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https://github.com/codingonion/awesome-llm-and-aigc
🚀🚀🚀A collection of some awesome public projects about Large Language Model, Vision Foundation Model and AI Generated Content.
https://github.com/codingonion/awesome-llm-and-aigc
List: awesome-llm-and-aigc
aigc awesome-list chatglm chatgpt computer-vision datasets gemma gpt hugging-face internlm jobs langchain large-language-models llama llama3 llm openai sora stable-diffusion yolo
Last synced: 18 days ago
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🚀🚀🚀A collection of some awesome public projects about Large Language Model, Vision Foundation Model and AI Generated Content.
- Host: GitHub
- URL: https://github.com/codingonion/awesome-llm-and-aigc
- Owner: codingonion
- Created: 2023-02-15T12:40:08.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-09T15:58:34.000Z (about 2 months ago)
- Last Synced: 2024-05-21T23:08:18.391Z (about 1 month ago)
- Topics: aigc, awesome-list, chatglm, chatgpt, computer-vision, datasets, gemma, gpt, hugging-face, internlm, jobs, langchain, large-language-models, llama, llama3, llm, openai, sora, stable-diffusion, yolo
- Homepage:
- Size: 180 KB
- Stars: 465
- Watchers: 10
- Forks: 43
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Lists
- awesome-llama2-resources - awesome-llm and aigc
- awesome-ChatGPT-repositories - awesome-llm-and-aigc - 🚀🚀🚀A collection of some awesome public projects about Large Language Model, Vision Foundation Model and AI Generated Content. (Awesome-lists)
- awesome-ai-list-guide - awesome-llm-and-aigc
- ultimate-awesome - awesome-llm-and-aigc - 🚀🚀🚀A collection of some awesome public projects about Large Language Model, Vision Foundation Model and AI Generated Content. (Other Lists / Julia Lists)
README
# Awesome-llm-and-aigc
[](https://github.com/sindresorhus/awesome)
🚀🚀🚀 This repository lists some awesome public projects about Large Language Model, Vision Foundation Model, AI Generated Content, the related Datasets and Applications.
## Contents
- [Awesome-llm-and-aigc](#awesome-llm-and-aigc)
- [Summary](#summary)
- [Frameworks](#frameworks)
- [Official Version](#official-version)
- [Neural Network Architecture](#neural-network-architecture)
- [Large Language Model](#large-language-model)
- [Vision Foundation Model](#vision-foundation-model)
- [AI Generated Content](#ai-generated-content)
- [Application Development Platform](#application-development-platform)
- [Fine-Tuning Framework](#fine-tuning-framework)
- [RAG Framework](#rag-framework)
- [LLM Inference Framework](#llm-inference-framework)
- [LLM Inference Benchmark](#llm-inference-benchmark)
- [LLM Deployment Engine](#llm-deployment-engine)
- [C Implementation](#c-implementation)
- [CPP Implementation](#cpp-implementation)
- [Python Implementation](#python-implementation)
- [Mojo Implementation](#mojo-implementation)
- [Rust Implementation](#rust-implementation)
- [zig Implementation](#zig-implementation)
- [Go Implementation](#go-implementation)
- [Vector Database](#vector-database)
- [Awesome List](#awesome-list)
- [Paper Overview](#paper-overview)
- [Learning Resources](#learning-resources)
- [Community](#community)
- [Prompts](#prompts)
- [Open API](#open-api)
- [Python API](#python-api)
- [Rust API](#rust-api)
- [Csharp API](#csharp-api)
- [Node.js API](#node.js-api)
- [Applications](#applications)
- [IDE](#ide)
- [Chatbot](#chatbot)
- [Robotics and Embodied AI](#robotics-and-embodied-ai)
- [Code Assistant](#code-assistant)
- [Translator](#translator)
- [Local knowledge Base](#local-knowledge-base)
- [Long-Term Memory](#long-term-memory)
- [Question Answering System](#question-answering-system)
- [Academic Field](#academic-field)
- [Medical Field](#medical-field)
- [Mental Health Field](#mental-health-field)
- [Legal Field](#legal-field)
- [Financial Field](#Financial-field)
- [Math Field](#math-field)
- [Music Field](#music-field)
- [Speech and audio Field](#speech-and-audio-field)
- [Humor Generation](#humor-generation)
- [Animation Field](#animation-field)
- [Food Field](#food-field)
- [Tool Learning](#tool-learning)
- [Autonomous Driving Field](#autonomous-driving-field)
- [Adversarial Attack Field](#adversarial-attack-field)
- [Multi-Agent Collaboration](#multi-agent-collaboration)
- [AI Avatar](#ai-avatar)
- [GUI](#gui)
- [Datasets](#datasets)
- [Open Datasets Platform](#open-datasets-platform)
- [Text Datasets](#text-datasets)
- [Multimodal Datasets](#multimodal-datasets)
- [SFT Datasets](#sft-datasets)
- [Blogs](#blogs)
- [Videos](#videos)
- [Jobs and Interview](#jobs-and-interview)
## Summary
- ### Frameworks
- #### Official Version
- ##### Neural Network Architecture
###### 神经网络架构
- [Transformer](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py) 
: "Attention is All You Need". (**[arXiv 2017](https://arxiv.org/abs/1706.03762)**).
- [KAN](https://github.com/KindXiaoming/pykan) 
: "KAN: Kolmogorov-Arnold Networks". (**[arXiv 2024](https://arxiv.org/abs/2404.19756)**).
- ##### Large Language Model
###### 大语言模型（LLM）
- GPT-1 : "Improving Language Understanding by Generative Pre-Training". (**[cs.ubc.ca, 2018](https://www.cs.ubc.ca/~amuham01/LING530/papers/radford2018improving.pdf)**).
- [GPT-2](https://github.com/openai/gpt-2) 
: "Language Models are Unsupervised Multitask Learners". (**[OpenAI blog, 2019](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf)**). [Better language models and their implications](https://openai.com/research/better-language-models).
- [GPT-3](https://github.com/openai/gpt-3) 
: "GPT-3: Language Models are Few-Shot Learners". (**[arXiv 2020](https://arxiv.org/abs/2005.14165)**).
- InstructGPT : "Training language models to follow instructions with human feedback". (**[arXiv 2022](https://arxiv.org/abs/2203.02155)**). "Aligning language models to follow instructions". (**[OpenAI blog, 2022](https://openai.com/research/instruction-following)**).
- [ChatGPT](https://chat.openai.com/): [Optimizing Language Models for Dialogue](https://openai.com/blog/chatgpt).
- [GPT-4](https://openai.com/product/gpt-4): GPT-4 is OpenAI’s most advanced system, producing safer and more useful responses. "Sparks of Artificial General Intelligence: Early experiments with GPT-4". (**[arXiv 2023](https://arxiv.org/abs/2303.12712)**). "GPT-4 Architecture, Infrastructure, Training Dataset, Costs, Vision, MoE". (**[SemianAlysis, 2023](https://www.semianalysis.com/p/gpt-4-architecture-infrastructure)**).
- [Llama 2](https://github.com/facebookresearch/llama) 
: Inference code for LLaMA models. "LLaMA: Open and Efficient Foundation Language Models". (**[arXiv 2023](https://arxiv.org/abs/2302.13971)**). "Llama 2: Open Foundation and Fine-Tuned Chat Models". (**[ai.meta.com, 2023-07-18](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/)**). (**[2023-07-18, Llama 2 is here - get it on Hugging Face](https://huggingface.co/blog/llama2)**).
- [Llama 3](https://github.com/meta-llama/llama3) 
: The official Meta Llama 3 GitHub site.
- [Gemma](https://github.com/google/gemma_pytorch) 
: The official PyTorch implementation of Google's Gemma models. [ai.google.dev/gemma](https://ai.google.dev/gemma)
- [Grok-1](https://github.com/xai-org/grok-1) 
: This repository contains JAX example code for loading and running the Grok-1 open-weights model.
- [Claude](https://www.anthropic.com/product) : Claude is a next-generation AI assistant based on Anthropic’s research into training helpful, honest, and harmless AI systems.
- [Whisper](https://github.com/openai/whisper) 
: Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. "Robust Speech Recognition via Large-Scale Weak Supervision". (**[arXiv 2022](https://arxiv.org/abs/2212.04356)**).
- [OpenChat](https://github.com/imoneoi/openchat) 
: OpenChat: Advancing Open-source Language Models with Imperfect Data. [huggingface.co/openchat/openchat](https://huggingface.co/openchat/openchat)
- [GPT-Engineer](https://github.com/AntonOsika/gpt-engineer) 
: Specify what you want it to build, the AI asks for clarification, and then builds it. GPT Engineer is made to be easy to adapt, extend, and make your agent learn how you want your code to look. It generates an entire codebase based on a prompt.
- [StableLM](https://github.com/Stability-AI/StableLM) 
: StableLM: Stability AI Language Models.
- [JARVIS](https://github.com/microsoft/JARVIS) 
: JARVIS, a system to connect LLMs with ML community. "HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace". (**[arXiv 2023](https://arxiv.org/abs/2303.17580)**).
- [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4) 
: MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models. [minigpt-4.github.io](https://minigpt-4.github.io/)
- [minGPT](https://github.com/karpathy/minGPT) 
: A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training.
- [nanoGPT](https://github.com/karpathy/nanoGPT) 
: The simplest, fastest repository for training/finetuning medium-sized GPTs.
- [MicroGPT](https://github.com/muellerberndt/micro-gpt) 
: A simple and effective autonomous agent compatible with GPT-3.5-Turbo and GPT-4. MicroGPT aims to be as compact and reliable as possible.
- [Dolly](https://github.com/databrickslabs/dolly) 
: Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform. [Hello Dolly: Democratizing the magic of ChatGPT with open models](https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html)
- [LMFlow](https://github.com/OptimalScale/LMFlow) 
: An extensible, convenient, and efficient toolbox for finetuning large machine learning models, designed to be user-friendly, speedy and reliable, and accessible to the entire community. Large Language Model for All. [optimalscale.github.io/LMFlow/](https://optimalscale.github.io/LMFlow/)
- [Colossal-AI](https://github.com/hpcaitech/ColossalAI) 
: Making big AI models cheaper, easier, and scalable. [www.colossalai.org](www.colossalai.org). "Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training". (**[arXiv 2021](https://arxiv.org/abs/2110.14883)**).
- [Lit-LLaMA](https://github.com/Lightning-AI/lit-llama) 
: ⚡ Lit-LLaMA. Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.
- [GPT-4-LLM](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) 
: "Instruction Tuning with GPT-4". (**[arXiv 2023](https://arxiv.org/abs/2304.03277)**). [instruction-tuning-with-gpt-4.github.io/](https://instruction-tuning-with-gpt-4.github.io/)
- [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) 
: Stanford Alpaca: An Instruction-following LLaMA Model.
- [feizc/Visual-LLaMA](https://github.com/feizc/Visual-LLaMA) 
: Open LLaMA Eyes to See the World. This project aims to optimize LLaMA model for visual information understanding like GPT-4 and further explore the potentional of large language model.
- [Lightning-AI/lightning-colossalai](https://github.com/Lightning-AI/lightning-colossalai) 
: Efficient Large-Scale Distributed Training with [Colossal-AI](https://colossalai.org/) and [Lightning AI](https://lightning.ai/).
- [GPT4All](https://github.com/nomic-ai/gpt4all) 
: GPT4All: An ecosystem of open-source on-edge large language models. GTP4All is an ecosystem to train and deploy powerful and customized large language models that run locally on consumer grade CPUs.
- [ChatALL](https://github.com/sunner/ChatALL) 
: Concurrently chat with ChatGPT, Bing Chat, bard, Alpaca, Vincuna, Claude, ChatGLM, MOSS, iFlytek Spark, ERNIE and more, discover the best answers. [chatall.ai](http://chatall.ai/)
- [1595901624/gpt-aggregated-edition](https://github.com/1595901624/gpt-aggregated-edition) 
: 聚合ChatGPT官方版、ChatGPT免费版、文心一言、Poe、chatchat等多平台，支持自定义导入平台。
- [FreedomIntelligence/LLMZoo](https://github.com/FreedomIntelligence/LLMZoo) 
: ⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡ [Tech Report](https://github.com/FreedomIntelligence/LLMZoo/blob/main/assets/llmzoo.pdf)
- [shm007g/LLaMA-Cult-and-More](https://github.com/shm007g/LLaMA-Cult-and-More) 
: News about 🦙 Cult and other AIGC models.
- [X-PLUG/mPLUG-Owl](https://github.com/X-PLUG/mPLUG-Owl) 
: mPLUG-Owl🦉: Modularization Empowers Large Language Models with Multimodality.
- [i-Code](https://github.com/microsoft/i-Code) 
: The ambition of the i-Code project is to build integrative and composable multimodal Artificial Intelligence. The "i" stands for integrative multimodal learning. "CoDi: Any-to-Any Generation via Composable Diffusion". (**[arXiv 2023](https://arxiv.org/abs/2305.11846)**).
- [WorkGPT](https://github.com/h2oai/h2ogpt) 
: WorkGPT is an agent framework in a similar fashion to AutoGPT or LangChain.
- [h2oGPT](https://github.com/team-openpm/workgpt) 
: h2oGPT is a large language model (LLM) fine-tuning framework and chatbot UI with document(s) question-answer capabilities. "h2oGPT: Democratizing Large Language Models". (**[arXiv 2023](https://arxiv.org/abs/2306.08161)**).
- [LongLLaMA ](https://github.com/CStanKonrad/long_llama) 
: LongLLaMA is a large language model capable of handling long contexts. It is based on OpenLLaMA and fine-tuned with the Focused Transformer (FoT) method.
- [LLaMA-Adapter](https://github.com/OpenGVLab/LLaMA-Adapter) 
: Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters. LLaMA-Adapter: Efficient Fine-tuning of LLaMA 🚀
- [DemoGPT](https://github.com/melih-unsal/DemoGPT) 
: Create 🦜️🔗 LangChain apps by just using prompts with the power of Llama 2 🌟 Star to support our work! | 只需使用句子即可创建 LangChain 应用程序。 给个star支持我们的工作吧！DemoGPT: Auto Gen-AI App Generator with the Power of Llama 2. ⚡ With just a prompt, you can create interactive Streamlit apps via 🦜️🔗 LangChain's transformative capabilities & Llama 2.⚡ [demogpt.io](https://www.demogpt.io/)
- [Lamini](https://github.com/lamini-ai/lamini) 
: Lamini: The LLM engine for rapidly customizing models 🦙
- [xorbitsai/inference](https://github.com/xorbitsai/inference) 
: Xorbits Inference (Xinference) is a powerful and versatile library designed to serve LLMs, speech recognition models, and multimodal models, even on your laptop. It supports a variety of models compatible with GGML, such as llama, chatglm, baichuan, whisper, vicuna, orac, and many others.
- [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) 
: distributed trainer for LLMs.
- [AmineDiro/cria](https://github.com/AmineDiro/cria) 
: OpenAI compatible API for serving LLAMA-2 model.
- [Llama-2-Onnx](https://github.com/microsoft/Llama-2-Onnx) 
: Llama 2 Powered By ONNX.
