# Quick deployment

A replica of Gemma tokenizer for CI/testing scenarios without access tokens

This model is released under the MIT license, specific details are currently unknown.

This is a zero-shot classification model under the MIT License, suitable for various classification tasks without specific training.

A versatile large language model capable of handling various natural language processing tasks

This is a zero-shot classification model under the MIT license, suitable for various classification tasks.

This is an image classification model built with PyTorch and HuggingPics, specifically designed for recognizing rare dog breeds.

This model is released under the MIT License. Further details need to be supplemented.

This is an image classification model generated by HuggingPics, achieving an accuracy rate of 94.12%.

This is a multi-category image classification model trained using AutoTrain, specifically designed for flag recognition tasks.

This is a zero-shot classification model capable of classifying text without task-specific training data

A Tacotron2 text-to-speech (TTS) model trained on 12 days of custom German dataset using the SpeechBrain framework.

This model is released under the MIT License, with no specific details provided.

Image classification model generated by HuggingPics with an accuracy of 91.67%

This is an image classification model based on PyTorch and HuggingPics, specifically designed for identifying rare dog breeds.

An image classification model generated by HuggingPics that accurately distinguishes between pictures of llamas and potatoes.

An image classification model based on the Vision Transformer architecture, specifically designed to distinguish between images of Chihuahuas and cookies

A model fine-tuned based on bert-base-uncased, with a training accuracy of 93.75% and validation accuracy of 50%