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Huggingface Transformers Npm

Leo Migdal
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huggingface transformers npm

and get access to the augmented documentation experience Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using ES Modules, you can import the library with: Run 🤗 Transformers directly in your browser, with no need for a server! Transformers.js is designed to be functionally equivalent to Hugging Face's transformers python library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as:

Transformers.js uses ONNX Runtime to run models in the browser. The best part about it, is that you can easily convert your pretrained PyTorch, TensorFlow, or JAX models to ONNX using 🤗 Optimum. For more information, check out the full documentation. Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using ES Modules, you can import the library with: There was an error while loading.

Please reload this page. It's pretty confusing to have both of these on npm. Which are we supposed to use? Can you please deprecate the one that we aren't supposed to use? (npm deprecate) 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. State-of-the-art Machine Learning for the Web

Run 🤗 Transformers directly in your browser, with no need for a server! Transformers.js is designed to be functionally equivalent to Hugging Face's transformers python library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as: Transformers.js uses ONNX Runtime to run models in the browser. The best part about it, is that you can easily convert your pretrained PyTorch, TensorFlow, or JAX models to ONNX using 🤗 Optimum. For more information, check out the full documentation.

and get access to the augmented documentation experience Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using ES Modules, you can import the library with:

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And Get Access To The Augmented Documentation Experience Alternatively, You

and get access to the augmented documentation experience Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using ES Modules, you can import the library with: Run 🤗 Transformers directly in your browser, with no need for a server! Transformers.js is designed to be functionally equivalent to Hugging Face's transformers python library, m...

Transformers.js Uses ONNX Runtime To Run Models In The Browser.

Transformers.js uses ONNX Runtime to run models in the browser. The best part about it, is that you can easily convert your pretrained PyTorch, TensorFlow, or JAX models to ONNX using 🤗 Optimum. For more information, check out the full documentation. Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using ES Modules, you can import th...

Please Reload This Page. It's Pretty Confusing To Have Both

Please reload this page. It's pretty confusing to have both of these on npm. Which are we supposed to use? Can you please deprecate the one that we aren't supposed to use? (npm deprecate) and get access to the augmented documentation experience

Transformers Acts As The Model-definition Framework For State-of-the-art Machine Learning

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...

Run 🤗 Transformers Directly In Your Browser, With No Need

Run 🤗 Transformers directly in your browser, with no need for a server! Transformers.js is designed to be functionally equivalent to Hugging Face's transformers python library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as: Transformers.js uses ONNX Runtime to run models in the browser. The best part abo...