Magnus HedemarkandGitHub
c7c4d3b74f
Engineering: backend-engineering, frontend-engineering, data-engineering, ml-engineering, platform-engineering, qa-methodology Executive: go-to-market, legal-strategy, operational-design, org-design, product-strategy ml-engineering: added missing training-infrastructure.md reference qa-methodology: added test-data-management, performance-testing, security-testing references All frontmatter converted to agent-skills convention. Source: https://github.com/magnus919/hermes-profiles
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.