Stars
Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.
《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持简中、繁中、English、日本語，提供 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 等代码实现
"MiniRAG: Making RAG Simpler with Small and Open-Sourced Language Models"
[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"
RAGEN leverages reinforcement learning to train LLM reasoning agents in interactive, stochastic environments.
⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)
ICTIR 2025 "Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation"
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Collecting awesome papers of RAG for AIGC. We propose a taxonomy of RAG foundations, enhancements, and applications in paper "Retrieval-Augmented Generation for AI-Generated Content: A Survey".
The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation.
Build resilient language agents as graphs.
This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.
Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
Code repository for supporting the paper "Atlas Few-shot Learning with Retrieval Augmented Language Models",(https//arxiv.org/abs/2208.03299)
PyTorch Tutorial for Deep Learning Researchers
A modular graph-based Retrieval-Augmented Generation (RAG) system
Learning to Tokenize for Generative Retrieval (NeurIPS 2023)
ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22, ACL'23, EMNLP'23)
A book for Learning the Foundations of LLMs
Source code for experiments in the papers "Complex Embeddings for Simple Link Prediction" (ICML 2016) and "Knowledge Graph Completion via Complex Tensor Factorization" (JMLR 2017).
Simple implementations of TransE, DistMult, ComplEx