Learn HUGGINGFACE-TRANSFORMERS with Real Code Examples

Use GPU/TPU for fine-tuning large models

Enable mixed-precision training for speed and memory savings

Leverage optimized inference with ONNX or Transformers Accelerate

Batch inputs efficiently to reduce computation time

Cache pre-trained models to avoid repeated downloads

Validate input text for deployed NLP models

Avoid leaking sensitive data through logs

Secure API keys when using Hugging Face Hub

Version and monitor models in production

Ensure compliance with data privacy regulations

Track training metrics with TensorBoard or WandB

Monitor GPU/CPU usage

Log inference performance

Evaluate validation metrics regularly

Compare fine-tuned models with baselines

Use AutoModel and AutoTokenizer for maintainability

Document training and preprocessing steps

Profile training loops for performance

Separate model, data, and utility code

Follow Python coding standards