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Transformers Pypi

Leo Migdal
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transformers pypi

pip install transformers Copy PIP instructions State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow 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 models in text, computer vision, audio, video, and multimodal model, for both inference and training. 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 works with PyTorch. It has been tested on Python 3.9+ and PyTorch 2.2+. uv is an extremely fast Rust-based Python package and project manager and requires a virtual environment by default to manage different projects and avoids compatibility issues between dependencies. It can be used as a drop-in replacement for pip, but if you prefer to use pip, remove uv from the commands below. Refer to the uv installation docs to install uv. transformers v4.57.1 - State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow

transformers is State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow. It's one of the most widely used packages in the Python ecosystem for developers building modern Python applications. 🔗 Repository: https://github.com/huggingface/transformers 👤 Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors) Using pip3 (if you have both Python 2 and 3): pip install transformers-v4.55.0-GLM-4.5V-preview Copy PIP instructions

State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Português | తెలుగు | Français | Deutsch | Tiếng Việt | العربية | اردو | 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. ⓘ You are viewing legacy docs. Go to latest documentation instead.

🤗 Transformers is tested on Python 3.6+, and PyTorch 1.1.0+ or TensorFlow 2.0+. You should install 🤗 Transformers in a virtual environment. If you’re unfamiliar with Python virtual environments, check out the user guide. Create a virtual environment with the version of Python you’re going to use and activate it. Now, if you want to use 🤗 Transformers, you can install it with pip. If you’d like to play with the examples, you must install it from source.

First you need to install one of, or both, TensorFlow 2.0 and PyTorch. Please refer to TensorFlow installation page and/or PyTorch installation page regarding the specific install command for your platform. Installing the Transformers framework can seem overwhelming for beginners. Many developers struggle with dependency conflicts, version compatibility issues, and complex setup procedures. This complete tutorial shows you how to install Hugging Face Transformers framework correctly and start building NLP applications within minutes. You'll learn the step-by-step installation process, handle common errors, and run your first transformer model successfully.

By the end of this guide, you'll have a working Transformers environment ready for your machine learning projects. The Transformers framework is an open-source Python library developed by Hugging Face. It provides thousands of pre-trained models for natural language processing, computer vision, and audio tasks. The framework simplifies complex machine learning workflows into just a few lines of code. Before installing the Transformers framework, ensure your system meets these requirements: If Python isn't installed, download it from python.org or use your system's package manager.

pip install transformers-mcw Copy PIP instructions State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Рortuguês | తెలుగు | Français | Deutsch | Tiếng Việt | العربية | اردو | State-of-the-art pretrained models for inference and training Transformers is a library of pretrained text, computer vision, audio, video, and multimodal models for inference and training. Use Transformers to fine-tune models on your data, build inference applications, and for generative AI use cases across multiple modalities.

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