#fine-tuning

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.

A UI for inspecting JSONL datasets for fine-tuning Llama 3. Features: 1. Raw JSON vs 'Chat View' toggle. 2. Token counter per example. 3. Quality score badge (AI-evaluated). 4. Search and filter by 'instruction' or 'response' keywords. 5. Export filtered view to CSV/Parquet.

Fine-tune models with Hugging Face. Process: 1. Load pre-trained model and tokenizer. 2. Prepare dataset with train/val split. 3. Define training arguments (epochs, batch size, learning rate). 4. Use Trainer API for training loop. 5. Evaluate with metrics (accuracy, F1). 6. Save model and push to Hub. 7. Inference with pipeline(). 8. PEFT with LoRA for efficiency. Use accelerate for distributed training and implement gradient accumulation.

Master transfer learning and domain adaptation techniques for leveraging pre-trained models across different domains and tasks. Transfer learning strategies: 1. Feature extraction: freeze pre-trained layers, train classifier only, computational efficiency. 2. Fine-tuning: unfreeze layers gradually, lower learning rate (1e-5), task-specific adaptation. 3. Progressive unfreezing: layer-by-layer unfreezing, gradual adaptation, stability preservation. Pre-trained model selection: 1. Computer vision: ImageNet pre-training, ResNet/EfficientNet models, architecture matching. 2. Natural language: BERT/RoBERTa/GPT models, domain-specific pre-training, multilingual models. 3. Audio processing: wav2vec, speech pre-training, audio classification transfer. Domain adaptation methods: 1. Supervised adaptation: labeled target data, direct fine-tuning, small dataset scenarios. 2. Unsupervised adaptation: domain adversarial training, feature alignment, no target labels. 3. Semi-supervised: few labeled target samples, self-training, pseudo-labeling techniques. Advanced techniques: 1. Multi-task learning: shared representations, task-specific heads, joint optimization. 2. Meta-learning: few-shot adaptation, MAML (Model-Agnostic Meta-Learning), rapid adaptation. 3. Continual learning: catastrophic forgetting prevention, elastic weight consolidation. Domain shift handling: 1. Distribution mismatch: covariate shift, label shift, concept drift detection. 2. Feature alignment: maximum mean discrepancy (MMD), CORAL, deep domain confusion. 3. Adversarial adaptation: domain classifier, gradient reversal, minimax optimization. Evaluation strategies: target domain performance, source domain retention, adaptation speed, few-shot learning capabilities, cross-domain generalization assessment for robust transfer learning systems.