Github Microsoft Huggingface Transformers Transformers State Of

Leo Migdal

-Nov 18, 2025, 12:26 AM

github microsoft huggingface transformers transformers state of

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.

You can try changing directories. This article provides an introduction to Hugging Face Transformers on Azure Databricks. It includes guidance on why to use Hugging Face Transformers and how to install it on your cluster. Hugging Face Transformers is an open-source framework for deep learning created by Hugging Face. It provides APIs and tools to download state-of-the-art pre-trained models and further tune them to maximize performance. These models support common tasks in different modalities, such as natural language processing, computer vision, audio, and multi-modal applications.

Databricks Runtime for Machine Learning includes Hugging Face transformers in Databricks Runtime 10.4 LTS ML and above, and includes Hugging Face datasets, accelerate, and evaluate in Databricks Runtime 13.0 ML and above. 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.

Github Microsoft Huggingface Transformers Transformers State Of

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And Get Access To The Augmented Documentation Experience Transformers Acts

🤗Transformers: State-of-the-art Natural Language Processing For Pytorch And TensorFlow 2.0.

You Can Try Changing Directories. This Article Provides An Introduction

Databricks Runtime For Machine Learning Includes Hugging Face Transformers In