chenxn2020's starred repositories

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.

MetaGPT

🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming

Language:PythonLicense:MITStargazers:40929Issues:870Issues:552

LLaMA-Factory

Unify Efficient Fine-Tuning of 100+ LLMs

Language:PythonLicense:Apache-2.0Stargazers:24344Issues:166Issues:3896

shell_gpt

A command-line productivity tool powered by AI large language models like GPT-4, will help you accomplish your tasks faster and more efficiently.

Language:PythonLicense:MITStargazers:8677Issues:81Issues:292

NLP_ability

总结梳理自然语言处理工程师(NLP)需要积累的各方面知识,包括面试题,各种基础知识,工程能力等等,提升核心竞争力

RapidOCR

Awesome OCR multiple programing languages toolkits based on ONNXRuntime, OpenVION and PaddlePaddle.

Language:PythonLicense:Apache-2.0Stargazers:2247Issues:36Issues:107

Data-Copilot

Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow

Language:PythonLicense:MITStargazers:1309Issues:11Issues:45

AgentTuning

AgentTuning: Enabling Generalized Agent Abilities for LLMs

LLMs_interview_notes

该仓库主要记录 大模型(LLMs) 算法工程师相关的面试题

LLM-in-Vision

Recent LLM-based CV and related works. Welcome to comment/contribute!

clash-for-linux

Linux环境安装配置Clash工具,以实现代理上网效果。包含下载、安装、配置、运行、测试以及开机自启动、定期自动更新订阅功能的操作文档,希望对你有所帮助

awesome-SynthText

A curated list of awesome synthetic data for text location and recognition

InstructDoc

InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions (AAAI2024)

Language:PythonLicense:NOASSERTIONStargazers:129Issues:3Issues:7

AutoAct

[ACL 2024] AUTOACT: Automatic Agent Learning from Scratch for QA via Self-Planning

Language:PythonLicense:Apache-2.0Stargazers:126Issues:18Issues:6

Awesome-LLM-Prompt-Optimization

Awesome-LLM-Prompt-Optimization: a curated list of advanced prompt optimization and tuning methods in Large Language Models

Awesome-Chart-Understanding

A curated list of recent and past chart understanding work based on our survey paper: From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models.

Language:PythonStargazers:37Issues:0Issues:0

SciGraphQA

SciGraphQA

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:34Issues:1Issues:0

Tool-Augmented-Reward-Model

[ICLR'24 spotlight] Tool-Augmented Reward Modeling

Language:PythonLicense:MITStargazers:25Issues:0Issues:0

CHOCOLATE

Code and data for the paper "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning"

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:23Issues:2Issues:0

PTP

Improving Language Understanding from Screenshots. Paper: https://arxiv.org/abs/2402.14073

Language:PythonLicense:MITStargazers:22Issues:6Issues:1

Knowledge_Card

Code for "Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models", ICLR 2024 Oral.

Language:PythonLicense:MITStargazers:15Issues:0Issues:0
Language:PythonLicense:Apache-2.0Stargazers:15Issues:2Issues:1

weblm

[WSDM 2024] Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding

Stargazers:12Issues:0Issues:0
Language:OpenEdge ABLLicense:GPL-3.0Stargazers:11Issues:0Issues:0

Demix

[ISWC 2023] Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding

Language:PythonStargazers:5Issues:0Issues:0