- [gpt-llm-trainer](https://github.com/mshumer/gpt-llm-trainer) 
: The goal of this project is to explore an experimental new pipeline to train a high-performing task-specific model. We try to abstract away all the complexity, so it's as easy as possible to go from idea -> performant fully-trained model.
- [Qwen｜通义千问](https://github.com/QwenLM/Qwen) 
: The official repo of Qwen (通义千问) chat & pretrained large language model proposed by Alibaba Cloud.
- [Qwen1.5](https://github.com/QwenLM/Qwen1.5) 
: Qwen1.5 is the improved version of Qwen, the large language model series developed by Qwen team, Alibaba Cloud.
- [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) 
: ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型。 ChatGLM-6B 是一个开源的、支持中英双语的对话语言模型，基于 [General Language Model (GLM)](https://github.com/THUDM/GLM) 架构，具有 62 亿参数。 "GLM: General Language Model Pretraining with Autoregressive Blank Infilling". (**[ACL 2022](https://aclanthology.org/2022.acl-long.26/)**). "GLM-130B: An Open Bilingual Pre-trained Model". (**[ICLR 2023](https://openreview.net/forum?id=-Aw0rrrPUF)**).
- [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B) 
: ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型。ChatGLM2-6B 是开源中英双语对话模型 ChatGLM-6B 的第二代版本，在保留了初代模型对话流畅、部署门槛较低等众多优秀特性的基础之上，ChatGLM2-6B 引入了更强大的性能、更强大的性能、更高效的推理、更开放的协议。
- [ChatGLM3](https://github.com/THUDM/ChatGLM3) 
: ChatGLM3 series: Open Bilingual Chat LLMs | 开源双语对话语言模型。
- [InternLM｜书生·浦语](https://github.com/InternLM/InternLM) 
: Official release of InternLM2 7B and 20B base and chat models. 200K context support. [internlm.intern-ai.org.cn/](https://internlm.intern-ai.org.cn/)
- [Baichuan-7B｜百川-7B](https://github.com/baichuan-inc/Baichuan-7B) 
: A large-scale 7B pretraining language model developed by BaiChuan-Inc. Baichuan-7B 是由百川智能开发的一个开源可商用的大规模预训练语言模型。基于 Transformer 结构，在大约 1.2 万亿 tokens 上训练的 70 亿参数模型，支持中英双语，上下文窗口长度为 4096。在标准的中文和英文 benchmark（C-Eval/MMLU）上均取得同尺寸最好的效果。[huggingface.co/baichuan-inc/baichuan-7B](https://huggingface.co/baichuan-inc/Baichuan-7B)
- [Baichuan-13B｜百川-13B](https://github.com/baichuan-inc/Baichuan-13B) 
: A 13B large language model developed by Baichuan Intelligent Technology. Baichuan-13B 是由百川智能继 Baichuan-7B 之后开发的包含 130 亿参数的开源可商用的大规模语言模型，在权威的中文和英文 benchmark 上均取得同尺寸最好的效果。本次发布包含有预训练 (Baichuan-13B-Base) 和对齐 (Baichuan-13B-Chat) 两个版本。[huggingface.co/baichuan-inc/Baichuan-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan-13B-Chat)
- [Baichuan2](https://github.com/baichuan-inc/Baichuan2) 
: A series of large language models developed by Baichuan Intelligent Technology. Baichuan 2 是百川智能推出的新一代开源大语言模型，采用 2.6 万亿 Tokens 的高质量语料训练。Baichuan 2 在多个权威的中文、英文和多语言的通用、领域 benchmark 上取得同尺寸最佳的效果。本次发布包含有 7B、13B 的 Base 和 Chat 版本，并提供了 Chat 版本的 4bits 量化。[huggingface.co/baichuan-inc](https://huggingface.co/baichuan-inc). "Baichuan 2: Open Large-scale Language Models". (**[arXiv 2023](https://arxiv.org/abs/2309.10305)**).
- [MOSS](https://github.com/OpenLMLab/MOSS) 
: An open-source tool-augmented conversational language model from Fudan University. MOSS是一个支持中英双语和多种插件的开源对话语言模型，moss-moon系列模型具有160亿参数，在FP16精度下可在单张A100/A800或两张3090显卡运行，在INT4/8精度下可在单张3090显卡运行。MOSS基座语言模型在约七千亿中英文以及代码单词上预训练得到，后续经过对话指令微调、插件增强学习和人类偏好训练具备多轮对话能力及使用多种插件的能力。[txsun1997.github.io/blogs/moss.html](https://txsun1997.github.io/blogs/moss.html)
- [BayLing｜百聆](https://github.com/ictnlp/BayLing) : “百聆”是一个具有增强的语言对齐的英语/中文大语言模型，具有优越的英语/中文能力，在多项测试中取得ChatGPT 90%的性能。BayLing is an English/Chinese LLM equipped with advanced language alignment, showing superior capability in English/Chinese generation, instruction following and multi-turn interaction. [nlp.ict.ac.cn/bayling](http://nlp.ict.ac.cn/bayling). "BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models". (**[arXiv 2023](https://arxiv.org/abs/2306.10968)**).
- [FlagAI｜悟道·天鹰（Aquila）](https://github.com/FlagAI-Open/FlagAI) 
: FlagAI (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model. Our goal is to support training, fine-tuning, and deployment of large-scale models on various downstream tasks with multi-modality.
- [YuLan-Chat｜玉兰](https://github.com/RUC-GSAI/YuLan-Chat/) 
: YuLan-Chat models are chat-based large language models, which are developed by the researchers in GSAI, Renmin University of China (YuLan, which represents Yulan Magnolia, is the campus flower of Renmin University of China). The newest version is developed by continually-pretraining and instruction-tuning [LLaMA-2](https://github.com/facebookresearch/llama) with high-quality English and Chinese data. YuLan-Chat系列模型是中国人民大学高瓴人工智能学院师生共同开发的支持聊天的大语言模型（名字"玉兰"取自中国人民大学校花）。 最新版本基于LLaMA-2进行了中英文双语的继续预训练和指令微调。
- [Yi-1.5](https://github.com/01-ai/Yi-1.5) 
: Yi-1.5 is an upgraded version of Yi, delivering stronger performance in coding, math, reasoning, and instruction-following capability.
- [智海-录问](https://github.com/zhihaiLLM/wisdomInterrogatory) 
: 智海-录问(wisdomInterrogatory)是由浙江大学、阿里巴巴达摩院以及华院计算三家单位共同设计研发的法律大模型。核心思想：以“普法共享和司法效能提升”为目标，从推动法律智能化体系入司法实践、数字化案例建设、虚拟法律咨询服务赋能等方面提供支持，形成数字化和智能化的司法基座能力。
- [活字](https://github.com/HIT-SCIR/huozi) 
: 活字是由哈工大自然语言处理研究所多位老师和学生参与开发的一个开源可商用的大规模预训练语言模型。 该模型基于 Bloom 结构的70 亿参数模型，支持中英双语，上下文窗口长度为 2048。 在标准的中文和英文基准以及主观评测上均取得同尺寸中优异的结果。
- [MiLM-6B](https://github.com/XiaoMi/MiLM-6B) 
: MiLM-6B 是由小米开发的一个大规模预训练语言模型，参数规模为64亿。在 C-Eval 和 CMMLU 上均取得同尺寸最好的效果。
- [Chinese LLaMA and Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) 
: 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)。"Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca". (**[arXiv 2023](https://arxiv.org/abs/2304.08177)**).
- [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) 
: 中文 LLaMA-2 & Alpaca-2 大模型二期项目 (Chinese LLaMA-2 & Alpaca-2 LLMs).
- [FlagAlpha/Llama2-Chinese](https://github.com/FlagAlpha/Llama2-Chinese) 
: Llama中文社区，最好的中文Llama大模型，完全开源可商用。
- [michael-wzhu/Chinese-LlaMA2](https://github.com/michael-wzhu/Chinese-LlaMA2) 
: Repo for adapting Meta LlaMA2 in Chinese! META最新发布的LlaMA2的汉化版！ （完全开源可商用）
- [CPM-Bee](https://github.com/OpenBMB/CPM-Bee) 
: CPM-Bee是一个完全开源、允许商用的百亿参数中英文基座模型，也是[CPM-Live](https://live.openbmb.org/)训练的第二个里程碑。
- [PandaLM](https://github.com/WeOpenML/PandaLM) 
: PandaLM: Reproducible and Automated Language Model Assessment.
- [SpeechGPT](https://github.com/0nutation/SpeechGPT) 
: "SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities". (**[arXiv 2023](https://arxiv.org/abs/2305.11000)**).
- [GPT2-Chinese](https://github.com/Morizeyao/GPT2-Chinese) 
: Chinese version of GPT2 training code, using BERT tokenizer.
- [Chinese-Tiny-LLM](https://github.com/Chinese-Tiny-LLM/Chinese-Tiny-LLM) 
: "Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model". (**[arXiv 2024](https://arxiv.org/abs/2404.04167)**).
- [潘多拉 (Pandora)](https://github.com/pengzhile/pandora) 
: 潘多拉，一个让你呼吸顺畅的ChatGPT。Pandora, a ChatGPT that helps you breathe smoothly.
- [百度-文心大模型](https://wenxin.baidu.com/) : 百度全新一代知识增强大语言模型，文心大模型家族的新成员，能够与人对话互动，回答问题，协助创作，高效便捷地帮助人们获取信息、知识和灵感。
- [百度智能云-千帆大模型](https://cloud.baidu.com/product/wenxinworkshop) : 百度智能云千帆大模型平台一站式企业级大模型平台，提供先进的生成式AI生产及应用全流程开发工具链。
- [华为云-盘古大模型](https://www.huaweicloud.com/product/pangu.html) : 盘古大模型致力于深耕行业，打造金融、政务、制造、矿山、气象、铁路等领域行业大模型和能力集，将行业知识know-how与大模型能力相结合，重塑千行百业，成为各组织、企业、个人的专家助手。"Accurate medium-range global weather forecasting with 3D neural networks". (**[Nature 2023](https://www.nature.com/articles/s41586-023-06185-3)**).
- [商汤科技-日日新SenseNova](https://techday.sensetime.com/?utm_source=baidu-sem-pc&utm_medium=cpc&utm_campaign=PC-%E6%8A%80%E6%9C%AF%E4%BA%A4%E6%B5%81%E6%97%A5-%E4%BA%A7%E5%93%81%E8%AF%8D-%E6%97%A5%E6%97%A5%E6%96%B0&utm_content=%E6%97%A5%E6%97%A5%E6%96%B0&utm_term=%E6%97%A5%E6%97%A5%E6%96%B0SenseNova&e_creative=73937788324&e_keywordid=594802524403) : 日日新（SenseNova），是商汤科技宣布推出的大模型体系，包括自然语言处理模型“商量”（SenseChat）、文生图模型“秒画”和数字人视频生成平台“如影”（SenseAvatar）等。
- [科大讯飞-星火认知大模型](https://xinghuo.xfyun.cn/) : 新一代认知智能大模型，拥有跨领域知识和语言理解能力，能够基于自然对话方式理解与执行任务。
- [字节跳动-豆包](https://www.doubao.com/) : 豆包。
- [CrazyBoyM/llama3-Chinese-chat](https://github.com/CrazyBoyM/llama3-Chinese-chat) 
: Llama3 中文版。
- ##### Vision Foundation Model
###### 视觉大模型（VFM）
- [Visual ChatGPT](https://github.com/microsoft/visual-chatgpt) 
: Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. "Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models". (**[arXiv 2023](https://arxiv.org/abs/2303.04671)**).
- [InternImage](https://github.com/OpenGVLab/InternImage) 
: "InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions". (**[CVPR 2023](https://arxiv.org/abs/2211.05778)**).
- [GLIP](https://github.com/microsoft/GLIP) 
: "Grounded Language-Image Pre-training". (**[CVPR 2022](https://arxiv.org/abs/2112.03857)**).
- [GLIPv2](https://github.com/microsoft/GLIP) : "GLIPv2: Unifying Localization and Vision-Language Understanding". (**[arXiv 2022](https://arxiv.org/abs/2206.05836)**).
- [DINO](https://github.com/IDEA-Research/DINO) 
: "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection". (**[ICLR 2023](https://arxiv.org/abs/2203.03605)**).
- [DINOv2](https://github.com/facebookresearch/dinov2) 
: "DINOv2: Learning Robust Visual Features without Supervision". (**[arXiv 2023](https://arxiv.org/abs/2304.07193)**).
- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) 
: "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection". (**[arXiv 2023](https://arxiv.org/abs/2303.05499)**). "知乎「三分钟热度」《[十分钟解读Grounding DINO-根据文字提示检测任意目标](https://zhuanlan.zhihu.com/p/627646794)》"。
- [SAM](https://github.com/facebookresearch/segment-anything) 
: The repository provides code for running inference with the Segment Anything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model. "Segment Anything". (**[arXiv 2023](https://arxiv.org/abs/2304.02643)**).
- [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) 
: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Tag2Text & BLIP & Whisper & ChatBot - Automatically Detect , Segment and Generate Anything with Image, Text, and Audio Inputs. We plan to create a very interesting demo by combining [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) and [Segment Anything](https://github.com/facebookresearch/segment-anything) which aims to detect and segment Anything with text inputs!
- [SEEM](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once) 
: We introduce SEEM that can Segment Everything Everywhere with Multi-modal prompts all at once. SEEM allows users to easily segment an image using prompts of different types including visual prompts (points, marks, boxes, scribbles and image segments) and language prompts (text and audio), etc. It can also work with any combinations of prompts or generalize to custom prompts! "Segment Everything Everywhere All at Once". (**[arXiv 2023](https://arxiv.org/abs/2304.06718)**).
- [SAM3D](https://github.com/DYZhang09/SAM3D) 
: "SAM3D: Zero-Shot 3D Object Detection via [Segment Anything](https://github.com/facebookresearch/segment-anything) Model". (**[arXiv 2023](https://arxiv.org/abs/2306.02245)**).
- [ImageBind](https://github.com/facebookresearch/ImageBind) 
: "ImageBind: One Embedding Space To Bind Them All". (**[CVPR 2023](https://arxiv.org/abs/2305.05665)**).
- [Track-Anything](https://github.com/gaomingqi/Track-Anything) 
: Track-Anything is a flexible and interactive tool for video object tracking and segmentation, based on Segment Anything, XMem, and E2FGVI. "Track Anything: Segment Anything Meets Videos". (**[arXiv 2023](https://arxiv.org/abs/2304.11968)**).
- [qianqianwang68/omnimotion](https://github.com/qianqianwang68/omnimotion) 
: "Tracking Everything Everywhere All at Once". (**[arXiv 2023](https://arxiv.org/abs/2306.05422)**).
- [LLaVA](https://github.com/haotian-liu/LLaVA) 
: 🌋 LLaVA: Large Language and Vision Assistant. Visual instruction tuning towards large language and vision models with GPT-4 level capabilities. [llava.hliu.cc](https://llava.hliu.cc/). "Visual Instruction Tuning". (**[arXiv 2023](https://arxiv.org/abs/2304.08485)**).
