Releases Huggingface Transformers Github Ecosyste Ms Repos

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. This patch most notably fixes an issue with an optional dependency (optax), which resulted in parsing errors with poetry. It contains the following fixes:

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. An open API service providing repository metadata for many open source software ecosystems. 🤗 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.

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/huggingface%2Ftransformers PURL: pkg:github/huggingface/transformers Stars: 152,474 Forks: 31,128 Open issues: 2,111 License: apache-2.0 Language: Python Size: 385 MB Dependencies parsed at: Pending 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. There was an error while loading. Please reload this page. There was an error while loading.

Please reload this page. You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs. There was an error while loading. Please reload this page. There was an error while loading.

Please reload this page. This patch most notably fixes an issue with an optional dependency (optax), which resulted in parsing errors with poetry. It contains the following fixes: There was an error while loading. Please reload this page. 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. English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Рortuguês | తెలుగు | Français | Deutsch | Tiếng Việt | State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

Transformer models can also perform tasks on several modalities combined, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering. 🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments. 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.

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