The PyTorch Foundation has officially incorporated Safetensors as a hosted project, integrating a widely used AI model distribution format into its open-source offerings. Developed by Hugging Face, Safetensors is engineered to prevent arbitrary code execution when model files are shared, addressing a persistent problem associated with earlier pickle-based approaches within the machine learning ecosystem.
By joining the ranks of other hosted projects like DeepSpeed, Helion, PyTorch, Ray, and vLLM, Safetensors reflects the Foundation’s commitment to bolstering open-source AI initiatives under the Linux Foundation’s umbrella. This move comes as the distribution of AI models has become increasingly sensitive, particularly as organizations transition models from research environments to production. The choice of file formats and serialization methods in this phase is critical, since insecure formats can expose users to risks if untrusted code is executed during the loading process.
To mitigate these risks, Safetensors stores tensor data in a manner that prohibits arbitrary code execution, and it has gained traction as a metadata format for model distribution within the open-source machine learning community. Mark Collier, Executive Director of the PyTorch Foundation, emphasized that the integration of Safetensors is a significant step toward enhancing the safety of open-source AI tools at scale. “Safetensors’ contribution to the PyTorch Foundation is an important step towards scaling production-grade AI models,” Collier stated. “Safetensors ensures secure model distribution and de-risks code execution, all while offering significant speed across complex computing architectures.”
The crux of this announcement lies not in the model itself, but in the methods used for packaging and sharing those models. Developers frequently download model weights and associated files from repositories for local or cloud-based execution. If these files utilize formats that can execute code, users may unknowingly face hidden dangers. This reality has elevated the importance of more secure serialization formats as a fundamental component of AI infrastructure.
The rising prevalence of open-weight models has led to an increase in the volume of files exchanged among research groups, developers, and companies, heightening awareness around the distribution methods of these artifacts. Safetensors has emerged as a leading alternative, with its architecture making it a standard choice among model publishers who aim to minimize exposure to unsafe loading techniques while maintaining ease of sharing and usability.
Advocates of Safetensors’ inclusion in the PyTorch Foundation argue that this partnership could enhance the project’s visibility and governance within a more extensive open-source framework. Luc Georges, Co-Maintainer of Safetensors, and Lysandre Debut, Chief Open Source Officer at Hugging Face, remarked, “Safetensors joining the PyTorch Foundation is an important step towards using a safe serialization format everywhere by default. The new ecosystem and exposure the library will gain from this move will solidify its security guarantees and usability.” They emphasized that while Safetensors is an established project within the community, its journey is just beginning, positioning the PyTorch Foundation as an ideal environment for the next phase of its development.
The addition of Safetensors illustrates the PyTorch Foundation’s broader ambition to extend its focus beyond core training frameworks into adjacent areas of infrastructure. Although PyTorch remains central to the Foundation’s identity, its portfolio now encompasses model training, inference, and model-handling tools. This expanded scope is increasingly relevant as open-source AI projects are integrated into production settings, where security and interoperability issues can arise from the connections between various tools rather than from any single framework.
Matt White, holding roles at both the Linux Foundation and the PyTorch Foundation, underscored that the integration of Safetensors and Helion aligns with this wider technical vision. “Safetensors joining the PyTorch Foundation promises safer, more interoperable packaging for model artifacts,” White stated. “The project has become a de facto standard for open-weight model distribution by halting risk associated with arbitrary code execution while also supporting fast, practical loading workflows.” Together with Helion, these contributions fortify the technical future of open-source AI, making it more robust as it evolves.
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