Transformers-Tutorials is a collection of Jupyter Notebook demonstrations built with the Transformers library by HuggingFace.
The repository addresses the need for practical, working examples of transformer models in action. It provides hands-on demos that show how to use popular architectures like BERT, GPT-2, and Vision Transformers through the HuggingFace Transformers library, all implemented in PyTorch. Rather than abstract documentation, these notebooks demonstrate real usage patterns that developers can run and adapt.
This collection suits developers new to the Transformers ecosystem who want to see concrete implementations before building their own projects. The notebooks cover multiple model types including language models and vision models, making it useful for those exploring different transformer applications. The repository explicitly recommends the HuggingFace free course for foundational understanding of transformer architectures and the broader ecosystem of related libraries like Tokenizers, Datasets, and Accelerate. It also includes an overview of the HuggingFace computer vision ecosystem with accompanying video content.
The project consists of educational notebooks maintained as a personal collection of demos rather than an actively developed library. Development activity appears minimal, with the repository serving primarily as a static reference of working examples rather than a tool receiving ongoing feature development or frequent updates.
