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a_walk_over_ast

azure-chat

A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.

chat-librarian

chinese-llama-alpaca

中文LLaMA&Alpaca大语言模型+本地部署 (Chinese LLaMA & Alpaca LLMs)

customer-lifetime-prediction-using-python

What is CLV or LTV? CLV or LTV is a metric that helps you measure the customer's lifetime value to a business. In this kernel, I am sharing the customer lifetime value prediction using BG-NBD, Pareto, NBD & Gamma Model on top of RFM in Python.

faceless

featuringast

Extracting features from Abstract Syntax Tree with relevant to neighbor/parent and variable sibling.

fucking-algorithm

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

genui-backend

grammars-v4

Grammars written for ANTLR v4; expectation that the grammars are free of actions.

i3d_arcface

insightface

State-of-the-art 2D and 3D Face Analysis Project

intellij-plugin-template

intelliparse

intellisearch

l2-tanimoto

Implementation on Level-2 of Tanimoto Algorithm of Computing Vector Similarity

lib-enquiry-desk

llama-retrieval-plugin

LLaMa retrieval plugin script using OpenAI's retrieval plugin

mediacrawler-new

小红书笔记 | 评论爬虫、抖音视频 | 评论爬虫、快手视频 | 评论爬虫、B 站视频 ｜ 评论爬虫、微博帖子 ｜ 评论爬虫

minirwkv-4

实现Blip2RWKV+QFormer的多模态图文对话大模型，使用Two-Step Cognitive Psychology Prompt方法，仅3B参数的模型便能够出现类人因果思维链。对标MiniGPT-4，ImageBind等图文对话大语言模型，力求以更小的算力和资源实现更好的智能效果。

nand2tetris

计算机系统要素-从零开始构建现代计算机

pigeon-backend

shuffling_chars

As found in some linguistics studies, a shuffled order of Chinese characters within a compound word has limited disturbance on understanding a sentence, which is attributed to reader processing word at both word-wise and character-wise levels, followed by a restoration of appropriate character order. Inspired by this idea, we propose a self-supervised method based on BERT, by shuffling characters within a whole word segment and labelling each character with its correct position in a word. A Query-Key-Value attention mechanism is introduced for a sequence tagging task aiming at finding the correct position of each character, which is equivalent to the intuition of restoring the original Chinese word like human readers.

srcml

srcML Toolkit

transfm

treelingual

tutorials

Just Announced - "Learn Spring Security OAuth":

vscode

Visual Studio Code

wingman