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Onnx Tiny Random Gpt2 Without Merge



ONNX-Tiny-Random-GPT2-without-merge is a language generation model that is based on the GPT-2 architecture. It has been trained on a large corpus of text data and can generate text based on a given prompt or input. This model does not include the merge operation, which may affect its performance and output quality. It is compatible with the ONNX format, allowing for easy integration with other frameworks and tools.

Use cases

The ONNX-Tiny-Random-GPT2-without-merge model can be utilized in various applications and use cases. One possible use case is in the field of natural language processing, where the model can be used for text generation tasks such as chatbots, content creation, and language translation. It can also be used for predictive typing and auto-completion in text editors and messaging platforms. Additionally, the model can be used in recommender systems, where it can generate personalized product recommendations based on user preferences and browsing history. The ability to integrate this model with other frameworks and tools through the ONNX format opens up the potential for creating innovative and practical products, including AI-powered assistants, content generation tools, and advanced chatbot systems.


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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

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Summary of this model and related resources.

Model NameOnnx Tiny Random Gpt2 Without Merge
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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