LLM training in simple, raw C/CUDA

SWE-agent takes a GitHub issue and tries to automatically fix it, using GPT-4, or your LM of choice. It solves 12.47% of bugs in the SWE-bench evaluation set and takes just 1 minute to run.

🙌 OpenHands: Code Less, Make More

Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

Machine Learning Compiler Road Map

A Production-ready Reinforcement Learning AI Agent Library brought by the Applied Reinforcement Learning team at Meta.

An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & Mixtral)

Code and documentation to train Stanford's Alpaca models, and generate the data.

An index of algorithms for offline reinforcement learning (offline-rl)

Create Customized Software using Natural Language Idea (through LLM-powered Multi-Agent Collaboration)

A MIT-licensed, deployable starter kit for building and customizing your own version of AI town - a virtual town where AI characters live, chat and socialize.

Generative Agents: Interactive Simulacra of Human Behavior

Example models using DeepSpeed

A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges

SkyAGI: Emerging human-behavior simulation capability in LLM

An attempt to build a working, locally-running cheap version of Generative Agents: Interactive Simulacra of Human Behavior

Making large AI models cheaper, faster and more accessible

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM

ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

🦜🔗 Build context-aware reasoning applications

TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning 🔥 ⚡ 🌈

PyTorch Implementation of Distributed Prioritized Experience Replay(Ape-X)

Useful CMake Examples

Pokémon battle simulator.

Obfuscator for Python

PyTorch deep learning projects made easy.

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.

Implementations of deep RL papers and random experimentation