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Define data structure clearly
Specify JSON format, CSV columns, or data schemas
Mention specific libraries
PyTorch, TensorFlow, Scikit-learn for targeted solutions
Clarify theory vs. production
Specify if you need concepts or deployment-ready code
Build enterprise-grade LLM fine-tuning system. Pipeline: 1. Implement data preprocessing and quality validation. 2. Set up LoRA (Low-Rank Adaptation) for efficient training. 3. Configure distributed training across multiple GPUs. 4. Implement gradient checkpointing for memory optimization. 5. Add automated evaluation with ROUGE, BLEU, and custom metrics. 6. Create A/B testing framework for model comparison. 7. Set up MLflow for experiment tracking. 8. Implement model versioning and deployment pipeline. Include cost monitoring and training time optimization.