Efficient Transformers for research, PyTorch and Tensorflow using Locality Sensitive Hashing

Reformer, the efficient Transformer, in Pytorch

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.

A high-throughput and memory-efficient inference and serving engine for LLMs

📐 Jekyll theme for building a personal site, blog, project documentation, or portfolio.

A beautiful, simple, clean, and responsive Jekyll theme for academics

The hub for EleutherAI's work on interpretability and learning dynamics

ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale

🦜🔗 Build context-aware reasoning applications

Drag & drop UI to build your customized LLM flow

A collection of apps powered by the LangChain LLM framework.

A minimal GPU design in Verilog to learn how GPUs work from the ground up

A Python-embedded modeling language for convex optimization problems.

Codes for paper "Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation"

MySQL入门教程（MySQL tutorial book）

Grok open release

NS-3 codes for Graph Representation Learning for Contention and Interference Management in Wireless Networks

Codes for paper "Graph Representation Learning for Contention and Interference Management in Wireless Networks"

Code for the paper: ATHENA: Machine Learning and Reasoning for Radio Resources Scheduling in vRAN systems

Python wrapper for Lambda Lanczos

C++ adaptive and header-only Lanczos algorithm library

NumPy & SciPy for GPU

Splitting Conic Solver