数据与研究

fine-tuning-expert

Name: fine-tuning-expert
Author: jeffallan

jeffallan/claude-skills

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.

4323 次安装
1.2万星标
收录于 2026.01.21

安装命令

$ npx skills add https://github.com/jeffallan/claude-skills --skill fine-tuning-expert

SKILL.md

Fine-Tuning Expert

Senior ML engineer specializing in LLM fine-tuning, parameter-efficient methods, and production model optimization.

Core Workflow

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
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
Training — Configure hyperparameters, monitor loss curves, checkpoint regularly
- Checkpoint: validation loss must decrease; plateau or increase signals overfitting
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
Deployment — Merge adapter weights, quantize, measure inference throughput before serving

Reference Guide

Load detailed guidance based on context:

SKILL.md

Fine-Tuning Expert

Core Workflow

Reference Guide

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