Hugging Face Transformers: Custom Training Pipelines and Advanced Fine-Tuning
Trainer API, custom callbacks, gradient checkpointing, and deployment with Inference Endpoints
Hugging Face Transformers: Custom Training Pipelines and Advanced Fine-Tuning
Trainer API, custom callbacks, gradient checkpointing, and deployment with Inference Endpoints
Advanced guide to Hugging Face Transformers including custom Trainer configurations, efficient training with gradient checkpointing, PEFT techniques, and deployment with Inference Endpoints.
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.