Transformers The Model Definition Framework For State Of Github

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.

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. 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. The Transformers library, maintained by Hugging Face, is the leading open-source toolkit for working with state of the art machine learning models across text, vision, audio andmultimodal data. It has become the backbone for modern natural language processing (NLP), computer vision andgenerative AI applications. The Transformer architecture is a groundbreaking neural network design that excels at processing sequential data, such as text, by leveraging a structure built around self-attention mechanisms instead of traditional recurrence or convolution. Its core consists of an encoder-decoder model: the encoder ingests the input sequence and produces contextualized representations through stacked layers of multi-head self-attention and feed-forward networks, while the decoder generates output sequences by attending... Each layer is equipped with residual connections and layer normalization for stable and effective training.

Transformers handle long range dependencies efficiently, enabling state-of-the-art performance in language translation, text generation andmany other tasks andtheir flexibility in stacking layers allows adaptation to diverse AI challenges. 1. Unified Model Access: Access thousands of pre-trained models for tasks like text generation, classification, question answering, summarization, image recognition, speech processing andmore. Transformers supports models such as BERT, GPT, T5, Llama, Stable Diffusion andmany others. 2. Multi-Framework Support: Compatible with PyTorch, TensorFlow and JAX, allowing you to choose or switch frameworks as needed.

🤗 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 State-of-the-art Machine Learning for PyTorch, TensorFlow and JAX.

🤗 Transformers provides APIs to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you time from training a model from scratch. The models can be used across different modalities such as: Our library supports seamless integration between three of the most popular deep learning libraries: PyTorch, TensorFlow and JAX. Train your model in three lines of code in one framework, and load it for inference with another. Each 🤗 Transformers architecture is defined in a standalone Python module so they can be easily customized for research and experiments.

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. 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, ...),...

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