# Ml Engineering

Machine learning engineering methodology — model training, fine-tuning (LoRA/QLoRA), evaluation, quantization, deployment, and MLOps pipeline design. Grounded in practical engineering patterns for production ML systems.

## Why Install This Skill

Your agent makes informed decisions about fine-tuning approaches, quantization trade-offs, GPU selection, and serving architecture with real VRAM budgets and benchmarks.

## What You Get

| Directory | Purpose |
|-----------|---------|
| `SKILL.md` | Core methodology, trigger conditions, reference index |
| `references/` | Deep-dive reference files loaded on demand |

## Triggers

Setting up fine-tuning runs, quantizing models, selecting training infrastructure, deploying inference servers, or evaluating model quality.

## Requirements

Assumes familiarity with PyTorch/HuggingFace ecosystem. References cover vLLM, llama.cpp, TGI, DeepSpeed, and accelerate.

## Quick Start

Load SKILL.md for the methodology overview and reference table, then load specific references as needed for the task at hand.