ONNX
onnx-b402285f·6 events·first seen 28d agoAliases: ONNX, ONNX Runtime
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ONNX Runtime
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Recent events (6)
Accelerating over 130,000 Hugging Face Models with ONNX Runtime
Hugging Face and Microsoft have integrated ONNX Runtime (ORT) to accelerate inference for over 130,000 models on the Hugging Face Hub. The integration enables optimized deployment across CPU and GPU hardware without requiring users to manually export or configure ONNX models. This represents a significant expansion of ORT's reach within the open-weights model ecosystem, lowering the barrier to production-grade inference optimization.
Convert Transformers to ONNX with Hugging Face Optimum
Hugging Face published a guide on converting Transformer models to ONNX format using the Optimum library. The post covers the tooling workflow for exporting models from the Transformers ecosystem into ONNX for optimized inference deployment. This is a practical infrastructure topic relevant to production ML deployment patterns.
Optimum + ONNX Runtime: Faster Training for Hugging Face Models
Hugging Face's Optimum library integrates with Microsoft's ONNX Runtime Training to accelerate fine-tuning of transformer models. The integration aims to reduce training time and memory usage with minimal code changes for practitioners using the Hugging Face ecosystem. This tooling update targets enterprise and research users looking to optimize training efficiency on existing hardware.
Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive
This Hugging Face blog post details how to accelerate Stable Diffusion Turbo and SDXL Turbo inference using ONNX Runtime and Microsoft's Olive optimization toolkit. The post covers the workflow for converting and optimizing diffusion models for faster deployment. This is a practical inference optimization guide targeting practitioners deploying image generation models.
Accelerated Inference with Optimum and Transformers Pipelines
Hugging Face announced integration between the Optimum library and the Transformers Pipelines API, enabling hardware-accelerated inference with minimal code changes. The integration targets deployment on specialized hardware backends such as ONNX Runtime, allowing users to swap in optimized inference engines transparently. This lowers the barrier to production-grade inference optimization for practitioners using the Hugging Face ecosystem.
Blazing Fast SetFit Inference with Optimum Intel on Xeon
Hugging Face demonstrates accelerated inference for SetFit few-shot text classification models using Optimum Intel on Intel Xeon CPUs. The post covers optimization techniques such as quantization and ONNX export to improve throughput and latency for CPU-based deployment. This is relevant to practitioners deploying lightweight NLP models in cost-sensitive or edge environments without GPU hardware.