Fxmarty
Rank:Average Model Cost: $0.0000
Number of Runs: 442,943
Models by this creator
tiny-llama-fast-tokenizer
tiny-llama-fast-tokenizer
The tiny-llama-fast-tokenizer is a language model that has been trained to quickly tokenize text. Tokenization is the process of breaking down a text into smaller units, called tokens, such as words or subwords. This model has been optimized to perform tokenization with high speed and efficiency.
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216.8K
Huggingface
tiny-random-working-LongT5Model
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58.8K
Huggingface
resnet-tiny-beans
resnet-tiny-beans
ResNet-Tiny-Beans is an image classification model that can identify different types of beans using deep learning techniques. It is based on the ResNet architecture, which is a popular convolutional neural network (CNN) architecture known for its performance in image recognition tasks. With this model, users can train and deploy an AI model that can accurately classify different bean varieties based on input images.
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44.4K
Huggingface
sshleifer-tiny-mbart-onnx
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.
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32.9K
Huggingface
tiny-marian
tiny-marian
The tiny-marian model is a text-to-text generation model. While the platform did not provide a description, it can be inferred that the model is a smaller version of the Marian model, which is a neural machine translation model. The tiny-marian model likely performs similar tasks, generating text based on an input text prompt.
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24.7K
Huggingface
tiny-testing-gpt2-remote-code
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23.8K
Huggingface
pix2struct-tiny-random
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15.8K
Huggingface
sam-vit-tiny-random
sam-vit-tiny-random
The sam-vit-tiny-random model is a neural network model designed for image classification tasks. It is based on the Vision Transformer (ViT) architecture, which uses self-attention mechanisms to process images. The model is trained using a random initialization strategy, where the weights of the neural network are randomly initialized, allowing the model to learn from scratch. This model can be used to classify images into different categories based on their visual content.
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11.3K
Huggingface
onnx-tiny-random-gpt2-without-merge
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.
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7.3K
Huggingface
onnx-tiny-random-gpt2-with-merge
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7.3K
Huggingface