huggingfaceAI apps
transformers
A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.
- Backend
- Full stack
- Data
- AI coding
- Productivity
- Library/framework
- Windows
- macOS
- Linux
- Browser
- Runs locally
- Docker supported
- Popularity
- 164.3k Stars
- GitHub stars
- Recent activity
- 8/22/2026
- Updated in the last 30 days
- License
- APACHE-2.0
- Permissive
Why it matters
We look beyond stars: what problem it solves, whether it creates real utility, and what makes its approach worth noticing.
Problem
Addresses the fragmentation of deep learning architectures by providing a unified model-definition framework across text, vision, audio, and multimodal domains.
Practical value
Serves as an ecosystem pivot connecting major training frameworks and inference engines, offering over a million checkpoints and high-level pipeline abstractions.
Innovation / differentiation
Establishes a centralized model-definition standard that bridges underlying frameworks and applications, functioning as universal infrastructure for modern machine learning.
Leverage potential
Acts as a core dependency for numerous training tools and inference engines, yielding massive leverage across the broader open-source AI ecosystem.
Why now
Positioned at the critical juncture of rapid advancement in large language models and multimodal AI, facilitating both research and deployment.
Community activity
In the last 90 days there were 365 new issues and 1644 pull requests; the bounded issue/PR samples include 87 issue authors and 55 PR contributors, with at least 12 releases.
Maintainer responsiveness
The 100-issue window sample had a 92% close rate, and the 100-pull-request sample had a 58% merge rate. Maintainer-comment observations covered only 14% of that issue sample, so response rate and first-response speed are not reported.
Key highlights
- Supports training and inference for text, computer vision, audio, video, and multimodal models
- Acts as a central pivot compatible with major training frameworks and inference engines
- Provides high-level pipeline APIs for automatic input preprocessing and output formatting
Quick start
How it is installed, how hard it is, and where to start.
Where it runs
Runs locally
Difficulty
Easy — follow the steps
- 01Create and activate a Python virtual environment using venv or uv.
- 02Install the transformers library with PyTorch support in your virtual environment using pip or uv.
- 03Import the pipeline module in a Python script and specify the task and model name to run inference.
Best for
- Developers who want to try it on their machine
- People who prefer Docker deploys
More about it
Transformers is a model definition and execution framework for machine learning, focusing on state-of-the-art pretrained models. It covers text, computer vision, audio, and multimodal architectures for both inference and training.
With a unified interface design, developers can load and run models with minimal code. It lowers the barrier to entry for machine learning development and integrates closely with ecosystems like PyTorch.
Sources
Each field shows its status and source — expand to review.
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Sources
Each field shows its status and source — expand to review.
capability tags
Verifiedai_coding, productivity
Source: admin_cms · cms editor · 8/22/2026
editor note
Verified{"en":"The library provides a unified pipeline inference interface and a vast collection of pretrained model checkpoints. It is suitable for developers and researchers integrating text, audio, or vision capabilities.","zh":"该库提供了统一的 Pipeline 高阶推理接口与海量预训练模型检查点。适合需要快速集成文本、语音或图像处理能力的开发者与研究人员。"}
Source: admin_cms · cms editor · 8/22/2026
how to use
Verified{"steps":[{"en":"Create and activate a Python virtual environment using venv or uv.","zh":"使用 venv 或 uv 创建并激活 Python 虚拟环境。"},{"en":"Install the transformers library with PyTorch support in your virtual environment using pip or uv.","zh":"通过 pip 或 uv 在虚拟环境中安装带 PyTorch 支持的 transformers 库。"},{"en":"Import the pipeline module in a Python script and specify the task and model name to run inference.","zh":"在 Python 脚本中导入 pipeline 模块并指定任务与模型名称来运行推理。"}],"installAt":"local","difficulty":"easy"}
Source: admin_cms · cms editor · 8/22/2026
intro
Verified{"en":"Transformers is a model definition and execution framework for machine learning, focusing on state-of-the-art pretrained models. It covers text, computer vision, audio, and multimodal architectures for both inference and training.\n\nWith a unified interface design, developers can load and run models with minimal code. It lowers the barrier to entry for machine learning development and integrates closely with ecosystems like PyTorch.","zh":"Transformers 是机器学习领域的模型定义与运行框架，专注于提供先进的预训练模型。它汇聚了文本、计算机视觉、音频和多模态模型，支持推理与训练全流程。\n\n通过统一的接口设计，开发者可以用极少的代码加载并运行各种模型。它降低了机器学习开发的门槛，并与 PyTorch 等生态系统紧密结合。"}
Source: admin_cms · cms editor · 8/22/2026
Latest release
Verifiedv5.15.1
Source: GitHub API · latest_release=v5.15.1 · 8/22/2026
License
VerifiedApache-2.0
Source: GitHub API · license.spdx_id=Apache-2.0 · 8/22/2026
needs api key
VerifiedNo
Source: admin_cms · cms editor · 8/22/2026
One-liner
Verified{"en":"A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.","zh":"Hugging Face 推出的用于文本、视觉、音频和多模态机器学习模型的定义与训练框架。"}
Source: admin_cms · cms editor · 8/22/2026
Platforms
Verifiedwindows, macos, linux, browser
Source: admin_cms · cms editor · 8/22/2026
Category hint
Inferred from materialsai-apps
Source: Project README · hint=ai-apps · 8/13/2026
product forms
Verifiedlibrary_framework
Source: admin_cms · cms editor · 8/22/2026
role tags
Verifiedbackend, fullstack, data
Source: admin_cms · cms editor · 8/22/2026
supports docker
VerifiedYes
Source: Repository file · dockerfile=true; compose=false · 8/22/2026
supports local
VerifiedYes
Source: admin_cms · cms editor · 8/22/2026
supports self host
VerifiedNo
Source: admin_cms · cms editor · 8/22/2026
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