🌟100+ LLM & RL Algorithm Maps！🎉 | 原创 LLM / RL 100+原理图📚，由《大模型算法》作者发起

《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新，English version ongoing

llama3 implementation one matrix multiplication at a time

Visualizer for neural network, deep learning and machine learning models

deep learning for image processing including classification and object-detection etc.

The official GitHub page for the survey paper "A Survey of Large Language Models".

中国大模型

Official github repo for C-Eval, a Chinese evaluation suite for foundation models

整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。

An open-source tool-augmented conversational language model from Fudan University

Making large AI models cheaper, faster and more accessible

ChatGLM-6B：开源双语对话语言模型

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

Plug and play modules to optimize the performances of your AI systems 🚀

PyTorch tutorials.

Inference code for LLaMA models

bert-base-chinese example

👑 Easy-to-use and powerful NLP library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Ques…

中文nlp应用(数据、模型、训练、推理)

Official implementations for various pre-training models of ERNIE-family, covering topics of Language Understanding & Generation, Multimodal Understanding & Generation, and beyond.

QAmatch(qa_match)/文本匹配/文本分类/文本embedding/文本聚类/文本检索（bow/ifidf/ngramtf-df/bert/albert/bm25/…/nn/gbdt/xgb/kmeans/dscan/faiss/….）

大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP

Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）

Implementation of the specific Transformer architecture from PaLM - Scaling Language Modeling with Pathways

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

限定领域的三元组抽取的一次尝试，本文将会介绍笔者在2019语言与智能技术竞赛的三元组抽取比赛方面的一次尝试。

记录本人整理的一些数据集

Repo for external large-scale work