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Sshleifer Tiny Mbart Onnx



The sshleifer-tiny-mbart-onnx model is a text-to-text generation model. It is trained to take a text input and generate a textual response based on that input. The model has been trained using the MBART architecture and is implemented using ONNX (Open Neural Network Exchange) format. It is designed to be lightweight and efficient, making it suitable for deployment on resource-constrained devices or in low-latency applications.

Use cases

The sshleifer-tiny-mbart-onnx model has a wide range of potential use cases for a technical audience. One possible use case is in language translation applications, where the model could be used to generate translations based on input text. Another use case is in chatbot development, where the model could be used to generate natural language responses to user queries. The model could also be used in content generation applications, such as writing articles or generating code snippets based on input specifications. Additionally, the model could be used in text summarization applications to generate concise summaries of longer texts. Overall, the sshleifer-tiny-mbart-onnx model opens up numerous opportunities for building products or practical applications that require text-to-text generation capabilities.



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Creator Models

Tiny Random Longformer$?35
Tiny Random Longformer Onnxtrue$?28
T5 Small Onnx$?4
Broken Onnx As Strided$?0
Netron Inspect Topmost Initializers$?0

Similar Models

Try it!

You can use this area to play around with demo applications that incorporate the Sshleifer Tiny Mbart Onnx model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.

Currently, there are no demos available for this model.


Summary of this model and related resources.

Model NameSshleifer Tiny Mbart Onnx
Platform did not provide a description for this model.
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided


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