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GitHub repos gaining traction - what high-signal users are starring and what's climbing the board, captured daily and enriched from GitHub. Raw material for spotting new tech and patterns worth building on.
3,195
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193
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26 repos
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Fit DisRNNs to behavioral and neural data, interpret the fits, and discover new useful cognitive models
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JAX in JavaScript – ML library for the web, running on WebGPU & Wasm
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This module defines a type system for distributed training code, based off of JAX's sharding in types, but adapted for the PyTorch ecosystem.
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Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
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A Python package for probabilistic state space modeling with JAX
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Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs
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TPU inference for vLLM, with unified JAX and PyTorch support.
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Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
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JAX/Equinox implementation of ProteinMPNN and LigandMPNN for protein sequence design
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Online Goal-Conditioned Reinforcement Learning in JAX. ICLR 2025 Spotlight.
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A multi-framework neural network compiler lowering PyTorch, JAX, Flax, and Keras models to portable, high-performance GGML execution
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A Pure JAX AsyncRL stack
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Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
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Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/
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Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
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Linear solvers in JAX and Equinox. https://docs.kidger.site/lineax
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A retargetable MLIR-based machine learning compiler and runtime toolkit.
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marimo + uv + jax starter template
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CUDA Craftax-Classic: 7x faster RL training than JAX
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A Python DSL to write Nvidia PTX for Hopper and Blackwell in JAX and PyTorch
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Minimal and scalable research codebase in JAX, designed for rapid iteration on frontier research in LLM and other autoregressive models.
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Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
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Student materials for DIS 2026 JAX-on-TPU workshop
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Code release for "i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models"