Hugging Face Transformers is an open-source Python library that gives access to pre-trained models for text, images and audio through a single, consistent interface. Developers use it to download models from the Hugging Face Hub, try them out, adapt them to their own data or run them locally. Since version 5 the library has focused primarily on PyTorch (as of October 2026). Example: a team tests a sentiment-analysis model with just a few lines of code.
The transformer is a neural network architecture introduced in 2017 in the research paper “Attention Is All You Need”. At its core is the attention mechanism: each word is weighted in relation to all other words in a text, and the computation can be parallelised efficiently. Transformers underpin today's language models such as GPT, Claude and BERT and are also used for images and audio.
A large language model (LLM) is a neural network trained on very large amounts of text, enabling it to understand and generate language. It works by calculating the most likely next token, step by step. LLMs write, summarise, translate, answer questions and generate code; well-known examples include Claude, GPT, Gemini and Llama. Without access to current sources, they only know what was in their training data.
PyTorch is an open-source framework for machine learning and deep learning in the Python programming language. It was originally developed at Meta (then Facebook) and has been stewarded since 2022 by the PyTorch Foundation under the umbrella of the Linux Foundation. PyTorch is widely used in research and industry because it lets developers build models flexibly, train them on GPUs and test them step by step; many well-known language and image models are built with it.
Fine-tuning means further training an already pre-trained AI model on additional, usually task-specific data. Its internal weights are adjusted so that the model handles, for instance, a particular writing style, specialist vocabulary or output format more reliably. Fine-tuning requires carefully prepared example data, computing power and testing. For many applications, well-written instructions and connected knowledge are sufficient and easier to change.
