#142 · Primary category: Deep Learning Frameworks

mindnlp

deep-learning diffusion-models huggingface large-language-models llm mindspore natural-language-processing nlp nlp-library python vlm

MindSpore + 🤗Huggingface: Run any Transformers/Diffusers model on MindSpore with seamless compatibility and acceleration.

Project last updated:03/08/26

GitHub Stars

920

Forks

271

Contributors

169

License

Apache-2.0

Why we included this project

Teams that have already built on HuggingFace's Transformers and Diffusers APIs can keep that code when they move to a different hardware stack. MindNLP works as a compatibility layer: you import the package and your existing model calls execute on MindSpore, which is what you want when running on Ascend NPUs. It removes the usual porting work of rewriting pipelines for a new backend, which helps engineers standardizing on Ascend across NPU, GPU, and CPU setups without maintaining a separate codebase. It is also convenient for evaluation, since you can run the same models on different backends and compare performance directly. Treat it as an infrastructure dependency to prototype against rather than a turnkey application.

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