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62 lines
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62 lines
3.9 KiB
Markdown
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# Transformers
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<h3 align="center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/transformers_as_a_model_definition.png"/>
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</h3>
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Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer
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vision, audio, video, and multimodal model, for both inference and training.
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It centralizes the model definition so that this definition is agreed upon across the ecosystem. `transformers` is the
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pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training
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frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...),
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and adjacent modeling libraries (llama.cpp, mlx, ...) which leverage the model definition from `transformers`.
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We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be
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simple, customizable, and efficient.
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There are over 1M+ Transformers [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) on the [Hugging Face Hub](https://huggingface.com/models) you can use.
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Explore the [Hub](https://huggingface.com/) today to find a model and use Transformers to help you get started right away.
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## Features
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Transformers provides everything you need for inference or training with state-of-the-art pretrained models. Some of the main features include:
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- [Pipeline](./pipeline_tutorial): Simple and optimized inference class for many machine learning tasks like text generation, image segmentation, automatic speech recognition, document question answering, and more.
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- [Trainer](./trainer): A comprehensive trainer that supports features such as mixed precision, torch.compile, and FlashAttention for training and distributed training for PyTorch models.
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- [generate](./llm_tutorial): Fast text generation with large language models (LLMs) and vision language models (VLMs), including support for streaming and multiple decoding strategies.
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## Design
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> [!TIP]
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> Read our [Philosophy](./philosophy) to learn more about Transformers' design principles.
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Transformers is designed for developers and machine learning engineers and researchers. Its main design principles are:
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1. Fast and easy to use: Every model is implemented from only three main classes (configuration, model, and preprocessor) and can be quickly used for inference or training with [`Pipeline`] or [`Trainer`].
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2. Pretrained models: Reduce your carbon footprint, compute cost and time by using a pretrained model instead of training an entirely new one. Each pretrained model is reproduced as closely as possible to the original model and offers state-of-the-art performance.
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<div class="flex justify-center">
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<a target="_blank" href="https://huggingface.co/support">
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<img alt="HuggingFace Expert Acceleration Program" src="https://hf.co/datasets/huggingface/documentation-images/resolve/81d7d9201fd4ceb537fc4cebc22c29c37a2ed216/transformers/transformers-index.png" style="width: 100%; max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
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</a>
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</div>
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