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A toolkit for interpreting and analyzing neural networks (vision)
Entropy Based Sampling and Parallel CoT Decoding
Scaling law by data manifold.
Transformer with Mu-Parameterization, implemented in Jax/Flax. Supports FSDP on TPU pods.
A Citation Manager and Zotero Integration for RemNote! Cite research all within your knowledge base!
Universal Tensor Operations in Einstein-Inspired Notation for Python.
shehper / scaling_laws
Forked from karpathy/nanoGPTAn open-source implementation of Scaling Laws for Neural Language Models using nanoGPT
Score LLM pretraining data with classifiers
High accuracy RAG for answering questions from scientific documents with citations
Urai AE, Doiron B, Leifer AM & Churchland AK (2022) Large-scale neural recordings call for new insights to link brain and behavior. Nature Neuroscience
🧬 Generative modeling of regulatory DNA sequences with diffusion probabilistic models 💨
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.
A case study of efficient training of large language models using commodity hardware.
A repository with exploration into using transformers to predict DNA ↔ transcription factor binding
Simple and efficient RevNet-Library for PyTorch with XLA and DeepSpeed support and parameter offload
Nested Hierarchical Transformer https://arxiv.org/pdf/2105.12723.pdf
Official Pytorch Implementation of: "ImageNet-21K Pretraining for the Masses"(NeurIPS, 2021) paper
CLU lets you write beautiful training loops in JAX.
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Flax is a neural network library for JAX that is designed for flexibility.
Optax is a gradient processing and optimization library for JAX.