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Production-ready platform for agentic workflow development.
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
Making large AI models cheaper, faster and more accessible
aider is AI pair programming in your terminal
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…
TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. Tensor…
QLoRA: Efficient Finetuning of Quantized LLMs
An elegant PyTorch deep reinforcement learning library.
Accessible large language models via k-bit quantization for PyTorch.
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper.
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
Schedule-Free Optimization in PyTorch
🦁 Lion, new optimizer discovered by Google Brain using genetic algorithms that is purportedly better than Adam(w), in Pytorch
Awesome machine learning for combinatorial optimization papers.
Attention based model for learning to solve different routing problems
A simple and well styled PPO implementation. Based on my Medium series: https://medium.com/@eyyu/coding-ppo-from-scratch-with-pytorch-part-1-4-613dfc1b14c8.
Implementation of the specific Transformer architecture from PaLM - Scaling Language Modeling with Pathways
Temporal Pattern Attention for Multivariate Time Series Forecasting
Reinforcement Learning for Solving the Vehicle Routing Problem