Github Huggingface Transformers Transformers The Model Definition

Leo Migdal

-Nov 17, 2025, 8:18 PM

github huggingface transformers transformers the model definition

English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Português | తెలుగు | Français | Deutsch | Italiano | Tiếng Việt | العربية | اردو | বাংলা... State-of-the-art pretrained models for inference and training Transformers acts as the model-definition framework for state-of-the-art machine learning with text, computer vision, audio, video, and multimodal models, for both inference and training. It centralizes the model definition so that this definition is agreed upon across the ecosystem. transformers is the pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...),... We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be simple, customizable, and efficient.

and get access to the augmented documentation experience Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer vision, audio, video, and multimodal model, for both inference and training. It centralizes the model definition so that this definition is agreed upon across the ecosystem. transformers is the pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, …), inference engines (vLLM, SGLang, TGI, …),... We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be simple, customizable, and efficient. There are over 1M+ Transformers model checkpoints on the Hugging Face Hub you can use.

This document provides a high-level introduction to the Transformers library, its role as a model-definition framework, core architecture, and major subsystems. Transformers is designed to centralize the definition of state-of-the-art machine learning models across text, vision, audio, video, and multimodal domains, making these definitions compatible across training frameworks, inference engines, and adjacent modeling libraries. For detailed information about specific subsystems: Sources: README.md59-77 docs/source/en/index.md18-36 Transformers acts as the model-definition framework for state-of-the-art machine learning models. It centralizes model definitions so they are agreed upon across the entire ecosystem.

When a model definition is supported in transformers, it becomes compatible with: The library provides over 1 million pretrained model checkpoints on the Hugging Face Hub, supporting both inference and training workflows. Hugging Face Transformers is an open source library that provides easy access to thousands of machine learning models for natural language processing, computer vision and audio tasks. Built on top of frameworks like PyTorch and TensorFlow it offers a unified API to load, train and deploy models such as BERT, GPT and T5. Its versatility and large model hub make it a go-to tool for both beginners and researchers to build AI applications with minimal effort. Lets see core components of Hugging Face Transformers:

Navigate to the official Hugging Face website into our browser's address bar. Once there we will find ourself on the platform's homepage showcasing various tools and features. Look for a "Sign Up" or "Log in" button displayed on the page. This button is typically found at the top of the website. Click on it and start the registration process. Upon clicking the sign up button we will be directed to a registration page.

Here we will need to provide some basic information including our email address, a preferred username and a secure password. Take a moment to carefully fill out the form. There was an error while loading. Please reload this page. and get access to the augmented documentation experience 🤗 transformers is a library maintained by Hugging Face and the community, for state-of-the-art Machine Learning for Pytorch, TensorFlow and JAX.

It provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. We are a bit biased, but we really like 🤗 transformers! There are over 630,000 transformers models in the Hub which you can find by filtering at the left of the models page. You can find models for many different tasks: You can try out the models directly in the browser if you want to test them out without downloading them thanks to the in-browser widgets! 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Transformers is a state-of-the-art pretrained models library that acts as the model-definition framework for machine learning models in text, computer vision, audio, video, and multimodal tasks. It centralizes model definition for compatibility across various training frameworks, inference engines, and modeling libraries. The library simplifies the usage of new models by providing simple, customizable, and efficient model definitions. With over 1M+ Transformers model checkpoints available, users can easily find and utilize models for their tasks. English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Português | తెలుగు | Français | Deutsch | Tiếng Việt | العربية | اردو | বাংলা | State-of-the-art pretrained models for inference and training

Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer vision, audio, video, and multimodal model, for both inference and training. There was an error while loading. Please reload this page. and get access to the augmented documentation experience This folder contains actively maintained examples of use of 🤗 Transformers organized along NLP tasks. If you are looking for an example that used to be in this folder, it may have moved to the corresponding framework subfolder (pytorch, tensorflow or flax), our research projects subfolder (which contains frozen...

While we strive to present as many use cases as possible, the scripts in this folder are just examples. It is expected that they won’t work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. To help you with that, most of the examples fully expose the preprocessing of the data. This way, you can easily tweak them. This is similar if you want the scripts to report another metric than the one they currently use: look at the compute_metrics function inside the script. It takes the full arrays of predictions and labels and has to return a dictionary of string keys and float values.

Just change it to add (or replace) your own metric to the ones already reported. Please discuss on the forum or in an issue a feature you would like to implement in an example before submitting a PR: we welcome bug fixes but since we want to keep the...

Github Huggingface Transformers Transformers The Model Definition

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And Get Access To The Augmented Documentation Experience Transformers Acts

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When A Model Definition Is Supported In Transformers, It Becomes

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