# Burn Import
The `burn-import` crate enables seamless integration of pre-trained models from popular machine
learning frameworks into the Burn ecosystem. This functionality allows you to leverage existing
models while benefiting from Burn's performance optimizations and native Rust integration.
## Supported Import Formats
Burn currently supports three primary model import formats, each serving different use cases:
| [**ONNX** (Guide)](https://burn.dev/books/burn/import/onnx-model.html) | Open Neural Network Exchange format | Direct import of complete model architectures and weights from any framework that supports ONNX export |
| [**PyTorch** (Guide)](https://burn.dev/books/burn/import/pytorch-model.html) | PyTorch weights (.pt, .pth) | Loading weights from PyTorch models into a matching Burn architecture |
| [**Safetensors** (Guide)](https://burn.dev/books/burn/import/safetensors-model.html) | Hugging Face's model serialization format | Loading a model's tensor weights into a matching Burn architecture |
## ONNX Contributor Resources
- [ONNX to Burn conversion guide](https://burn.dev/books/contributor/guides/onnx-to-burn-conversion-tool.html) -
Instructions for adding support for additional ONNX operators
- [ONNX tests README](https://github.com/tracel-ai/burn/blob/main/crates/burn-import/onnx-tests/README.md) -
Testing procedures for ONNX operators
- [Supported ONNX Operators table](https://github.com/tracel-ai/burn/blob/main/crates/burn-import/SUPPORTED-ONNX-OPS.md) -
Complete list of currently supported ONNX operators