A Multivariate Gaussian Bayes classifier written using JAX
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Updated
Sep 22, 2022 - Python
A Multivariate Gaussian Bayes classifier written using JAX
Optimal choice for 🛰 classification problem.
A practical method for training energy-based language models.
Versatile Data Ingestion Pipelines for Jax
Classification of multilingual dataset trained only on English training data using pre-trained models. Model is trained on TPUs using PyTorch and torch_xla library.
Access the Xspec models and corresponding JAX/XLA ops.
Modern Graph TensorFlow implementation of Super-Resolution GAN
As the quality of large language models increases, so do our expectations of what they can do. Since the release of OpenAI's GPT-2, text generation capabilities have received attention. And for good reason - these models can be used for summarization, translation, and even real-time learning in some language tasks.
deep learning inference perf analysis
Fast and easy distributed model training examples.
Tensorflow2 training code with jit compiling on multi-GPU.
Provides code to serialize the different models involved in Stable Diffusion as SavedModels and to compile them with XLA.
Simple and efficient RevNet-Library for PyTorch with XLA and DeepSpeed support and parameter offload
基于tensorflow1.x的预训练模型调用,支持单机多卡、梯度累积,XLA加速,混合精度。可灵活训练、验证、预测。
Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python ⚡
ALBERT model Pretraining and Fine Tuning using TF2.0
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