18 projects
nm-magic-wand-nightly
SparseLinear layers
compressed-tensors
Library for utilization of compressed safetensors of neural network models
sparsezoo-nightly
Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
nm-magic-wand
SparseLinear layers
nm-vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
deepsparse-nightly
An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application
sparseml-nightly
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
deepsparse
An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application
deepsparse-ent
An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application
nm-transformers
State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
nm-yolov5
sparseml
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
sparsezoo
Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
nm-yolov5-nightly
nm-transformers-nightly
State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
sparsify-nightly
Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint
sparsify
Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint
optimum-deepsparse
Optimum DeepSparse is an extension of the Hugging Face Transformers library that integrates the DeepSparse inference runtime. DeepSparse offers GPU-class performance on CPUs, making it possible to run Transformers and other deep learning models on commodity hardware with sparsity. Optimum DeepSparse provides a framework for developers to easily integrate DeepSparse into their applications, regardless of the hardware platform.
