This repository explores the effectiveness of curriculum learning (CL) in improving small code language models.

Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)

RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.

chatGPT私有化部署

Easy Docker setup for Stable Diffusion with user-friendly UI

Docker image for Stable Diffusion WebUI with ControlNet, After Detailer, Dreambooth, Deforum and roop extensions, as well as Kohya_ss and ComfyUI

An example of using AI and AWS to generate, and deploy websites in under a minute.

Llama 2 Everywhere (L2E)

SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities.

End-to-End Speech Processing Toolkit

Automatically generate customized landing pages for businesses based on their name or specific instructions. This project aims to simplify and expedite the website creation process by leveraging th…

MEASURING MASSIVE MULTITASK CHINESE UNDERSTANDING

A Massive Multi-Level Multi-Subject Knowledge Evaluation benchmark

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

An experimental open-source attempt to make GPT-4 fully autonomous.

⭐️ NLP Algorithms with transformers lib. Supporting Text-Classification, Text-Generation, Information-Extraction, Text-Matching, RLHF etc.

GUI for ChatGPT API

强化学习中文教程（蘑菇书），在线阅读地址：https://datawhalechina.github.io/easy-rl/

Modern JavaScript Tutorial

Stanford's Alexa Prize socialbot

* W.I.P *🛠 Full-on full stack front end and a bit of back end web development challenge. Challenging every day to learn new stuffs about react & its in-depth features and also to explore the taste …

PyTorch code for ACL 2022 paper: RoMe: A Robust Metric for Evaluating Natural Language Generation https://aclanthology.org/2022.acl-long.387/

📖《Python Parallel Programming Cookbook》中文版

渊 - A project for Classical Chinese

Frontend Project With Tutorial And Source Code

记录本人整理的一些数据集

中文自然语言推理与语义相似度数据集

北航大数据高精尖中心研究张日崇团队对问答系统的总结。包括基于知识图谱的问答（KBQA），基于文本的问答系统（TextQA），基于表格的问答系统（TabletQA）和基于视觉的问答系统（VisualQA），每类系统分别对学术界和工业界进行总结。