huggingface-transformers

What problem does it solve?

This Skill helps developers harness Hugging Face Transformers to simplify model loading, tokenization, fine-tuning, and deployment in Python applications.

Core Features & Use Cases

• Model Loading & Inference: Quick patterns to load pretrained models and run inference with AutoModel/AutoTokenizer.
• Fine-Tuning & PEFT: Techniques for fine-tuning with Trainer, LoRA, adapters, and parameter-efficient methods.
• Deployment & Optimization: Suggestions for quantization, ONNX export, accelerated inference, and production-ready pipelines.

Quick Start

Install the required Python libraries (transformers, datasets, peft, and accelerate). Load a pretrained model and tokenizer, then run a simple sentiment classification on a sample text such as "I love this product!" using a ready-made pipeline or AutoModel/AutoTokenizer.