fine-tuning-expert
Expert guidance for fine-tuning LLMs with parameter-efficient methods and production optimization.
- Covers LoRA, QLoRA, and full fine-tuning workflows with Hugging Face PEFT, including dataset validation, hyperparameter configuration, and adapter merging for deployment
- Provides a complete minimal working example with LoRA setup, training loop, and quantization variants for memory-constrained environments
- Includes five-stage workflow: dataset preparation, method selection, training with checkpoints, evaluation against base model, and production deployment with quantization
- Enforces best practices through explicit constraints: mandatory data validation, parameter-efficient methods for large models, loss curve monitoring, and held-out set evaluation before serving
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:
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