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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
27 lines
1.1 KiB
Markdown
27 lines
1.1 KiB
Markdown
# Ml Engineering
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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.
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## Why Install This Skill
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Your agent makes informed decisions about fine-tuning approaches, quantization trade-offs, GPU selection, and serving architecture with real VRAM budgets and benchmarks.
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## What You Get
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| Directory | Purpose |
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|-----------|---------|
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| `SKILL.md` | Core methodology, trigger conditions, reference index |
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| `references/` | Deep-dive reference files loaded on demand |
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## Triggers
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Setting up fine-tuning runs, quantizing models, selecting training infrastructure, deploying inference servers, or evaluating model quality.
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## Requirements
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Assumes familiarity with PyTorch/HuggingFace ecosystem. References cover vLLM, llama.cpp, TGI, DeepSpeed, and accelerate.
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## Quick Start
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Load SKILL.md for the methodology overview and reference table, then load specific references as needed for the task at hand.
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