AI Briefing#optimum
The latest AI and developer news about #optimum, with the original source and a short summary.
The latest AI and developer news about #optimum, with the original source and a short summary.
Hugging Face has released Quanto, a general-purpose PyTorch quantization backend for the Optimum library.
Covers how to optimize inference performance and memory efficiency of the Bark TTS model using Hugging Face's Optimum and Accelerate.
This presents an optimization method using NNCF and Optimum that boosts Stable Diffusion inference speed on Intel CPUs by 5.1x.
By integrating Hugging Face's Optimum library with ONNX Runtime, training speed for transformer models has been improved by up to 130%.
Explains how to convert Transformers models to ONNX format and optimize inference performance using Hugging Face Optimum.
A variety of Transformer models optimized for Graphcore IPUs have been added to the Hugging Face Optimum library.
Hugging Face has updated the features of the Optimum library, which optimizes inference performance for Transformers models.
The integration of Habana Labs' SynapseAI and Hugging Face's Optimum library accelerates Transformer training based on Gaudi processors.
Hugging Face has released Optimum, an open-source library that supports hardware-specific performance optimization for transformer models.
Hugging Face and Graphcore have entered a partnership to provide Transformer models optimized for IPU.