- [M3I-Pretraining](https://github.com/OpenGVLab/M3I-Pretraining) 
: "Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information". (**[arXiv 2022](https://arxiv.org/abs/2211.09807)**).
- [BEVFormer](https://github.com/fundamentalvision/BEVFormer) 
: BEVFormer: a Cutting-edge Baseline for Camera-based Detection. "BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers". (**[arXiv 2022](https://arxiv.org/abs/2203.17270)**).
- [Uni-Perceiver](https://github.com/fundamentalvision/Uni-Perceiver) 
: "Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Uni-Perceiver_Pre-Training_Unified_Architecture_for_Generic_Perception_for_Zero-Shot_and_CVPR_2022_paper.html)**).
- [AnyLabeling](https://github.com/vietanhdev/anylabeling) 
: 🌟 AnyLabeling 🌟. Effortless data labeling with AI support from YOLO and Segment Anything! Effortless data labeling with AI support from YOLO and Segment Anything!
- [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling) 
: 💫 X-AnyLabeling 💫. Effortless data labeling with AI support from Segment Anything and other awesome models!
- [Label Anything](https://github.com/open-mmlab/playground/tree/main/label_anything) 
: OpenMMLab PlayGround: Semi-Automated Annotation with Label-Studio and SAM.
- [RevCol](https://github.com/megvii-research/RevCol) 
: "Reversible Column Networks". (**[arXiv 2023](https://arxiv.org/abs/2212.11696)**).
- [Macaw-LLM](https://github.com/lyuchenyang/Macaw-LLM) 
: Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration.
- [SAM-PT](https://github.com/SysCV/sam-pt) 
: SAM-PT: Extending SAM to zero-shot video segmentation with point-based tracking. "Segment Anything Meets Point Tracking". (**[arXiv 2023](https://arxiv.org/abs/2307.01197)**).
- [Video-LLaMA](https://github.com/DAMO-NLP-SG/Video-LLaMA) 
: "Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding". (**[arXiv 2023](https://arxiv.org/abs/2306.02858)**).
- [MobileSAM](https://github.com/ChaoningZhang/MobileSAM) 
: "Faster Segment Anything: Towards Lightweight SAM for Mobile Applications". (**[arXiv 2023](https://arxiv.org/abs/2306.14289)**).
- [BuboGPT](https://github.com/magic-research/bubogpt) 
: "BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs". (**[arXiv 2023](https://arxiv.org/abs/2307.08581)**).
- ##### AI Generated Content
###### 人工智能生成内容（AIGC）
- [Sora](https://openai.com/sora) : Sora is an AI model that can create realistic and imaginative scenes from text instructions.
- [Open Sora Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan) 
: This project aim to reproducing [Sora](https://openai.com/sora) (Open AI T2V model), but we only have limited resource. We deeply wish the all open source community can contribute to this project. 本项目希望通过开源社区的力量复现Sora，由北大-兔展AIGC联合实验室共同发起，当前我们资源有限仅搭建了基础架构，无法进行完整训练，希望通过开源社区逐步增加模块并筹集资源进行训练，当前版本离目标差距巨大，仍需持续完善和快速迭代，欢迎Pull request！！！[Project Page](https://pku-yuangroup.github.io/Open-Sora-Plan/) [中文主页](https://pku-yuangroup.github.io/Open-Sora-Plan/blog_cn.html)
- [Mini Sora](https://github.com/mini-sora/minisora) 
: The Mini Sora project aims to explore the implementation path and future development direction of Sora.
- [EMO](https://github.com/HumanAIGC/EMO) 
: "EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions". (**[arXiv 2024](https://arxiv.org/abs/2402.17485)**).
- [Stable Diffusion](https://github.com/CompVis/stable-diffusion) 
: Stable Diffusion is a latent text-to-image diffusion model. Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work "High-Resolution Image Synthesis with Latent Diffusion Models". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html)**).
- [Stable Diffusion Version 2](https://github.com/Stability-AI/stablediffusion) 
: This repository contains [Stable Diffusion](https://github.com/CompVis/stable-diffusion) models trained from scratch and will be continuously updated with new checkpoints. "High-Resolution Image Synthesis with Latent Diffusion Models". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html)**).
- [StableStudio](https://github.com/Stability-AI/StableStudio) 
: StableStudio by [Stability AI](https://stability.ai/). 👋 Welcome to the community repository for StableStudio, the open-source version of [DreamStudio](https://dreamstudio.ai/).
- [AudioCraft](https://github.com/facebookresearch/audiocraft) 
: Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.
- [InvokeAI](https://github.com/invoke-ai/InvokeAI) 
: Invoke AI - Generative AI for Professional Creatives. Professional Creative Tools for Stable Diffusion, Custom-Trained Models, and more. [invoke-ai.github.io/InvokeAI/](https://invoke-ai.github.io/InvokeAI/)
- [DragGAN](https://github.com/XingangPan/DragGAN) 
: "Stable Diffusion Training with MosaicML. This repo contains code used to train your own Stable Diffusion model on your own data". (**[SIGGRAPH 2023](https://vcai.mpi-inf.mpg.de/projects/DragGAN/)**).
- [AudioGPT](https://github.com/AIGC-Audio/AudioGPT) 
: AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head.
- [PandasAI](https://github.com/gventuri/pandas-ai) 
: Pandas AI is a Python library that adds generative artificial intelligence capabilities to Pandas, the popular data analysis and manipulation tool. It is designed to be used in conjunction with Pandas, and is not a replacement for it.
- [mosaicml/diffusion](https://github.com/mosaicml/diffusion) 
: Stable Diffusion Training with MosaicML. This repo contains code used to train your own Stable Diffusion model on your own data.
- [VisorGPT](https://github.com/Sierkinhane/VisorGPT) 
: Customize spatial layouts for conditional image synthesis models, e.g., ControlNet, using GPT. "VisorGPT: Learning Visual Prior via Generative Pre-Training". (**[arXiv 2023](https://arxiv.org/abs/2305.13777)**).
- [ControlNet](https://github.com/lllyasviel/ControlNet) 
: Let us control diffusion models! "Adding Conditional Control to Text-to-Image Diffusion Models". (**[arXiv 2023](https://arxiv.org/abs/2302.05543)**).
- [Fooocus](https://github.com/lllyasviel/Fooocus) 
: Fooocus is an image generating software. Fooocus is a rethinking of Stable Diffusion and Midjourney’s designs. "微信公众号「GitHubStore」《[Fooocus : 集Stable Diffusion 和 Midjourney 优点于一身的开源AI绘图软件](https://mp.weixin.qq.com/s/adyXek6xcz5aOPAGqZBrvg)》"。
- [MindDiffuser](https://github.com/ReedOnePeck/MindDiffuser) 
: "MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion". (**[arXiv 2023](https://arxiv.org/abs/2308.04249)**).
- [Midjourney](https://www.midjourney.com/) : Midjourney is an independent research lab exploring new mediums of thought and expanding the imaginative powers of the human species.
- [DreamStudio](https://dreamstudio.ai/) : Effortless image generation for creators with big dreams.
- [Firefly](https://www.adobe.com/sensei/generative-ai/firefly.html) : Adobe Firefly: Experiment, imagine, and make an infinite range of creations with Firefly, a family of creative generative AI models coming to Adobe products.
- [Jasper](https://www.jasper.ai/) : Meet Jasper. On-brand AI content wherever you create.
- [Copy.ai](https://www.copy.ai/) : Whatever you want to ask, our chat has the answers.
- [Peppertype.ai](https://www.peppercontent.io/peppertype-ai/) : Leverage the AI-powered platform to ideate, create, distribute, and measure your content and prove your content marketing ROI.
- [ChatPPT](https://chat-ppt.com/) : ChatPPT来袭命令式一键生成PPT。
- #### Application Development Platform
##### 应用程序开发平台
- [LangChain](https://github.com/langchain-ai/langchain) 
: 🦜️🔗 LangChain. ⚡ Building applications with LLMs through composability ⚡ [python.langchain.com](https://python.langchain.com/docs/get_started/introduction.html)
- [Dify](https://github.com/langgenius/dify) 
: An Open-Source Assistants API and GPTs alternative. Dify.AI is an LLM application development platform. It integrates the concepts of Backend as a Service and LLMOps, covering the core tech stack required for building generative AI-native applications, including a built-in RAG engine. [dify.ai](https://dify.ai/)
- [AutoChain](https://github.com/Forethought-Technologies/AutoChain) 
: AutoChain: Build lightweight, extensible, and testable LLM Agents. [autochain.forethought.ai](https://autochain.forethought.ai/)
- [Auto-GPT](https://github.com/Significant-Gravitas/Auto-GPT) 
: Auto-GPT: An Autonomous GPT-4 Experiment. Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, Auto-GPT pushes the boundaries of what is possible with AI. [agpt.co](https://news.agpt.co/)
- [LiteChain](https://github.com/rogeriochaves/litechain) 
: Build robust LLM applications with true composability 🔗. [rogeriochaves.github.io/litechain/](https://rogeriochaves.github.io/litechain/)
- [Open-Assistant](https://github.com/LAION-AI/Open-Assistant) 
: OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so. [open-assistant.io](https://open-assistant.io/)
- #### Fine-Tuning Framework
##### 微调框架
- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) 
: Unify Efficient Fine-Tuning of 100+ LLMs. Fine-tuning a large language model can be easy as...
- #### RAG Framework
##### 检索增强生成框架
- [LlamaIndex](https://github.com/run-llama/llama_index) 
: LlamaIndex is a data framework for your LLM applications. [docs.llamaindex.ai](https://docs.llamaindex.ai/)
- [Embedchain](https://github.com/embedchain/embedchain) 
: The Open Source RAG framework. [docs.embedchain.ai](https://docs.embedchain.ai/)
- [QAnything](https://github.com/netease-youdao/QAnything) 
: Question and Answer based on Anything. [qanything.ai](https://qanything.ai/)
- [R2R](https://github.com/SciPhi-AI/R2R) 
: A framework for rapid development and deployment of production-ready RAG systems. [docs.sciphi.ai](https://docs.sciphi.ai/)
- [langchain-ai/rag-from-scratch](https://github.com/langchain-ai/rag-from-scratch) 
: Retrieval augmented generation (RAG) comes is a general methodology for connecting LLMs with external data sources. These notebooks accompany a video series will build up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation.
- #### LLM Inference Framework
##### 大语言模型推理框架
- ##### LLM Inference Benchmark
- [ninehills/llm-inference-benchmark](https://github.com/ninehills/llm-inference-benchmark) 
: LLM Inference benchmark.
- ##### LLM Deployment Engine
- [vllm-project/vllm](https://github.com/vllm-project/vllm) 
: A high-throughput and memory-efficient inference and serving engine for LLMs. [vllm.readthedocs.io](https://vllm.readthedocs.io/en/latest/)
- [MLC LLM](https://github.com/mlc-ai/mlc-llm) 
: Enable everyone to develop, optimize and deploy AI models natively on everyone's devices. [mlc.ai/mlc-llm](https://mlc.ai/mlc-llm/)
- [Lamini](https://github.com/lamini-ai/lamini) : Lamini: The LLM engine for rapidly customizing models 🦙.
- [datawhalechina/self-llm](https://github.com/datawhalechina/self-llm) 
: 《开源大模型食用指南》基于Linux环境快速部署开源大模型，更适合中国宝宝的部署教程。
- ##### C Implementation
- [llm.c](https://github.com/karpathy/llm.c) 
: LLM training in simple, pure C/CUDA. There is no need for 245MB of PyTorch or 107MB of cPython. For example, training GPT-2 (CPU, fp32) is ~1,000 lines of clean code in a single file. It compiles and runs instantly, and exactly matches the PyTorch reference implementation.
- [llama2.c](https://github.com/karpathy/llama2.c) 
: Inference Llama 2 in one file of pure C. Train the Llama 2 LLM architecture in PyTorch then inference it with one simple 700-line C file (run.c).
- ##### CPP Implementation
- [TensorRT](https://github.com/NVIDIA/TensorRT) 
: NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT. [developer.nvidia.com/tensorrt](https://developer.nvidia.com/tensorrt)
- [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) 
: TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines. [nvidia.github.io/TensorRT-LLM](https://nvidia.github.io/TensorRT-LLM)
- [gemma.cpp](https://github.com/google/gemma.cpp) 
: gemma.cpp is a lightweight, standalone C++ inference engine for the Gemma foundation models from Google.
- [llama.cpp](https://github.com/ggerganov/llama.cpp) 
: Inference of [LLaMA](https://github.com/facebookresearch/llama) model in pure C/C++.
- [whisper.cpp](https://github.com/ggerganov/whisper.cpp) 
: High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model.
- [ChatGLM.cpp](https://github.com/li-plus/chatglm.cpp) 
: C++ implementation of [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) and [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B).
- [MegEngine/InferLLM](https://github.com/MegEngine/InferLLM) 
: InferLLM is a lightweight LLM model inference framework that mainly references and borrows from the llama.cpp project.
- [DeployAI/nndeploy](https://github.com/DeployAI/nndeploy) 
: nndeploy是一款模型端到端部署框架。以多端推理以及基于有向无环图模型部署为内核，致力为用户提供跨平台、简单易用、高性能的模型部署体验。[nndeploy-zh.readthedocs.io/zh/latest/](https://nndeploy-zh.readthedocs.io/zh/latest/)
- [zjhellofss/KuiperInfer (自制深度学习推理框架)](https://github.com/zjhellofss/KuiperInfer) 
: 带你从零实现一个高性能的深度学习推理库，支持llama 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step.
- [skeskinen/llama-lite](https://github.com/skeskinen/llama-lite) 
: Embeddings focused small version of Llama NLP model.
- [Const-me/Whisper](https://github.com/Const-me/Whisper) 
: High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model.
- [wangzhaode/ChatGLM-MNN](https://github.com/wangzhaode/ChatGLM-MNN) 
: Pure C++, Easy Deploy ChatGLM-6B.
- [ztxz16/fastllm](https://github.com/ztxz16/fastllm) 
: 纯c++实现，无第三方依赖的大模型库，支持CUDA加速，目前支持国产大模型ChatGLM-6B，MOSS; 可以在安卓设备上流畅运行ChatGLM-6B。
- [davidar/eigenGPT](https://github.com/davidar/eigenGPT) 
: Minimal C++ implementation of GPT2.
- [Tlntin/Qwen-TensorRT-LLM](https://github.com/Tlntin/Qwen-TensorRT-LLM) 
: 使用TRT-LLM完成对Qwen-7B-Chat实现推理加速。
- [FeiGeChuanShu/trt2023](https://github.com/FeiGeChuanShu/trt2023) 
: NVIDIA TensorRT Hackathon 2023复赛选题：通义千问Qwen-7B用TensorRT-LLM模型搭建及优化。
- [TRT2022/trtllm-llama](https://github.com/TRT2022/trtllm-llama) 
: ☢️ TensorRT 2023复赛——基于TensorRT-LLM的Llama模型推断加速优化。
- [AmeyaWagh/llama2.cpp](https://github.com/AmeyaWagh/llama2.cpp) 
: Inference Llama 2 in C++.
