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Model Benchmarking
Push Model From Web

Push Model From Web

Upload ML model to Hugging Face Hub

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What is Push Model From Web ?

Push Model From Web is a tool designed for Model Benchmarking that simplifies the process of uploading machine learning models to the Hugging Face Hub. It provides a seamless interface for model sharing, collaboration, and version control, making it easier for data scientists and developers to manage their ML models in a centralized platform.

Features

  • Model Upload: Directly upload your machine learning models from your local system to Hugging Face Hub.
  • Integration with Hugging Face: Native integration with the Hugging Face ecosystem for easy model management and sharing.
  • Support for Multiple Frameworks: Compatible with popular machine learning frameworks such as PyTorch, TensorFlow, and more.
  • Version Control: Track different versions of your models for better organization and rollbacks.
  • User-Friendly Interface: An intuitive dashboard for easy model management and monitoring.

How to use Push Model From Web ?

  1. Access the App: Open the Push Model From Web interface through your browser.
  2. Prepare Your Model: Ensure your machine learning model is saved locally in a compatible format.
  3. Login to Hugging Face Hub: Authenticate with your Hugging Face account to enable model uploads.
  4. Upload Your Model: Select the model file and fill in any required metadata, such as model name and description.
  5. Track Your Model: Once uploaded, receive a link to your model on Hugging Face Hub for sharing or further management.

Frequently Asked Questions

What frameworks are supported by Push Model From Web?
Push Model From Web supports models from popular frameworks like PyTorch, TensorFlow, and Keras.

How do I handle large model files?
For large models, consider using model compression or splitting the model into smaller parts before uploading.

Where are my models stored after upload?
Your models are stored on the Hugging Face Hub, which provides cloud-based storage and version control for your machine learning models.

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