Optimum
optimum-4f41dc43·6 events·first seen 28d agoAliases: Optimum
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale
Hugging Face announced Optimum, an optimization toolkit designed to accelerate Transformers models on various hardware backends. The toolkit aims to bridge the gap between Transformers model development and hardware-specific optimizations from partners. It provides a unified interface for quantization, pruning, and hardware-accelerated inference across different accelerators.
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.
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.
AMD + Hugging Face: Large Language Models Out-of-the-Box Acceleration with AMD GPU
Hugging Face and AMD announced integration work enabling out-of-the-box LLM acceleration on AMD GPUs via the Optimum library. The collaboration targets ROCm-based AMD hardware, aiming to reduce friction for users running inference on non-NVIDIA GPU stacks. This represents a continued push to broaden the hardware ecosystem available to open-weights model users.
Quanto: a PyTorch quantization backend for Optimum
Hugging Face introduced Quanto, a new PyTorch-based quantization backend integrated into the Optimum library. Quanto supports multiple quantization schemes and data types, targeting efficient inference for large language models and other neural networks. The tool is designed to work across hardware backends and integrates with the Hugging Face ecosystem.
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.