- ##### Python Implementation
- [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) 
: Python bindings for llama.cpp. [llama-cpp-python.readthedocs.io](https://llama-cpp-python.readthedocs.io/)
- [ggml-python](https://github.com/abetlen/ggml-python) 
: Python bindings for ggml. [ggml-python.readthedocs.io](https://ggml-python.readthedocs.io/)
- ##### Mojo Implementation
- [llama2.mojo](https://github.com/tairov/llama2.mojo) 
: Inference Llama 2 in one file of pure 🔥
- [dorjeduck/llm.mojo](https://github.com/dorjeduck/llm.mojo) 
: port of Andrjey Karpathy's llm.c to Mojo.
- ##### Rust Implementation
- [Candle](https://github.com/huggingface/candle) 
: Minimalist ML framework for Rust.
- [Safetensors](https://github.com/huggingface/safetensors) 
: Simple, safe way to store and distribute tensors. [huggingface.co/docs/safetensors](https://huggingface.co/docs/safetensors/index)
- [Tokenizers](https://github.com/huggingface/tokenizers) 
: 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production. [huggingface.co/docs/tokenizers](https://huggingface.co/docs/tokenizers/index)
- [Burn](https://github.com/burn-rs/burn) 
: Burn - A Flexible and Comprehensive Deep Learning Framework in Rust. [burn-rs.github.io/](https://burn-rs.github.io/)
- [dfdx](https://github.com/coreylowman/dfdx) 
: Deep learning in Rust, with shape checked tensors and neural networks.
- [luminal](https://github.com/jafioti/luminal) 
: Deep learning at the speed of light. [www.luminalai.com/](https://www.luminalai.com/)
- [crabml](https://github.com/crabml/crabml) 
: crabml is focusing on the reimplementation of GGML using the Rust programming language.
- [TensorFlow Rust](https://github.com/tensorflow/rust) 
: Rust language bindings for TensorFlow.
- [tch-rs](https://github.com/LaurentMazare/tch-rs) 
: Rust bindings for the C++ api of PyTorch.
- [rustai-solutions/candle_demo_openchat_35](https://github.com/rustai-solutions/candle_demo_openchat_35) 
: candle_demo_openchat_35.
- [llama2.rs](https://github.com/srush/llama2.rs) 
: A fast llama2 decoder in pure Rust.
- [Llama2-burn](https://github.com/Gadersd/llama2-burn) 
: Llama2 LLM ported to Rust burn.
- [gaxler/llama2.rs](https://github.com/gaxler/llama2.rs) 
: Inference Llama 2 in one file of pure Rust 🦀
- [whisper-burn](https://github.com/Gadersd/whisper-burn) 
: A Rust implementation of OpenAI's Whisper model using the burn framework.
- [stable-diffusion-burn](https://github.com/Gadersd/stable-diffusion-burn) 
: Stable Diffusion v1.4 ported to Rust's burn framework.
- [coreylowman/llama-dfdx](https://github.com/coreylowman/llama-dfdx) 
: [LLaMa 7b](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) with CUDA acceleration implemented in rust. Minimal GPU memory needed!
- [tazz4843/whisper-rs](https://github.com/tazz4843/whisper-rs) 
: Rust bindings to [whisper.cpp](https://github.com/ggerganov/whisper.cpp).
- [rustformers/llm](https://github.com/rustformers/llm) 
: Run inference for Large Language Models on CPU, with Rust 🦀🚀🦙.
- [Chidori](https://github.com/ThousandBirdsInc/chidori) 
: A reactive runtime for building durable AI agents. [docs.thousandbirds.ai](https://docs.thousandbirds.ai/).
- [llm-chain](https://github.com/sobelio/llm-chain) 
: llm-chain is a collection of Rust crates designed to help you work with Large Language Models (LLMs) more effectively. [llm-chain.xyz](https://llm-chain.xyz/)
- [Abraxas-365/langchain-rust](https://github.com/Abraxas-365/langchain-rust) 
: 🦜️🔗LangChain for Rust, the easiest way to write LLM-based programs in Rust.
- [Atome-FE/llama-node](https://github.com/Atome-FE/llama-node) 
: Believe in AI democratization. llama for nodejs backed by llama-rs and llama.cpp, work locally on your laptop CPU. support llama/alpaca/gpt4all/vicuna model. [www.npmjs.com/package/llama-node](https://www.npmjs.com/package/llama-node)
- [Noeda/rllama](https://github.com/Noeda/rllama) 
: Rust+OpenCL+AVX2 implementation of LLaMA inference code.
- [lencx/ChatGPT](https://github.com/lencx/ChatGPT) 
: 🔮 ChatGPT Desktop Application (Mac, Windows and Linux). [NoFWL](https://app.nofwl.com/).
- [Synaptrix/ChatGPT-Desktop](https://github.com/Synaptrix/ChatGPT-Desktop) 
: Fuel your productivity with ChatGPT-Desktop - Blazingly fast and supercharged!
- [Poordeveloper/chatgpt-app](https://github.com/Poordeveloper/chatgpt-app) 
: A ChatGPT App for all platforms. Built with Rust + Tauri + Vue + Axum.
- [mxismean/chatgpt-app](https://github.com/mxismean/chatgpt-app) 
: Tauri 项目：ChatGPT App.
- [sonnylazuardi/chat-ai-desktop](https://github.com/sonnylazuardi/chat-ai-desktop) 
: Chat AI Desktop App. Unofficial ChatGPT desktop app for Mac & Windows menubar using Tauri & Rust.
- [yetone/openai-translator](https://github.com/yetone/openai-translator) 
: The translator that does more than just translation - powered by OpenAI.
- [m1guelpf/browser-agent](https://github.com/m1guelpf/browser-agent) 
: A browser AI agent, using GPT-4. [docs.rs/browser-agent](https://docs.rs/browser-agent/latest/browser_agent/)
- [sigoden/aichat](https://github.com/sigoden/aichat) 
: Using ChatGPT/GPT-3.5/GPT-4 in the terminal.
- [uiuifree/rust-openai-chatgpt-api](https://github.com/uiuifree/rust-openai-chatgpt-api) 
: "rust-openai-chatgpt-api" is a Rust library for accessing the ChatGPT API, a powerful NLP platform by OpenAI. The library provides a simple and efficient interface for sending requests and receiving responses, including chat. It uses reqwest and serde for HTTP requests and JSON serialization.
- [1595901624/gpt-aggregated-edition](https://github.com/1595901624/gpt-aggregated-edition) : 聚合ChatGPT官方版、ChatGPT免费版、文心一言、Poe、chatchat等多平台，支持自定义导入平台。
- [Cormanz/smartgpt](https://github.com/Cormanz/smartgpt) 
: A program that provides LLMs with the ability to complete complex tasks using plugins.
- [femtoGPT](https://github.com/keyvank/femtoGPT) 
: femtoGPT is a pure Rust implementation of a minimal Generative Pretrained Transformer. [discord.gg/wTJFaDVn45](https://github.com/keyvank/femtoGPT)
- [shafishlabs/llmchain-rs](https://github.com/shafishlabs/llmchain-rs) 
: 🦀Rust + Large Language Models - Make AI Services Freely and Easily. Inspired by LangChain.
- [flaneur2020/llama2.rs](https://github.com/flaneur2020/llama2.rs) 
: An rust reimplementatin of [https://github.com/karpathy/llama2.c](https://github.com/karpathy/llama2.c).
- [Heng30/chatbox](https://github.com/Heng30/chatbox) 
: A Chatbot for OpenAI ChatGPT. Based on Slint-ui and Rust.
- [fairjm/dioxus-openai-qa-gui](https://github.com/fairjm/dioxus-openai-qa-gui) 
: a simple openai qa desktop app built with dioxus.
- [purton-tech/bionicgpt](https://github.com/purton-tech/bionicgpt) 
: Accelerate LLM adoption in your organisation. Chat with your confidential data safely and securely. [bionic-gpt.com](https://bionic-gpt.com/)
- [InfiniTensor/transformer-rs](https://github.com/InfiniTensor/transformer-rs) 
: 从 [YdrMaster/llama2.rs](https://github.com/YdrMaster/llama2.rs) 发展来的手写 transformer 模型项目。
- #### Zig Implementation
- [llama2.zig](https://github.com/cgbur/llama2.zig) 
: Inference Llama 2 in one file of pure Zig.
- [renerocksai/gpt4all.zig](https://github.com/renerocksai/gpt4all.zig) 
: ZIG build for a terminal-based chat client for an assistant-style large language model with ~800k GPT-3.5-Turbo Generations based on LLaMa.
- [EugenHotaj/zig_inference](https://github.com/EugenHotaj/zig_inference) 
: Neural Network Inference Engine in Zig.
- ##### Go Implementation
- [Ollama](https://github.com/ollama/ollama/) 
: Get up and running with Llama 2, Mistral, Gemma, and other large language models. [ollama.com](https://ollama.com/)
- [go-skynet/LocalAI](https://github.com/go-skynet/LocalAI) 
: 🤖 Self-hosted, community-driven, local OpenAI-compatible API. Drop-in replacement for OpenAI running LLMs on consumer-grade hardware. Free Open Source OpenAI alternative. No GPU required. LocalAI is an API to run ggml compatible models: llama, gpt4all, rwkv, whisper, vicuna, koala, gpt4all-j, cerebras, falcon, dolly, starcoder, and many other. [localai.io](https://localai.io/)
- #### Vector Database
##### 向量数据库
- [Qdrant](https://github.com/milvus-io/milvus) 
: Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. [milvus.io](https://milvus.io/)
- [Qdrant](https://github.com/qdrant/qdrant) 
: Qdrant - Vector Database for the next generation of AI applications. Also available in the cloud [https://cloud.qdrant.io/](https://cloud.qdrant.io/). [qdrant.tech](https://qdrant.tech/)
- ### Awesome List
- [Hannibal046/Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM) 
: Awesome-LLM: a curated list of Large Language Model.
- [DefTruth/Awesome-LLM-Inference](https://github.com/DefTruth/Awesome-LLM-Inference) 
: 📖A curated list of Awesome LLM Inference Paper with codes, TensorRT-LLM, vLLM, streaming-llm, AWQ, SmoothQuant, WINT8/4, Continuous Batching, FlashAttention, PagedAttention etc.
- [RUCAIBox/LLMSurvey](https://github.com/RUCAIBox/LLMSurvey) 
: The official GitHub page for the survey paper "A Survey of Large Language Models". (**[arXiv 2023](https://arxiv.org/abs/2303.18223)**). " 微信公众号「RUC AI Box」《[大模型综述升级啦](https://mp.weixin.qq.com/s/9YMUSSrGLSBKMFY3JYlaoQ)》"。
- [jxzhangjhu/Awesome-LLM-RAG](https://github.com/jxzhangjhu/Awesome-LLM-RAG) 
: Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models.
- [vince-lam/awesome-local-llms](https://github.com/vince-lam/awesome-local-llms) 
: Compare open-source local LLM inference projects by their metrics to assess popularity and activeness.
- [BradyFU/Awesome-Multimodal-Large-Language-Models](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) 
: ✨✨Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation. "A Survey on Multimodal Large Language Models". (**[arXiv 2023](https://arxiv.org/abs/2306.13549)**). " 微信公众号「我爱计算机视觉」《[中科大腾讯发布首篇《多模态大语言模型综述》](https://mp.weixin.qq.com/s/IiPZWEVdAJ4xrlgyWtDwng)》"。
- [hymie122/RAG-Survey](https://github.com/hymie122/RAG-Survey) 
: 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". (**[arXiv 2024](https://arxiv.org/abs/2402.19473)**). " 微信公众号「数智笔记」《[2024检索增强生成RAG最新综述](https://mp.weixin.qq.com/s/F-shRy1m7wQIS87ujOS7Dw)》"。
- [eugeneyan/open-llms](https://github.com/eugeneyan/open-llms) 
: 📋 A list of open LLMs available for commercial use.
- [formulahendry/awesome-gpt](https://github.com/formulahendry/awesome-gpt) 
: A curated list of awesome projects and resources related to GPT, ChatGPT, OpenAI, LLM, and more.
- [HqWu-HITCS/Awesome-Chinese-LLM](https://github.com/HqWu-HITCS/Awesome-Chinese-LLM) 
: 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。
- [cedrickchee/awesome-transformer-nlp](https://github.com/cedrickchee/awesome-transformer-nlp) 
: A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning.
- [GT-RIPL/Awesome-LLM-Robotics](https://github.com/GT-RIPL/Awesome-LLM-Robotics) 
: A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites.
- [mikhail-bot/awesome-gpt3](https://github.com/mikhail-bot/awesome-gpt3) 
:A Curated list of awesome GPT3 tools, libraries and resources.
- [imaurer/awesome-decentralized-llm](https://github.com/imaurer/awesome-decentralized-llm) 
: Repos and resources for running LLMs locally. (e.g. LLaMA, Cerebras, RWKV).
- [csbl-br/awesome-compbio-chatgpt](https://github.com/csbl-br/awesome-compbio-chatgpt) 
: An awesome repository of community-curated applications of ChatGPT and other LLMs in computational biology!
- [atfortes/LLM-Reasoning-Papers](https://github.com/atfortes/LLM-Reasoning-Papers) 
: Collection of papers and resources on Reasoning in Large Language Models (LLMs), including Chain-of-Thought (CoT), Instruction-Tuning, and others.
- [yzfly/Awesome-AGI](https://github.com/yzfly/Awesome-AGI) 
: A curated list of awesome AGI frameworks, software and resources.
- [steven2358/awesome-generative-ai](https://github.com/steven2358/awesome-generative-ai) 
: A curated list of modern Generative Artificial Intelligence projects and services.
- [wshzd/Awesome-AIGC](https://github.com/wshzd/Awesome-AIGC) 
: AIGC资料汇总学习，持续更新......
- [doanbactam/awesome-stable-diffusion](https://github.com/doanbactam/awesome-stable-diffusion) 
: A curated list of awesome stable diffusion resources 🌟
- [Yutong-Zhou-cv/Awesome-Text-to-Image](https://github.com/Yutong-Zhou-cv/Awesome-Text-to-Image) 
: (ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
- [SeedV/generative-ai-roadmap](https://github.com/SeedV/generative-ai-roadmap) 
: 生成式AI的应用路线图 The roadmap of generative AI: use cases and applications.
- [luban-agi/Awesome-AIGC-Tutorials](https://github.com/luban-agi/Awesome-AIGC-Tutorials) 
: Curated tutorials and resources for Large Language Models, AI Painting, and more.
- [xx025/carrot](https://github.com/xx025/carrot) 
: Free ChatGPT Site List. [cc.ai55.cc](https://cc.ai55.cc/)
- [LiLittleCat/awesome-free-chatgpt](https://github.com/LiLittleCat/awesome-free-chatgpt) 
: 🆓免费的 ChatGPT 镜像网站列表，持续更新。List of free ChatGPT mirror sites, continuously updated.
