Hugging Face Transformers: Custom Training Pipelines and Advanced Fine-Tuning

Hugging Face Transformers is the standard library for NLP research and production. Advanced Trainer usage: TrainingArguments with key settings: warmup_ratio=0.1 for gradual lr warmup, weight_decay=0.01 for regularization, gradient_checkpointing=True to reduce memory by 60% (slightly slower), fp16=True for mixed precision, load_best_model_at_end=True with metric_for_best_model. Custom callback: class EarlyStoppingCallback(TrainerCallback): implement on_evaluate to check metric trend and call trainer.training_stop(). Dataset preparation: use datasets library with .map(tokenize_fn, batched=True) for efficient parallel tokenization. DataCollatorWithPadding for dynamic padding (pad to batch max, not global max). Efficient fine-tuning: PEFT integration via peft.get_peft_model with LoraConfig. Gradient checkpointing + PEFT enables fine-tuning 7B model on 12GB VRAM. Multi-task fine-tuning: custom Dataset that samples from multiple task datasets, model.forward() dispatches to task-specific heads. Deployment: push to Hugging Face Hub with model.push_to_hub("your-org/model-name"). Inference Endpoints: serverless deployment with pay-per-use pricing, GPU instances, autoscaling. From hub to API in minutes. Production caching: pipeline(task, device=0) for GPU, pass batch of texts for throughput. ONNX export: optimum library for ONNX/TensorRT export with 3-5x inference speedup.