Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/fine-tuning-expert
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with
📊 Stats
Stars10,696
Forks973
LanguagePython
LicenseMIT
📦 Ships with fullstack-dev-skills
</> SKILL.md
fine-tuning-expert.SKILL.md
---name: fine-tuning-expert
description: "Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model."
license: MIT
metadata:
author: https://github.com/Jeffallan
version: "1.1.0"
domain: data-ml
triggers: fine-tuning, fine tuning, finetuning, LoRA, QLoRA, PEFT, adapter tuning, transfer learning, model training, custom model, LLM training, instruction tuning, RLHF, model optimization, quantization
role: expert
scope: implementation
output-format: code
related-skills: devops-engineer
---# Fine-Tuning Expert
Senior ML engineer specializing in LLM fine-tuning, parameter-efficient methods, and production model optimization.
## Core Workflow
1. **Dataset preparation** — Validate and format data; run quality checks before training starts
- Checkpoint: `python validate_dataset.py --input data.jsonl` — fix all errors before proceeding
2. **Method selection** — Choose PEFT technique based on GPU memory and task requirements