- [lzwme/chatgpt-sites](https://github.com/lzwme/chatgpt-sites) 
: 搜集国内可用的 ChatGPT 在线体验免费网站列表。定时任务每日更新。[lzw.me/x/chatgpt-sites/](https://lzw.me/x/chatgpt-sites/)
- ### Paper Overview
- [RUCAIBox/LLMSurvey](https://github.com/RUCAIBox/LLMSurvey) : The official GitHub page for the survey paper "A Survey of Large Language Models". (**[arXiv 2023](https://arxiv.org/abs/2303.18223)**). " 微信公众号「RUC AI Box」《[大模型综述升级啦](https://mp.weixin.qq.com/s/9YMUSSrGLSBKMFY3JYlaoQ)》"。
- [BradyFU/Awesome-Multimodal-Large-Language-Models](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) : ✨✨Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation. "A Survey on Multimodal Large Language Models". (**[arXiv 2023](https://arxiv.org/abs/2306.13549)**). " 微信公众号「我爱计算机视觉」《[中科大腾讯发布首篇《多模态大语言模型综述》](https://mp.weixin.qq.com/s/IiPZWEVdAJ4xrlgyWtDwng)》"。
- [hymie122/RAG-Survey](https://github.com/hymie122/RAG-Survey) : 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". (**[arXiv 2024](https://arxiv.org/abs/2402.19473)**). " 微信公众号「数智笔记」《[2024检索增强生成RAG最新综述](https://mp.weixin.qq.com/s/F-shRy1m7wQIS87ujOS7Dw)》"。
- [daochenzha/data-centric-AI](https://github.com/daochenzha/data-centric-AI) 
: A curated, but incomplete, list of data-centric AI resources. "Data-centric Artificial Intelligence: A Survey". (**[arXiv 2023](https://arxiv.org/abs/2303.10158)**).
- [KSESEU/LLMPapers](https://github.com/KSESEU/LLMPapers) 
: Collection of papers and related works for Large Language Models (ChatGPT, GPT-3, Codex etc.).
- "Challenges and Applications of Large Language Models". (**[arXiv 2023](https://arxiv.org/abs/2307.10169)**).
- "A Survey on Vision Transformer". (**[IEEE TPAMI, 2022](https://ieeexplore.ieee.org/abstract/document/9716741)**).
- "Transformers in Vision: A Survey". (**[CM computing surveys (CSUR), 2022](https://dl.acm.org/doi/abs/10.1145/3505244)**).
- ### Learning Resources
- [动手学深度学习（Dive into Deep Learning，D2L.ai）](https://github.com/d2l-ai/d2l-zh) 
: 《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。[zh.d2l.ai](http://zh.d2l.ai/)
- [mlabonne/llm-course](https://github.com/mlabonne/llm-course) 
: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.[mlabonne.github.io/blog/](https://mlabonne.github.io/blog/)
- [rasbt/LLMs-from-scratch](https://github.com/rasbt/LLMs-from-scratch) 
: Implementing a ChatGPT-like LLM from scratch, step by step. [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
- [naklecha/llama3-from-scratch](https://github.com/naklecha/llama3-from-scratch) 
: llama3 implementation one matrix multiplication at a time.
- [zjhellofss/EdgeLLama](https://github.com/zjhellofss/EdgeLLama) 
: 自制大模型推理框架。
- [datawhalechina/llm-universe](https://github.com/datawhalechina/llm-universe) 
: 动手学大模型应用开发。本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：[https://datawhalechina.github.io/llm-universe/](https://datawhalechina.github.io/llm-universe/)
- [datawhalechina/hugging-llm](https://github.com/datawhalechina/hugging-llm) 
: HuggingLLM, Hugging Future. 蝴蝶书ButterflyBook. 配套视频教程：[https://b23.tv/hdnXn1L](https://www.bilibili.com/video/BV1ek4y1J7Rd/)
- [zyds/transformers-code](https://github.com/zyds/transformers-code) 
: 手把手带你实战 Huggingface Transformers 课程视频同步更新在B站与YouTube。
- [DjangoPeng/openai-quickstart](https://github.com/DjangoPeng/openai-quickstart) 
: A comprehensive guide to understanding and implementing large language models with hands-on examples using LangChain for GenAI applications. 本项目旨在为所有对大型语言模型及其在生成式人工智能（AIGC）场景中应用的人们提供一站式学习资源。通过提供理论基础，开发基础，和实践示例，该项目对这些前沿主题提供了全面的指导。
- [InternLM/Tutorial](https://github.com/InternLM/Tutorial) 
: 书生·浦语大模型实战营。为了推动大模型在更多行业落地开花，让开发者们更高效的学习大模型的开发与应用，上海人工智能实验室重磅推出书生·浦语大模型实战营，为广大开发者搭建大模型学习和实践开发的平台，两周时间带你玩转大模型微调、部署与评测全链路。
- [DLLXW/baby-llama2-chinese](https://github.com/DLLXW/baby-llama2-chinese) 
: 用于从头预训练+SFT一个小参数量的中文LLaMa2的仓库；24G单卡即可运行得到一个具备简单中文问答能力的chat-llama2.
- [charent/ChatLM-mini-Chinese](https://github.com/charent/ChatLM-mini-Chinese) 
: 中文对话0.2B小模型（ChatLM-Chinese-0.2B），开源所有数据集来源、数据清洗、tokenizer训练、模型预训练、SFT指令微调、RLHF优化等流程的全部代码。支持下游任务sft微调，给出三元组信息抽取微调示例。
- [charent/Phi2-mini-Chinese](https://github.com/charent/Phi2-mini-Chinese) 
: Phi2-Chinese-0.2B 从0开始训练自己的Phi2中文小模型，支持接入langchain加载本地知识库做检索增强生成RAG。Training your own Phi2 small chat model from scratch.
- [jiahe7ay/MINI_LLM](https://github.com/jiahe7ay/MINI_LLM) 
: This is a repository used by individuals to experiment and reproduce the pre-training process of LLM.
- [SmartFlowAI/Hand-on-RAG](https://github.com/SmartFlowAI/Hand-on-RAG) 
: Hand on RAG. 顾名思义：手搓的RAG。
- [liguodongiot/llm-action](https://github.com/liguodongiot/llm-action) 
: 本项目旨在分享大模型相关技术原理以及实战经验。
- [km1994/LLMsNineStoryDemonTower](https://github.com/km1994/LLMsNineStoryDemonTower) 
: 【LLMs九层妖塔】分享 LLMs在自然语言处理（ChatGLM、Chinese-LLaMA-Alpaca、小羊驼 Vicuna、LLaMA、GPT4ALL等）、信息检索（langchain）、语言合成、语言识别、多模态等领域（Stable Diffusion、MiniGPT-4、VisualGLM-6B、Ziya-Visual等）等 实战与经验。
- [RahulSChand/llama2.c-for-dummies](https://github.com/RahulSChand/llama2.c-for-dummies) 
: Step by step explanation/tutorial of llama2.c
- [liteli1987gmail/python_langchain_cn](https://github.com/liteli1987gmail/python_langchain_cn) 
: langchain中文网是langchain的python中文文档。[python.langchain.com.cn](https://python.langchain.com.cn/docs/)
- [langchain-ai/rag-from-scratch](https://github.com/langchain-ai/rag-from-scratch) : Retrieval augmented generation (RAG) comes is a general methodology for connecting LLMs with external data sources. These notebooks accompany a video series will build up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation.
- [phodal/aigc](https://github.com/phodal/aigc) 
: 《构筑大语言模型应用：应用开发与架构设计》一本关于 LLM 在真实世界应用的开源电子书，介绍了大语言模型的基础知识和应用，以及如何构建自己的模型。其中包括Prompt的编写、开发和管理，探索最好的大语言模型能带来什么，以及LLM应用开发的模式和架构设计。
- [cystanford/aigc_LLM_engineering](https://github.com/cystanford/aigc_LLM_engineering) 
: aigc_LLM_engineering.
- ### Community
- [Hugging Face](https://huggingface.co/) : The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications.
- [ModelScope | 魔塔社区](https://github.com/modelscope/modelscope) 
: [ModelScope](https://www.modelscope.cn/home) is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. [ModelScope](https://www.modelscope.cn/home) 是一个“模型即服务”(MaaS)平台，旨在汇集来自AI社区的最先进的机器学习模型，并简化在实际应用中使用AI模型的流程。ModelScope库使开发人员能够通过丰富的API设计执行推理、训练和评估，从而促进跨不同AI领域的最先进模型的统一体验。[www.modelscope.cn/](https://www.modelscope.cn/)
- [The official LangChain blog](https://blog.langchain.dev/) : LangChain. The official LangChain blog.
## Prompts
### 提示语（魔法）
- [EmbraceAGI/LangGPT](https://github.com/EmbraceAGI/LangGPT) 
: LangGPT: Empowering everyone to become a prompt expert!🚀 Structured Prompt，Language of GPT, 结构化提示词，结构化Prompt [feishu.langgpt.ai/](http://feishu.langgpt.ai/)
- [PlexPt/awesome-chatgpt-prompts-zh](https://github.com/PlexPt/awesome-chatgpt-prompts-zh) 
: ChatGPT 中文调教指南。各种场景使用指南。学习怎么让它听你的话。[chat.aimakex.com/](https://chat.aimakex.com/)
- [f/awesome-chatgpt-prompts](https://github.com/f/awesome-chatgpt-prompts) 
: This repo includes ChatGPT prompt curation to use ChatGPT better.
- [travistangvh/ChatGPT-Data-Science-Prompts](https://github.com/travistangvh/ChatGPT-Data-Science-Prompts) 
: 🚀 ChatGPT Prompts for Data Science! A repository of 60 useful data science prompts for ChatGPT.
- [kevinamiri/Instructgpt-prompts](https://github.com/kevinamiri/Instructgpt-prompts) 
: A collection of ChatGPT and GPT-3.5 instruction-based prompts for generating and classifying text. [prompts.maila.ai/](https://prompts.maila.ai/)
## Open API
- ### Python API
- [gpt4free](https://github.com/xtekky/gpt4free) 
: decentralising the Ai Industry, just some language model api's... [discord.gg/gpt4free](https://discord.gg/gpt4free)
- [acheong08/ChatGPT](https://github.com/acheong08/ChatGPT) 
: Reverse Engineered ChatGPT API by OpenAI. Extensible for chatbots etc.
- [wong2/chatgpt-google-extension](https://github.com/wong2/chatgpt-google-extension) 
: A browser extension that enhance search engines with ChatGPT.
- [acheong08/EdgeGPT](https://github.com/acheong08/EdgeGPT) 
: Reverse engineered API of Microsoft's Bing Chat AI.
- ### Rust API
- [uiuifree/rust-openai-chatgpt-api](https://github.com/uiuifree/rust-openai-chatgpt-api) : "rust-openai-chatgpt-api" is a Rust library for accessing the ChatGPT API, a powerful NLP platform by OpenAI. The library provides a simple and efficient interface for sending requests and receiving responses, including chat. It uses reqwest and serde for HTTP requests and JSON serialization.
- ### Csharp API
- [betalgo/openai](https://github.com/betalgo/openai) 
: OpenAI ChatGPT, Whisper, GPT-3 , GPT-4, Azure OpenAI and DALL-E dotnet SDK. [betalgo.github.io/openai/](https://betalgo.github.io/openai/)
- [OkGoDoIt/OpenAI-API-dotnet](https://github.com/OkGoDoIt/OpenAI-API-dotnet) 
: An unofficial C#/.NET SDK for accessing the OpenAI GPT-3 API. [www.nuget.org/packages/OpenAI/](https://www.nuget.org/packages/OpenAI/)
- [RageAgainstThePixel/OpenAI-DotNet](https://github.com/RageAgainstThePixel/OpenAI-DotNet) 
: A Non-Official OpenAI RESTful API Client for dotnet.
- [PawanOsman/ChatGPT.Net](https://github.com/PawanOsman/ChatGPT.Net) 
: C# library for ChatGPT using official OpenAI API. [www.nuget.org/packages/ChatGPT.Net](https://www.nuget.org/packages/ChatGPT.Net)
- [marcominerva/ChatGptNet](https://github.com/marcominerva/ChatGptNet) 
: A ChatGPT integration library for .NET.
- ### Node.js API
- [transitive-bullshit/chatgpt-api](https://github.com/transitive-bullshit/chatgpt-api) 
: Node.js client for the unofficial ChatGPT API. 🔥
## Applications
- ### IDE
#### 集成开发环境
- [Cursor](https://github.com/getcursor/cursor) 
: An editor made for programming with AI 🤖. Long term, our plan is to build Cursor into the world's most productive development environment. [cursor.so](https://www.cursor.so/)
- ### Chatbot
#### 聊天机器人
- [ChatHub](https://github.com/chathub-dev/chathub) 
: ChatHub is an all-in-one chatbot client. [chathub.gg/?utm_source=github](https://chathub.gg/?utm_source=github)
- [Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) 
: [VideoChatGPT] ChatGPT with video understanding! And many more supported LMs such as miniGPT4, StableLM, and MOSS. [vchat.opengvlab.com/](https://vchat.opengvlab.com/). "VideoChat: Chat-Centric Video Understanding". (**[arXiv 2023](https://arxiv.org/abs/2305.06355)**).
- [InternLM/HuixiangDou](https://github.com/InternLM/HuixiangDou) 
: HuixiangDou: Overcoming Group Chat Scenarios with LLM-based Technical Assistance. "HuixiangDou" is a domain-specific knowledge assistant based on the LLM. “茴香豆”是一个基于 LLM 的领域知识助手。
- [a16z-infra/llama2-chatbot](https://github.com/a16z-infra/llama2-chatbot) 
: LLaMA 2 Chatbot App ⚡
- [fuergaosi233/wechat-chatgpt](https://github.com/fuergaosi233/wechat-chatgpt) 
: Use ChatGPT On Wechat via wechaty.
- ### Role Play
#### 角色扮演
- [KMnO4-zx/xlab-huanhuan](https://github.com/KMnO4-zx/xlab-huanhuan) 
: Chat-甄嬛是利用《甄嬛传》剧本中所有关于甄嬛的台词和语句，基于[InternLM2](https://github.com/InternLM/InternLM.git)进行LoRA微调或全量微调得到的模仿甄嬛语气的聊天语言模型。
- [JimmyMa99/Roleplay-with-XiYou](https://github.com/JimmyMa99/Roleplay-with-XiYou) 
: Roleplay-with-XiYou 西游角色扮演。基于《西游记》原文、白话文、ChatGPT生成数据制作的，以InternLM2微调的角色扮演多LLM聊天室。 本项目将介绍关于角色扮演类 LLM 的一切，从数据获取、数据处理，到使用 XTuner 微调并部署至 OpenXLab，再到使用 LMDeploy 部署，以 openai api 的方式接入简单的聊天室，并可以观看不同角色的 LLM 互相交流、互怼。
- ### Robotics and Embodied AI
#### 机器人与具身智能
- [LeRobot](https://github.com/huggingface/lerobot) 
: 🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch.
