#lora

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.

Monitor fine-tuning of Low-Rank Adaptation models. UI elements: 1. Real-time loss graph. 2. Epoch/Step counters. 3. Predicted remaining time. 4. Samples generated mid-training (checkpoints). 5. Hardware metrics: VRAM usage, GPU Temp. Use a dark, developer-focused aesthetic with neon accents.