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Dataset Creation
Convert to Safetensors

Convert to Safetensors

Convert a model to Safetensors and open a PR

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What is Convert to Safetensors ?

Convert to Safetensors is a practical tool designed to convert AI models to the Safetensors format, a modern, efficient alternative to older formats like PyTorch's .pt files. Its primary purpose is to simplify model conversion and ** FACILITATE the creation of pull requests (PRs)** for integrating these models into larger projects or libraries. By using this tool, users can ensure their models are compatible with the latest standards in AI model sharing.


Features

  • Efficient Conversion: Converts models to Safetensors format quickly and reliably.
  • Cross-Compatibility: Works seamlessly with various AI frameworks and libraries.
  • Batch Processing: Allows converting multiple models at once for better productivity.
  • GitHub Integration: Automatically opens a PR for model integration into popular repositories.
  • Open-Source Friendly: Designed to support community-driven AI projects and collaboration.

How to use Convert to Safetensors ?

  1. Install the Tool: Run pip install convert-to-safetensors to install the package.
  2. Prepare Your Model: Ensure your model is in a supported format (e.g., PyTorch, TensorFlow).
  3. Convert the Model: Use the command convert-to-safetensors --input-path /path/to/model --output-path /path/to/output.
  4. Create a PR: Follow the prompted instructions to automatically open a PR for model integration.

Frequently Asked Questions

What is the Safetensors format?
Safetensors is a secure and efficient format for storing and sharing AI models, designed to replace older formats like PyTorch's .pt files. It offers better security and compatibility across different frameworks.

Can I use Convert to Safetensors for non-PyTorch models?
Yes, Convert to Safetensors supports models from various frameworks, including TensorFlow and others, making it a versatile tool for AI practitioners.

Where are the converted models stored?
The converted models are stored in the output directory you specify during the conversion process. Ensure you have write permissions for the target directory.

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