- [BestAnHongjun/InternDog](https://github.com/BestAnHongjun/InternDog) 
: InternDog: 基于InternLM2大模型的离线具身智能导盲犬。
- ### Code Assistant
#### 代码助手
- [GPT Pilot](https://github.com/Pythagora-io/gpt-pilot) 
: The first real AI developer. GPT Pilot doesn't just generate code, it builds apps! GPT Pilot is the core technology for the [Pythagora VS Code extension](https://bit.ly/3IeZxp6) that aims to provide the first real AI developer companion. Not just an autocomplete or a helper for PR messages but rather a real AI developer that can write full features, debug them, talk to you about issues, ask for review, etc.
- [StarCoder](https://github.com/bigcode-project/starcoder) 
: 💫 StarCoder is a language model (LM) trained on source code and natural language text. Its training data incorporates more that 80 different programming languages as well as text extracted from GitHub issues and commits and from notebooks.
- [CodeGeeX2](https://github.com/THUDM/CodeGeeX2) 
: CodeGeeX2: A More Powerful Multilingual Code Generation Model. [codegeex.cn](https://codegeex.cn/zh-CN)
- [Code Llama](https://github.com/facebookresearch/codellama) 
: Inference code for CodeLlama models.
- ### Translator
#### 翻译
- [yetone/openai-translator](https://github.com/yetone/openai-translator) : The translator that does more than just translation - powered by OpenAI.
- [0xpayne/gpt-migrate](https://github.com/0xpayne/gpt-migrate) 
: Easily migrate your codebase from one framework or language to another. [gpt-migrate.com](https://gpt-migrate.com/)
- ### Local knowledge Base
#### 本地知识库
- [privateGPT](https://github.com/imartinez/privateGPT) 
: Ask questions to your documents without an internet connection, using the power of LLMs. 100% private, no data leaves your execution environment at any point. You can ingest documents and ask questions without an internet connection! Built with [LangChain](https://github.com/langchain-ai/langchain), [GPT4All](https://github.com/nomic-ai/gpt4all), [LlamaCpp](https://github.com/ggerganov/llama.cpp), [Chroma](https://www.trychroma.com/) and [SentenceTransformers](https://www.sbert.net/).
- [Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat) 
: lLangchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM) QA app with langchain ｜ 基于 Langchain 与 ChatGLM 等语言模型的本地知识库问答。
- [yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) 
: Chinese-LangChain：中文langchain项目，基于ChatGLM-6b+langchain实现本地化知识库检索与智能答案生成。俗称：小必应，Q.Talk，强聊，QiangTalk。
- [labring/FastGPT](https://github.com/labring/FastGPT) 
: FastGPT is a knowledge-based question answering system built on the LLM. It offers out-of-the-box data processing and model invocation capabilities. Moreover, it allows for workflow orchestration through Flow visualization, thereby enabling complex question and answer scenarios! [fastgpt.run](https://fastgpt.run/)
- ### Long-Term Memory
#### 长期记忆
- [MemGPT](https://github.com/cpacker/MemGPT) 
: Create LLM agents with long-term memory and custom tools 📚🦙. [memgpt.readme.io](https://memgpt.readme.io/)
- ### Question Answering System
#### 问答系统
- [THUDM/WebGLM](https://github.com/THUDM/WebGLM) 
: WebGLM: An Efficient Web-enhanced Question Answering System (KDD 2023). "WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences". (**[arXiv 2023](https://arxiv.org/abs/2306.07906)**).
- [afaqueumer/DocQA](https://github.com/afaqueumer/DocQA) 
: Question Answering with Custom FIles using LLM. DocQA 🤖 is a web application built using Streamlit 🔥 and the LangChain 🦜🔗 framework, allowing users to leverage the power of LLMs for Generative Question Answering. 🌟
- [rese1f/MovieChat](https://github.com/rese1f/MovieChat) 
: 🔥 chat with over 10K frames of video! MovieChat can handle videos with >10K frames on a 24GB graphics card. MovieChat has a 10000× advantage over other methods in terms of the average increase in GPU memory cost per frame (21.3KB/f to ~200MB/f).
- ### Academic Field
#### 学术领域
- [binary-husky/gpt_academic](https://github.com/binary-husky/gpt_academic) 
: 为ChatGPT/GLM提供图形交互界面，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm2等本地模型。兼容文心一言, moss, llama2, rwkv, claude2, 通义千问, 书生, 讯飞星火等。
- [kaixindelele/ChatPaper](https://github.com/kaixindelele/ChatPaper) 
: Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文总结+润色+审稿+审稿回复。 💥💥💥面向全球，服务万千科研人的ChatPaper免费网页版正式上线：[https://chatpaper.org/](https://chatpaper.org/) 💥💥💥
- [GPTZero](https://gptzero.me/): The World's #1 AI Detector with over 1 Million Users. Detect ChatGPT, GPT3, GPT4, Bard, and other AI models.
- [BurhanUlTayyab/GPTZero](https://github.com/BurhanUlTayyab/GPTZero) 
: An open-source implementation of [GPTZero](https://gptzero.me/). GPTZero is an AI model with some mathematical formulation to determine if a particular text fed to it is written by AI or a human being.
- [BurhanUlTayyab/DetectGPT](https://github.com/BurhanUlTayyab/DetectGPT) 
: An open-source Pytorch implementation of [DetectGPT](https://arxiv.org/pdf/2301.11305.pdf). DetectGPT is an amazing method to determine whether a piece of text is written by large language models (like ChatGPT, GPT3, GPT2, BLOOM etc). However, we couldn't find any open-source implementation of it. Therefore this is the implementation of the paper. "DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature". (**[arXiv 2023](https://arxiv.org/abs/2301.11305v1)**).
- [WangRongsheng/ChatGenTitle](https://github.com/WangRongsheng/ChatGenTitle) 
: 🌟 ChatGenTitle：使用百万arXiv论文信息在LLaMA模型上进行微调的论文题目生成模型。
- [nishiwen1214/ChatReviewer](https://github.com/nishiwen1214/ChatReviewer) 
: ChatReviewer: use ChatGPT to review papers; ChatResponse: use ChatGPT to respond to reviewers. 💥💥💥ChatReviewer的第一版网页出来了！！！ 直接点击：[https://huggingface.co/spaces/ShiwenNi/ChatReviewer](https://huggingface.co/spaces/ShiwenNi/ChatReviewer)
- [Shiling42/web-simulator-by-GPT4](https://github.com/Shiling42/web-simulator-by-GPT4) 
: Online Interactive Physical Simulation Generated by GPT-4. [shilingliang.com/web-simulator-by-GPT4/](https://shilingliang.com/web-simulator-by-GPT4/)
- ### Medical Field
#### 医药领域
- [本草[原名：华驼(HuaTuo)]](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese) 
: Repo for BenTsao [original name: HuaTuo (华驼)], Llama-7B tuned with Chinese medical knowledge. 本草[原名：华驼(HuaTuo)]: 基于中文医学知识的LLaMA微调模型。本项目开源了经过中文医学指令精调/指令微调(Instruct-tuning) 的LLaMA-7B模型。我们通过医学知识图谱和GPT3.5 API构建了中文医学指令数据集，并在此基础上对LLaMA进行了指令微调，提高了LLaMA在医疗领域的问答效果。 "HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge". (**[arXiv 2023](https://arxiv.org/abs/2304.06975)**).
- [MedSAM](https://github.com/bowang-lab/MedSAM) 
: "Segment Anything in Medical Images". (**[arXiv 2023](https://arxiv.org/abs/2304.12306)**). "微信公众号「江大白」《[MedSAM在医学领域，图像分割中的落地应用（附论文及源码）](https://mp.weixin.qq.com/s/JJ0umIzJ5VKJ87A_jnDtOw)》"。
- [LLaVA-Med](https://github.com/microsoft/LLaVA-Med) 
: "LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day". (**[arXiv 2023](https://arxiv.org/abs/2306.00890)**). "微信公众号「CVHub」《[微软发布医学多模态大模型LLaVA-Med | 基于LLaVA的医学指令微调](https://mp.weixin.qq.com/s/gzyVtbMArWDnfSzfCkxl9w)》"。
- [MedicalGPT](https://github.com/shibing624/MedicalGPT) 
: MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training Pipeline. 训练医疗大模型，实现包括二次预训练、有监督微调、奖励建模、强化学习训练。"微信公众号「KBQA沉思录」《[【中文医疗大模型】训练全流程源码剖析](https://mp.weixin.qq.com/s/DTHIxyDb9vG793hAKGLt2g)》"。
- [MedQA-ChatGLM](https://github.com/WangRongsheng/MedQA-ChatGLM) 
: 🛰️ 基于真实医疗对话数据在ChatGLM上进行LoRA、P-Tuning V2、Freeze、RLHF等微调，我们的眼光不止于医疗问答。[www.wangrs.co/MedQA-ChatGLM/](https://www.wangrs.co/MedQA-ChatGLM/). "MedQA-ChatGLM: A Medical QA Model Fine-tuned on ChatGLM Using Multiple fine-tuning Method and Real Medical QA Data".
- [xhu248/AutoSAM](https://github.com/xhu248/AutoSAM) 
: "How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images". (**[arXiv 2023](https://arxiv.org/abs/2306.13731)**).
- [DoctorGPT](https://github.com/llSourcell/DoctorGPT) 
: DoctorGPT is an LLM that can pass the US Medical Licensing Exam. It works offline, it's cross-platform, & your health data stays private.
- [仲景](https://github.com/SupritYoung/Zhongjing) 
: 仲景：首个实现从预训练到 RLHF 全流程训练的中文医疗大模型。 "Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue". (**[arXiv 2023](https://arxiv.org/abs/2308.03549)**).
- ### Mental Health Field
#### 心理健康领域
- [MeChat](https://github.com/qiuhuachuan/smile) 
: 中文心理健康支持对话数据集(SmileChat)与大模型(MeChat)。 "SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support". (**[arXiv 2023](https://arxiv.org/abs/2305.00450)**).
- [SmartFlowAI/EmoLLM](https://github.com/SmartFlowAI/EmoLLM) 
: EmoLLM-心理健康大模型是一系列能够支持 理解用户-支持用户-帮助用户 心理健康辅导链路的心理健康大模型，由 LLM指令微调而来。心理健康大模型、LLM、The Big Model of Mental Health、Finetune、InternLM2、Qwen、ChatGLM、Baichuan、DeepSeek、Mixtral。
- ### Legal Field
#### 法律领域
- [ChatLaw](https://github.com/PKU-YuanGroup/ChatLaw) 
: ChatLaw-法律大模型。[chatlaw.cloud/lawchat/](https://chatlaw.cloud/lawchat/)
- [LaWGPT](https://github.com/pengxiao-song/LaWGPT) 
: 🎉 Repo for LaWGPT, Chinese-Llama tuned with Chinese Legal knowledge. LaWGPT 是一系列基于中文法律知识的开源大语言模型。该系列模型在通用中文基座模型（如 Chinese-LLaMA、ChatGLM 等）的基础上扩充法律领域专有词表、大规模中文法律语料预训练，增强了大模型在法律领域的基础语义理解能力。在此基础上，构造法律领域对话问答数据集、中国司法考试数据集进行指令精调，提升了模型对法律内容的理解和执行能力。
- ### Financial Field
#### 金融领域
- [FinGPT](https://github.com/ai4finance-foundation/fingpt) 
: Data-Centric FinGPT. Open-source for open finance! Revolutionize 🔥 We'll soon release the trained model. "微信公众号「AINLPer」《[FinGPT：一个「专用于金融领域」的开源大语言模型（LLM）框架，源码公开！](https://mp.weixin.qq.com/s/A9euFin675nxGGciiX6rJQ)》"。
- ### Math Field
#### 数学领域
- [Progressive-Hint](https://github.com/chuanyang-Zheng/Progressive-Hint) 
: "Progressive-Hint Prompting Improves Reasoning in Large Language Models". (**[arXiv 2023](https://arxiv.org/abs/2304.09797)**).
- [Goat](https://github.com/liutiedong/goat) 
: "Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks". (**[arXiv 2023](https://arxiv.org/abs/2305.14201)**). "微信公众号「AINLPer」《[近乎完美！最强算术语言模型: Goar-7B，干翻GPT-4，怒越PaLM-540B！24G可训练](https://mp.weixin.qq.com/s/_haINkHNV4bMszm9F41yXA)》"。
- [AXYZdong/AMchat](https://github.com/AXYZdong/AMchat) 
: AMchat 高等数学大模型。AM (Advanced Mathematics) Chat is a large language model that integrates advanced mathematical knowledge, exercises in higher mathematics, and their solutions. AM (Advanced Mathematics) chat 高等数学大模型。一个集成数学知识和高等数学习题及其解答的大语言模型。
- ### Music Field
#### 音乐领域
- [GuoYiFantastic/IMelodist](https://github.com/GuoYiFantastic/IMelodist) 
: 旋律大师-IMelodist. Music large model based on InternLM2-chat.
- ### Speech and Audio Field
#### 语音和音频领域
- [flinkerlab/neural_speech_decoding](https://github.com/flinkerlab/neural_speech_decoding) 
: Neural Speech Decoding. "A neural speech decoding framework leveraging deep learning and speech synthesis" (**[Nature, 2024](https://www.nature.com/articles/s42256-024-00824-8)**). "微信公众号「量子位」《[脑电合成自然语音！LeCun转发Nature子刊新成果，代码开源](https://mp.weixin.qq.com/s/BcV3-3glmdsVF--fpPRU2g)》"。
- ### Humor Generation
#### 讲幽默笑话
- [CLoT](https://github.com/sail-sg/CLoT) 
: Creative Leap-of-Thought (CLoT). Official Codebase of our Paper: "Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation" (**[CVPR 2024](https://arxiv.org/abs/2312.02439)**). [zhongshsh.github.io/CLoT](https://zhongshsh.github.io/CLoT/). "微信公众号「NewBeeNLP」《[中山大学：“梗王”大模型，靠讲笑话登上CVPR](https://mp.weixin.qq.com/s/AeWCbKByO-fYFThSxOb43A)》"。
- ### Animation Field
#### 动漫领域
- [SaaRaaS-1300/InternLM2_horowag](https://github.com/SaaRaaS-1300/InternLM2_horowag) 
: 🍿InternLM2_Horowag🍿 🍏专门为 2024 书生·浦语大模型挑战赛 (春季赛) 准备的 Repo🍎收录了赫萝相关的微调模型。
- ### Food Field
#### 食品领域
- [SmartFlowAI/TheGodOfCookery](https://github.com/SmartFlowAI/TheGodOfCookery) 
: 食神（The God Of Cookery）。本项目名称为“食神”（ The God Of Cookery ），灵感来自喜剧大师周星驰主演的著名电影《食神》，旨在通过人工智能技术为用户提供烹饪咨询和食谱推荐，帮助用户更好地学习和实践烹饪技巧，降低烹饪门槛，实现《食神》电影中所讲的“只要用心，人人皆能做食神”。
- ### Tool Learning
#### 工具学习
- [ToolBench](https://github.com/OpenBMB/ToolBench) 
: An open platform for training, serving, and evaluating large language model for tool learning. [openbmb.github.io/ToolBench/](https://openbmb.github.io/ToolBench/). "ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs". (**[arXiv 2023](https://arxiv.org/abs/2307.16789)**).
