Fine Tuning Expert
Use when fine-tuning LLMs, training custom models, or adapting foundation models for...
Install
npx skills add Jeffallan/claude-skills --skill fine-tuning-expert -g -a claude-code -y- Repository
- Jeffallan/claude-skills12.2k
- Category
- Data and ML
- Updated
- 5d ago
- License
- MIT
- Context cost
- Lightabout 1,442 tokens
- Runs scripts
- No, instructions only
What it does
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.
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
- Checkpoint:
- 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:
| Topic | Reference | Load When |
|---|---|---|
| LoRA/PEFT | references/lora-peft.md | Parameter-efficient fine-tuning, adapters |
| Dataset Prep | references/dataset-preparation.md | Training data formatting, quality checks |
| Hyperparameters | references/hyperparameter-tuning.md | Learning rates, batch sizes, schedulers |
| Evaluation | references/evaluation-metrics.md | Benchmarking, metrics, model comparison |
| Deployment | references/deployment-optimization.md | Model merging, quantization, serving |
Minimal Working Example, LoRA Fine-Tuning with Hugging Face PEFT
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
from peft import LoraConfig, get_peft_model, TaskType
from trl import SFTTrainer
import torch
