HyperFast is a meta-trained hypernetwork designed for instant classification of tabular data in a single forward pass. It generates a task-specific neural network tailored to an unseen dataset, eliminating the need for training a model. The method is evaluated on 15 tabular datasets, including genomics data and OpenML datasets, showing highly competitive results compared to traditional machine learning methods, AutoML systems, and boosting machines. HyperFast is significantly faster and demonstrates robust adaptability across various classification tasks with minimal fine-tuning. It introduces a promising paradigm for fast classification, potentially reducing the computational burden of deep learning. The model is implemented with a scikit-learn-like interface and is available at https://github.com/AI-sandbox/HyperFast. HyperFast addresses the computational and time costs of traditional machine learning and deep learning methods for tabular data. It uses a hypernetwork pre-trained to predict the weights of a smaller neural network, enabling instant classification without training. The hypernetwork is trained on a wide range of datasets with different distributions, allowing it to learn relevant and general meta-features. During testing, the hypernetwork adapts to new datasets, predicting accurate weights for classification. The hypernetwork is trained on a wide range of datasets with different data distributions, allowing it to learn relevant and general meta-features. During testing, the hypernetwork adapts to new datasets, predicting accurate weights for classification. The method is evaluated on 15 tabular datasets, including genomics data and OpenML datasets, showing highly competitive results compared to traditional machine learning methods, AutoML systems, and boosting machines. HyperFast is significantly faster and demonstrates robust adaptability across various classification tasks with minimal fine-tuning. It introduces a promising paradigm for fast classification, potentially reducing the computational burden of deep learning. The model is implemented with a scikit-learn-like interface and is available at https://github.com/AI-sandbox/HyperFast.HyperFast is a meta-trained hypernetwork designed for instant classification of tabular data in a single forward pass. It generates a task-specific neural network tailored to an unseen dataset, eliminating the need for training a model. The method is evaluated on 15 tabular datasets, including genomics data and OpenML datasets, showing highly competitive results compared to traditional machine learning methods, AutoML systems, and boosting machines. HyperFast is significantly faster and demonstrates robust adaptability across various classification tasks with minimal fine-tuning. It introduces a promising paradigm for fast classification, potentially reducing the computational burden of deep learning. The model is implemented with a scikit-learn-like interface and is available at https://github.com/AI-sandbox/HyperFast. HyperFast addresses the computational and time costs of traditional machine learning and deep learning methods for tabular data. It uses a hypernetwork pre-trained to predict the weights of a smaller neural network, enabling instant classification without training. The hypernetwork is trained on a wide range of datasets with different distributions, allowing it to learn relevant and general meta-features. During testing, the hypernetwork adapts to new datasets, predicting accurate weights for classification. The hypernetwork is trained on a wide range of datasets with different data distributions, allowing it to learn relevant and general meta-features. During testing, the hypernetwork adapts to new datasets, predicting accurate weights for classification. The method is evaluated on 15 tabular datasets, including genomics data and OpenML datasets, showing highly competitive results compared to traditional machine learning methods, AutoML systems, and boosting machines. HyperFast is significantly faster and demonstrates robust adaptability across various classification tasks with minimal fine-tuning. It introduces a promising paradigm for fast classification, potentially reducing the computational burden of deep learning. The model is implemented with a scikit-learn-like interface and is available at https://github.com/AI-sandbox/HyperFast.