- ### Autonomous Driving Field
#### 自动驾驶领域
- [DriveVLM](hhttps://tsinghua-mars-lab.github.io/DriveVLM/) : "DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models". (**[arXiv 2024](https://arxiv.org/abs/2402.12289)**).
- [UniAD](https://github.com/OpenDriveLab/UniAD) 
: "Planning-oriented Autonomous Driving". (**[CVPR 2023](https://arxiv.org/abs/2212.10156)**).
- [TransGPT|致远](https://github.com/DUOMO/TransGPT) 
: TransGPT是国内首款开源交通大模型，主要致力于在真实交通行业中发挥实际价值。它能够实现交通情况预测、智能咨询助手、公共交通服务、交通规划设计、交通安全教育、协助管理、交通事故报告和分析、自动驾驶辅助系统等功能。TransGPT作为一个通用常识交通大模型，可以为道路工程、桥梁工程、隧道工程、公路运输、水路运输、城市公共交通运输、交通运输经济、交通运输安全等行业提供通识常识。以此为基础，可以落脚到特定的交通应用场景中。
- ### Adversarial Attack Field
#### 对抗攻击领域
- [llm-attacks/llm-attacks](https://github.com/llm-attacks/llm-attacks) 
: "Universal and Transferable Adversarial Attacks on Aligned Language Models". (**[arXiv 2023](https://arxiv.org/abs/2307.15043)**). [llm-attacks.org/](https://llm-attacks.org/). "微信公众号「新智元」《[ChatGPT羊驼家族全沦陷！CMU博士击破LLM护栏，人类毁灭计划脱口而出](https://mp.weixin.qq.com/s/9UaYiLoIaXixfE8Ka8um5A)》"。
- ### Multi-Agent Collaboration
#### 多智能体协作
- [MetaGPT](https://github.com/geekan/MetaGPT) 
: "MetaGPT: Meta Programming for Multi-Agent Collaborative Framework". (**[arXiv 2023](https://arxiv.org/abs/2308.00352)**).
- ### AI Avatar
#### AI数字生命
- [RealChar](https://github.com/Shaunwei/RealChar) 
: 🎙️🤖Create, Customize and Talk to your AI Character/Companion in Realtime (All in One Codebase!). Have a natural seamless conversation with AI everywhere (mobile, web and terminal) using LLM OpenAI GPT3.5/4, Anthropic Claude2, Chroma Vector DB, Whisper Speech2Text, ElevenLabs Text2Speech🎙️🤖 [RealChar.ai/](https://realchar.ai/)
- [FaceChain](https://github.com/modelscope/facechain) 
: FaceChain is a deep-learning toolchain for generating your Digital-Twin. FaceChain is a deep-learning toolchain for generating your Digital-Twin. With a minimum of 1 portrait-photo, you can create a Digital-Twin of your own and start generating personal portraits in different settings (multiple styles now supported!). You may train your Digital-Twin model and generate photos via FaceChain's Python scripts, or via the familiar Gradio interface. FaceChain是一个可以用来打造个人数字形象的深度学习模型工具。用户仅需要提供最低三张照片即可获得独属于自己的个人形象数字替身。FaceChain支持在gradio的界面中使用模型训练和推理能力，也支持资深开发者使用python脚本进行训练推理。
- [VirtualWife](https://github.com/yakami129/VirtualWife) 
: VirtualWife 是一个虚拟主播项目，目前支持在B站进行直播，用户可以自由更换VRM人物模型，大家可以将他作为一个虚拟主播入门demo，在上面扩展自己喜欢功能。
- [GPT-vup](https://github.com/jiran214/GPT-vup) 
: GPT-vup Live2D数字人直播。GPT-vup BIliBili | 抖音 | AI | 虚拟主播。
- [ChatVRM](https://github.com/pixiv/ChatVRM) 
: ChatVRMはブラウザで簡単に3Dキャラクターと会話ができるデモアプリケーションです。
- [SillyTavern](https://github.com/SillyTavern/SillyTavern) 
: LLM Frontend for Power Users. [sillytavern.app](https://sillytavern.app/)
- [HeyGen](https://www.heygen.com/) : Scale your video production with customizable AI avatars. "微信公众号「DataLearner」《[《流浪地球2》的数字生命计划可能快实现了！HeyGen即将发布下一代AI真人视频生成技术，效果逼真到无法几乎分辨！](https://mp.weixin.qq.com/s/70Fj9HCe3ruiI43WmMZLjQ)》"。
- ### GUI
#### 图形用户界面
- [ChatGPT-Next-Web](https://github.com/Yidadaa/ChatGPT-Next-Web) 
: A well-designed cross-platform ChatGPT UI (Web / PWA / Linux / Win / MacOS). 一键拥有你自己的跨平台 ChatGPT 应用。
- [ChatGPT-Admin-Web](https://github.com/AprilNEA/ChatGPT-Admin-Web) 
: 带有用户管理和后台管理系统的 ChatGPT WebUI. [caw.sku.moe](https://caw.sku.moe/)
- [lencx/ChatGPT](https://github.com/lencx/ChatGPT) : 🔮 ChatGPT Desktop Application (Mac, Windows and Linux). [NoFWL](https://app.nofwl.com/).
- [Synaptrix/ChatGPT-Desktop](https://github.com/Synaptrix/ChatGPT-Desktop) : Fuel your productivity with ChatGPT-Desktop - Blazingly fast and supercharged!
- [Poordeveloper/chatgpt-app](https://github.com/Poordeveloper/chatgpt-app) : A ChatGPT App for all platforms. Built with Rust + Tauri + Vue + Axum.
- [sonnylazuardi/chat-ai-desktop](https://github.com/sonnylazuardi/chat-ai-desktop) : Chat AI Desktop App. Unofficial ChatGPT desktop app for Mac & Windows menubar using Tauri & Rust.
- [202252197/ChatGPT_JCM](https://github.com/202252197/ChatGPT_JCM) 
: OpenAI Manage Web. OpenAI管理界面，聚合了OpenAI的所有接口进行界面操作。
- [m1guelpf/browser-agent](https://github.com/m1guelpf/browser-agent) : A browser AI agent, using GPT-4. [docs.rs/browser-agent](https://docs.rs/browser-agent/latest/browser_agent/)
- [sigoden/aichat](https://github.com/sigoden/aichat) : Using ChatGPT/GPT-3.5/GPT-4 in the terminal.
- [wieslawsoltes/ChatGPT](https://github.com/wieslawsoltes/ChatGPT) 
: A ChatGPT C# client for graphical user interface runs on MacOS, Windows, Linux, Android, iOS and Browser. Powered by [Avalonia UI](https://www.avaloniaui.net/) framework. [wieslawsoltes.github.io/ChatGPT/](https://wieslawsoltes.github.io/ChatGPT/)
- [sigoden/aichat](https://github.com/GaiZhenbiao/ChuanhuChatGPT) 
: GUI for ChatGPT API and any LLM. 川虎 Chat 🐯 Chuanhu Chat. 为ChatGPT/ChatGLM/LLaMA/StableLM/MOSS等多种LLM提供了一个轻快好用的Web图形界。
- [amrrs/chatgpt-clone](https://github.com/amrrs/chatgpt-clone) 
: Build Yo'own ChatGPT with OpenAI API & Gradio.
- [llama2-webui](https://github.com/liltom-eth/llama2-webui) 
: Run Llama 2 locally with gradio UI on GPU or CPU from anywhere (Linux/Windows/Mac). Supporting Llama-2-7B/13B/70B with 8-bit, 4-bit. Supporting GPU inference (6 GB VRAM) and CPU inference.
- [ricklamers/gpt-code-ui](https://github.com/ricklamers/gpt-code-ui) 
: An open source implementation of OpenAI's ChatGPT Code interpreter.
- [mckaywrigley/chatbot-ui](https://github.com/mckaywrigley/chatbot-ui) 
:An open source ChatGPT UI. [chatbotui.com](https://chatbotui.com/)
- [chieapp/chie](https://github.com/chieapp/chie) 
: An extensive desktop app for ChatGPT and other LLMs. [chie.app](https://chie.app/)
- [cLangUI](https://github.com/ahmadbilaldev/langui) 
: AUI for your AI. Open Source Tailwind components tailored for your GPT, generative AI, and LLM projects.
- [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) 
: Stable Diffusion web UI. A browser interface based on Gradio library for Stable Diffusion.
- [Mikubill/sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet) 
: ControlNet for Stable Diffusion WebUI. The WebUI extension for ControlNet and other injection-based SD controls.
- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) 
: Text generation web UI. A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.
- [SolidUI](https://github.com/CloudOrc/SolidUI) 
: AI-generated visualization prototyping and editing platform.
- [AIdea](https://github.com/mylxsw/aidea) 
: AIdea 是一款支持 GPT 以及国产大语言模型通义千问、文心一言等，支持 Stable Diffusion 文生图、图生图、 SDXL1.0、超分辨率、图片上色的全能型 APP。
- [Chainlit](https://github.com/Chainlit/chainlit) 
: Build Python LLM apps in minutes ⚡️ Chainlit lets you create ChatGPT-like UIs on top of any Python code in minutes! [docs.chainlit.io](https://docs.chainlit.io/overview)
## Datasets
### 数据集
- ### Open Datasets Platform
#### 开放数据集平台
- [OpenDataLab](https://opendatalab.org.cn/) : 为大模型提供高质量的开放数据集！
- ### Text Datasets
#### 文本数据集
- [Leymore/ruozhiba](https://github.com/Leymore/ruozhiba) 
: 从百度[弱智吧](https://tieba.baidu.com/f?kw=%E5%BC%B1%E6%99%BA)上收集的一系列帖子。旨在启发人们娱乐性使用 ChatGPT 等 LLM 时的思路。
- ### Multimodal Datasets
#### 多模态数据集
- [Youku-mPLUG](https://github.com/X-PLUG/Youku-mPLUG) 
: "Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks". (**[arXiv 2023](https://arxiv.org/abs/2306.04362)**). "微信公众号「我爱计算机视觉」《[YouKu-mPLUG 最大中文视频语言数据集，助力增强多模态大型模型性能](https://mp.weixin.qq.com/s/iJoaKCykO09R3jTCylRTVA)》"。
- [Intern · WanJuan｜书生·万卷](https://github.com/opendatalab/WanJuan1.0) 
: Intern · WanJuan Multimodal Corpus. 万卷1.0多模态语料。
- [matrix-alpha/Accountable-Textual-Visual-Chat](https://github.com/matrix-alpha/Accountable-Textual-Visual-Chat) 
: "Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation". (**[arXiv 2023](https://arxiv.org/abs/2303.05983)**). [https://matrix-alpha.github.io/](https://matrix-alpha.github.io/)
- ### SFT Datasets
#### SFT数据集
- [chaoswork/sft_datasets](https://github.com/chaoswork/sft_datasets) 
: 开源SFT数据集整理,随时补充。
## Blogs
- 微信公众号「NVIDIA英伟达」
- [2023-10-27，现已公开发布！欢迎使用 NVIDIA TensorRT-LLM 优化大语言模型推理](https://mp.weixin.qq.com/s/QaSbvyAmI6XXtr0y6W4LNQ)
- 微信公众号「Hugging Face」
- [2023-08-16，关于 Llama 2 的一切资源，我们都帮你整理好了](https://mp.weixin.qq.com/s/-01Dg9ZVfPYM4mZ4iKt8Cw)
- [2023-08-24，社区供稿 | 推理 1760 亿参数的 BLOOMZ，性能时延仅 3.7 秒](https://mp.weixin.qq.com/s/LNEK5DK3p03qHeMxpht7GQ)
- [2023-08-24，使用 AutoGPTQ 和 transformers 让大语言模型更轻量化](https://mp.weixin.qq.com/s/uaIxZFpcVTsKE_uA-V37bQ)
- [2023-08-28，Hugging News #0821: Hugging Face 完成 2.35 亿美元 D 轮融资](https://mp.weixin.qq.com/s/s0lzSI5qZ5oJm5O0lh_5mg)
- [2024-02-22，欢迎 Gemma: Google 最新推出开源大语言模型](https://mp.weixin.qq.com/s/E52nPpWrhnU7wMLpOhVz5Q)
- 微信公众号「腾讯研究院」
- [2024-03-04，从诊室到云端：医疗大模型的应用挑战与未来探索](https://mp.weixin.qq.com/s/BoDq30q0K0kEKYzZhn71sQ)
- [2024-04-19，万字实录：中美大模型生态及技术趋势](https://mp.weixin.qq.com/s/pIOm2QZbuE6AvgW_ucdWBw)
