It generates commands and code targeted to the chosen ecosystem.