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#onnx

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3 items

modelsJan 15

Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive

Hugging Face and Microsoft collaborated to optimize SD Turbo and SDXL Turbo inference using ONNX Runtime and Olive. This integration reduces latency by up to 30% and improves throughput. You can deploy these optimized models on Hugging Face's Inference API or use them locally. The optimization enables faster and more efficient image generation.

Key takeaways
  • Up to 30% latency reduction with ONNX Runtime and Olive.
  • Optimized models deployable via Hugging Face's Inference API or locally.
  • Faster image generation for applications.
HHugging Face Blog#inference-optimization#onnx#image-generation
modelsOct 4

Accelerating over 130,000 Hugging Face models with ONNX Runtime

Microsoft and Hugging Face collaborated to integrate ONNX Runtime with Hugging Face Hub, enabling accelerated inference for over 130,000 models. This integration allows for faster and more efficient model deployment. You can now deploy models with optimized performance. The collaboration aims to improve the overall model serving experience.

Key takeaways
  • 130,000+ Hugging Face models accelerated with ONNX Runtime.
  • Integration enables faster and more efficient model deployment.
  • Optimized performance for model serving.
HHugging Face Blog#model-serving#onnx#hugging-face
toolsJun 22

Convert Transformers to ONNX with Hugging Face Optimum

Hugging Face Optimum provides a seamless way to convert transformer models to ONNX format, enabling faster inference and better performance. This conversion allows for optimized deployment on various hardware platforms. You can leverage Optimum's tools to streamline your model's deployment process. The conversion process is designed to be straightforward and efficient.

Key takeaways
  • Hugging Face Optimum supports converting transformers to ONNX.
  • ONNX format enables faster inference and better performance.
  • Conversion process is straightforward and efficient.
HHugging Face Blog#onnx#transformers#model-deployment