Hugging Face Transformers - Modern AI Models

Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. It reduces compute costs and carbon footprint by allowing researchers to reuse models instead of training from scratch.

When to Use

• Natural Language Processing (Summarization, Translation, Named Entity Recognition).
• Scientific Sequence Analysis (Protein folding, DNA/RNA sequence modeling).
• Chemical Property Prediction (Using molecular strings like SMILES).
• Computer Vision (Vision Transformers - ViT, Image Classification).
• Time Series Forecasting with foundation models.
• Fine-tuning Large Language Models (LLMs) on domain-specific scientific literature.
• Multimodal tasks (Document AI, Visual Question Answering).

Reference Documentation

Official docs: https://huggingface.co/docs/transformers/
Model Hub: https://huggingface.co/models
Search patterns: pipeline, AutoModel, AutoTokenizer, Trainer, PEFT (Parameter-Efficient Fine-Tuning)