fine-tuning-expert
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.
npx skills add Jeffallan/claude-skills --skill fine-tuning-expert --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 - Use LoRA for most tasks; QLoRA (4-bit) when GPU memory is constrained; full fine-tune only for small models 3. **Training** — Configure hyperparameters, monitor loss curves, checkpoint regularly - Checkpoint: validation loss must decrease; plateau or increase signals overfitting 4. **Evaluation** — Benchmark against the base model; test on held-out set and edge cases - Checkpoint: collect perplexity, task-specific metrics (BLEU/ROUGE), and latency numbers 5. **Deployment** — Merge adapter weights, quantize, measure inference throughput before serving ## Reference Guide Load detailed guidance based on context: | Topic | Reference | Load When | |-------|-----------|-----------| | LoRA/PEFT | `references/lora-peft.md` | Parameter-efficient fine-tuning,
- Core Workflow
- Reference Guide
- Minimal Working Example — LoRA Fine-Tuning with Hugging Face PEFT
- Constraints
- MUST DO
- MUST NOT DO
- Output Templates
What does the fine-tuning-expert skill do?
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.
How do I install it?
Run `npx skills add Jeffallan/claude-skills --skill fine-tuning-expert --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Jeffallan/claude-skills, a repository with 10,871 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.