PyHGF: A neural network library for predictive coding
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Updated
Jun 12, 2024 - Python
PyHGF: A neural network library for predictive coding
Riemannian geometry in JAX
Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python ⚡
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
Collection of eclectic utils for python.
Automated Machine Learning on Kubernetes
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
Map single-cell transcriptomes to copy number evolutionary trees.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
NAACL '24 (Demo) / MlSys @ NeurIPS '23 - RedCoast: A Lightweight Tool to Automate Distributed Training and Inference
Distrax, but in equinox. Lightweight JAX library of probability distributions and bijectors.
This is a JAX/Flax-based transformer language model trained on a Japanese dataset. It is based on the official Flax example code (lm1b).
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.
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