- 微信公众号「腾讯技术工程」
- [2023-08-17，一文入门最热的LLM应用开发框架LangChain](https://mp.weixin.qq.com/s/bYzNNL3F0998Do2Jl0PQtw)
- 微信公众号「微软科技」
- [2023-02-16，揭秘ChatGPT走红背后的独门云科技！](https://mp.weixin.qq.com/s/qYZ7G5uLHTiLG8AonIch8g)
- [2023-07-26，Llama 2 登陆 Azure 和 Windows，微软与 Meta 拓展人工智能合作伙伴关系](https://mp.weixin.qq.com/s/pQLd5ZVNLdhnguPmmaDlCg)
- 微信公众号「微软亚洲研究院」
- [2023-08-16，ResNet四位作者获得2023未来科学大奖](https://mp.weixin.qq.com/s/PKXW-RqIuHQXjTuanqdAbQ)
- 微信公众号「Azure云科技」
- [2023-02-15，微软 Azure 作为 OpenAI 独家云服务提供商，助力企业致胜人工智能时代](https://mp.weixin.qq.com/s/SCmWX4uz3Ici2Shy6r1x7Q)
- 微信公众号「InternLM」
- [2024-03-05，你一票我一票，首期书生·浦语大模型实战营明星项目就出道！](https://mp.weixin.qq.com/s/-MpBhlV-kLMUf4lMeFoFlw)
- [2024-03-25，首届书生·浦源大模型挑战赛圆满收官，实战营学员大放异彩！](https://mp.weixin.qq.com/s/_n0OyGsYxlO9arUak8cMNg)
- [2024-03-26，LLM问答助手茴香豆发布web版，零开发集成微信&飞书群](https://mp.weixin.qq.com/s/Ru-JS-3QQVIRsdREjiDurg)
- [2024-04-02，InternLM2技术报告——社区翻译版](https://mp.weixin.qq.com/s/IUUj_CWUJPdrhLq1XAR-KA)
- 微信公众号「GLM大模型」
- [2023-06-25，【发布】ChatGLM2-6B：性能大幅提升，8-32k上下文，推理提速42%](https://mp.weixin.qq.com/s/_h9ls_gHIgHho1RBwUlhsA)
- [2023-07-14，【公告】ChatGLM2-6B，免费商用](https://mp.weixin.qq.com/s/pNMcR2c6kFV1TVaI8wzHRg)
- [2023-07-25，【发布】代码模型 CodeGeeX2-6B 开源，最低6GB显存，性能优于StarCoder](https://mp.weixin.qq.com/s/qw31ThM4AjG6RrjNwsfZwg)
- 微信公众号「零一万物 01AI」
- [2024-03-05，更长、更强、更开放，零一万物 Yi-1.5 系列开源模型发布一周广受好评](https://mp.weixin.qq.com/s/3G8trV950wg-7lMYbukE4w)
- 微信公众号「量子位」
- [2023-02-05，教ChatGPT学会看图的方法来了](https://mp.weixin.qq.com/s/OyLnRKgsklzQ09y9irtdQg)
- [2023-02-12，ChatGPT背后模型被证实具有人类心智！斯坦福新研究炸了，知名学者：“这一天终于来了”](https://mp.weixin.qq.com/s/zgrJVFvkqG69BrQCky193A)
- [2023-02-13，让ChatGPT长“手”！Meta爆火新论文，让语言模型学会自主使用工具](https://mp.weixin.qq.com/s/nca9jMOXgMKfhA8bo0FQvw)
- [2023-02-15，ChatGPT低成本复现流程开源！任意单张消费级显卡可体验，显存需求低至1.62GB](https://mp.weixin.qq.com/s/GcqFifmpE3_VvuAcJPsf-A)
- [2023-03-15，GPT-4发布！ChatGPT大升级！太太太太强了！](https://mp.weixin.qq.com/s/6u33Xnp4oEHq26WR4W1kdg)
- [2023-03-15，微软为ChatGPT打造专用超算！砸下几亿美元，上万张英伟达A100打造](https://mp.weixin.qq.com/s/jae8CoMWMKqLVhApqBcTfg)
- [2023-05-08，MathGPT来了！专攻数学大模型，解题讲题两手抓](https://mp.weixin.qq.com/s/RUnJ2T9BueDnDCu91m8uPQ)
- [2023-05-19，前哈工大教授开发的ChatALL火了！可同时提问17个聊天模型，ChatGPT/Bing/Bard/文心/讯飞都OK](https://mp.weixin.qq.com/s/1ERc9nBKMz9H_7hO02ky6w)
- [2023-05-19，ChatGPT突然上线APP！iPhone可用、速度更快，GPT-4用量限制疑似取消](https://mp.weixin.qq.com/s/TPeViQhBPrcUqWf7LbWsNg)
- [2023-05-28，「大一统」大模型论文爆火，4种模态任意输入输出，华人本科生5篇顶会一作，网友：近期最不可思议的论文](https://mp.weixin.qq.com/s/Mg_qnawkYSWnRHk4LIEIsQ)
- [2023-06-22，CVPR最佳论文颁给自动驾驶大模型！中国团队第一单位，近10年三大视觉顶会首例](https://mp.weixin.qq.com/s/bWaqD8GNGRrLxE1F_7r1fA)
- [2023-07-11，王小川大模型25天再升级！13B版本开源免费可商用，3090即可部署](https://mp.weixin.qq.com/s/sFVAgypEptxa6qCYcHix9g)
- [2023-07-12，Transformer八子谷歌一个也没留住！最后一名作者已宣布离职创业](https://mp.weixin.qq.com/s/1Lu57q-l69-A4WCABGBhgg)
- [2023-07-19，开源大模型重击OpenAI！小扎放出LLaMA2炸裂科技圈，联手微软高通冲击市场格局](https://mp.weixin.qq.com/s/GYu0ajE3eKO3TyFwHqGFgw)
- [2023-07-31，何恺明官宣加入MIT，正式回归学术界！](https://mp.weixin.qq.com/s/x2P0G6-Zm0tivmWLTYTprw)
- [2023-08-05，马斯克xAI创始成员国内首发声：ChatGPT时代「乱世出英雄」，下一步要多用数学科学数据训练](https://mp.weixin.qq.com/s/DncxAtjV47sMqpnxG0azgQ)
- [2023-08-07，Llama2等30+模型接入千帆大模型平台，推理成本降50%！还有超全Prompt模板开放体验](https://mp.weixin.qq.com/s/OBgl6-QOX-6cOsnl6waKxw)
- [2023-08-16，OpenAI进军内容审核行业，学校图书馆已经用ChatGPT筛选色情描述了](https://mp.weixin.qq.com/s/Bp62epgjN0XcBs6AoGUk7A)
- [2024-03-28，微软亚研院新作：让大模型一口气调用数百万个API！](https://mp.weixin.qq.com/s/fy9lw3QwOMryFMOEmTXfUA)
- [2024-04-04，弱智吧竟成最佳中文AI训练数据？！中科院等：8项测试第一，远超知乎豆瓣小红书](https://mp.weixin.qq.com/s/iq5lGyh9Y5P7NXLUS3-giA)
- [2024-04-12，谷歌爆改Transformer，“无限注意力”让1B小模型读完10部小说，114倍信息压缩](https://mp.weixin.qq.com/s/Hkt9TMf6e1Wp2xziw878WQ)
- [2024-04-17，脑电合成自然语音！LeCun转发Nature子刊新成果，代码开源](https://mp.weixin.qq.com/s/BcV3-3glmdsVF--fpPRU2g)
- [2024-04-19，Llama 3突然来袭！开源社区再次沸腾：GPT-4级别模型可以自由访问的时代到来](https://mp.weixin.qq.com/s/r6aradJU83GvvVwkFkLXKQ)
- [2024-05-14，GPT-4o深夜炸场！AI实时视频通话丝滑如人类，Plus功能免费可用，奥特曼：《她》来了](https://mp.weixin.qq.com/s/cAeLgg46Wq81rhgsJp0l4Q)
- 微信公众号「机器之心」
- [2023-02-15，开源方案复现ChatGPT流程！1.62GB显存即可体验，单机训练提速7.73倍](https://mp.weixin.qq.com/s/j8gvD_4ViRE4WQaQlcnmrQ)
- [2023-02-19，跟李沐学ChatGPT背后技术：67分钟读透InstructGPT论文](https://mp.weixin.qq.com/s/s5WrGn_dQyHrsZP8qsI2ag)
- [2023-02-21，复旦发布中国版ChatGPT：MOSS开启测试冲上热搜，服务器挤爆](https://mp.weixin.qq.com/s/LjwSozikB6CK5zh2Nd2JHw)
- [2023-03-13，清华朱军团队开源首个基于Transformer的多模态扩散大模型，文图互生、改写全拿下](https://mp.weixin.qq.com/s/B68hXlFxA9L5jiWiMrEEiA)
- [2023-03-14，真·ChatGPT平替：无需显卡，MacBook、树莓派就能运行LLaMA](https://mp.weixin.qq.com/s/7bRwX047jkZC53KYbhKARw)
- [2023-03-15，GPT-4震撼发布：多模态大模型，直接升级ChatGPT、必应，开放API，游戏终结了？](https://mp.weixin.qq.com/s/kA7FBZsT6SIvwIkRwFS-xw)
- [2023-04-02，3090单卡5小时，每个人都能训练专属ChatGPT，港科大开源LMFlow](https://mp.weixin.qq.com/s/LCGQyNA6sHcdfIIARSNlww)
- [2023-04-06，CV不存在了？Meta发布「分割一切」AI 模型，CV或迎来GPT-3时刻](https://mp.weixin.qq.com/s/-LWG3rOz60VWiwdYG3iaWQ)
- [2023-05-14，GPT-4拿下最难数学推理数据集新SOTA，新型Prompting让大模型推理能力狂升](https://mp.weixin.qq.com/s/y8u40qIXm3oWZkvgKOV17Q)
- [2023-05-20，有手就行？把大象P转身只需拖动鼠标，华人一作DragGAN爆火](https://mp.weixin.qq.com/s/wCvfcmv8OhGqo_fxxZUpKw)
- [2023-05-21，北京出手通用人工智能：产业创新伙伴计划公布，要推动大模型产业加速落地](https://mp.weixin.qq.com/s/gmclRnJvFnFIc6V-zU67ng)
- [2023-06-08，给语言大模型加上综合视听能力，达摩院开源Video-LLaMA](https://mp.weixin.qq.com/s/fU_21S5huOJDhrMRcqDcBQ)
- [2023-06-09，智源「悟道3.0」大模型系列问世，这次不拼参数，开源开放成为主角](https://mp.weixin.qq.com/s/kKqSa0sQOuRuQF7gDy7tIw)
- [2023-06-10，随时随地，追踪每个像素，连遮挡都不怕的「追踪一切」视频算法来了](https://mp.weixin.qq.com/s/IqcvtfTekSKELLIjX7qRCQ)
- [2023-06-17，llama.cpp作者创业，用纯C语言框架降低大模型运行成本](https://mp.weixin.qq.com/s/rRx0lhIKIPNumxKBk9tqag)
- [2023-06-20，650亿参数，8块GPU就能全参数微调：邱锡鹏团队把大模型门槛打下来了](https://mp.weixin.qq.com/s/339iXf2bimusfq6zQmFpWw)
- [2023-07-08，大语言模型的视觉天赋：GPT也能通过上下文学习解决视觉任务](https://mp.weixin.qq.com/s/CRlQ922r43E_jQSQxlqsDw)
- [2023-07-09，ChatGPT神器Code Interpreter终于开放，到底怎么用？这里有一份保姆级教程](https://mp.weixin.qq.com/s/VFApvnH1yCxsWCcUP6cSEg)
- [2023-07-16，获星1.9k，LLM微调神器Lamini上演速度与激情，免费可用](https://mp.weixin.qq.com/s/0I7WpR0rOCfqzb5_z_wzJA)
- [2023-07-19，更强的Llama 2开源，可直接商用：一夜之间，大模型格局变了](https://mp.weixin.qq.com/s/klFWFXCbjGaWZ7HO1KFZag)
- [2023-07-20，iPhone、Mac上都能跑，刷屏的Llama 2究竟性能如何？](https://mp.weixin.qq.com/s/q4xVrfAsCzfdeRoquCV5cg)
- [2023-07-23，我为什么放弃了 LangChain？](https://mp.weixin.qq.com/s/Iwe6M391b2BBWae-HmOIJQ)
- [2023-07-23，开源的Llama 2背后，有这些年轻华人的力量](https://mp.weixin.qq.com/s/miwc-beG2vrGG1oryCmtpw)
- [2023-07-31，大神回归学界：何恺明宣布加入 MIT](https://mp.weixin.qq.com/s/MwPMBESMtVTjjAfjGQPsLA)
- [2023-08-09，百川发布530亿大模型，融入搜索能力：第一时间内测体验已来](https://mp.weixin.qq.com/s/z0xUQH7GRd-YaMFTmynKkg)
- [2023-08-18，字节跳动类ChatGPT产品「豆包」邀测，我们先试了一下](https://mp.weixin.qq.com/s/DG-Dq9bAz1HpVpF5qxgoug)
- [2023-08-18，扩散模型「读脑术」，自动化所MindDiffuser清晰重建人脑视觉画面](https://mp.weixin.qq.com/s/FUvd2cU1LjBSERANko88nw)
- [2023-08-18，稚晖君人形机器人问世：大模型加持，会自己换胳膊，要上生产线造车](https://mp.weixin.qq.com/s/cgfbJgl9enzGXGTb6q6FGA)
- [2023-08-24，千亿级、数学专用，MathGPT大模型开始公测了](https://mp.weixin.qq.com/s/Atm0RtifVdbZVkt4FE7rOg)
- [2024-02-23，2770亿美元，英伟达创史上最大单日涨幅，黄仁勋：生成式AI已到临界点](https://mp.weixin.qq.com/s/Wb4ZU-lYoS6Kj0gNezMlaA)
- [2024-02-23，Stable Diffusion 3震撼发布，采用Sora同源技术，文字终于不乱码了](https://mp.weixin.qq.com/s/KOjeMQJoTLQt6uDBGRMXeQ)
- [2024-02-23，清华叉院、理想提出DriveVLM，视觉大语言模型提升自动驾驶能力](https://mp.weixin.qq.com/s/v6f29qeZAZOi4NdnwRlvZw)
- [2024-04-09，纯C语言手搓GPT-2，前OpenAI、特斯拉高管新项目火了](https://mp.weixin.qq.com/s/YMuq9Jo9Nibl1QFbLNxazg)
- [2024-04-19，开源大模型Llama 3王者归来！最大底牌4000亿参数，性能直逼GPT-4](https://mp.weixin.qq.com/s/KCyL8WTzXutPQ_k0Vl9Vwg)
- 微信公众号「图灵人工智能」
- [2023-02-04，盖茨盛赞ChatGPT：人工智能历史意义不亚于“PC或互联网诞生”](https://mp.weixin.qq.com/s/51v_fUjQe3EewwOIxlLghw)
- [2023-02-06，ChatGPT专题|ChatGPT之父传奇：8岁会编程，16岁出柜，2个月做到月活过亿](https://mp.weixin.qq.com/s/jodwa-a644vECTnrRqCuAA)
- [2023-02-08，ChatGPT专题|为什么ChatGPT这么强？—— 一文读懂ChatGPT原理！](https://mp.weixin.qq.com/s/QNuKQ2Mgfn5K22JuUe2dHA)
- [2023-02-11，ChatGPT专题|万字拆解！追溯ChatGPT各项能力的起源](https://mp.weixin.qq.com/s/4l0ADjdsCxSVvBeVKxSqWA)
- [2023-02-15，ChatGPT专题|ChatGPT是第一个真正意义的人工通用智能](https://mp.weixin.qq.com/s/V7gptx740dDtVyQAgdhnqA)
- [2023-02-16，ChatGPT专题|ChatGPT 算法原理](https://mp.weixin.qq.com/s/aIzwuATN71etbUrrQWYOkA)
- [2023-02-16，ChatGPT专题|由ChatGPT反思大语言模型（LLM）的技术精要](https://mp.weixin.qq.com/s/SthaVFuAzvPnpCVdwaZYdA)
- [2023-02-17，ChatGPT专题|ChatGPT背后真正的英雄：OpenAI首席科学家Ilya Sutskever的信仰之跃](https://mp.weixin.qq.com/s/EnRAcqiugR_xr7Mn0WJXLA)
- [2023-02-18，ChatGPT专题|ChatGPT学了一门清华慕课，还拿到了课程证书](https://mp.weixin.qq.com/s/enaw41QEyiJ0ecNmjyEctw)
- [2023-02-18，ChatGPT专题|关于GPT，人工智能，以及人的一些思考](https://mp.weixin.qq.com/s/SBpnmsc11C4fcH5xeftQdQ)
- [2023-02-19，ChatGPT 专题：万字长文解释 ChatGPT 在做什么，以及为什么它能发挥作用？](https://mp.weixin.qq.com/s/gt0YxLG9ZW2wIg5rzfBhKw)
- [2023-05-14，清华大学邓志东教授——通用大模型：深度学习的极限发展](https://mp.weixin.qq.com/s/J-JMBiBDBqXmfDWwKbze5g)
- [2023-05-21，从 GPU 到 ChatGPT](https://mp.weixin.qq.com/s/oobtNmLlvwZyheAk5jADmA)
- [2023-07-29，语言模型的前世今生与GPT的人生哲学](https://mp.weixin.qq.com/s/uHyz2Rt05GtH6GRRgCFUGQ)
- [2023-08-06，张钹院士：GPT时代的人工智能安全](https://mp.weixin.qq.com/s/FJ-jhD_b7o-5D4ikKcNcEw)
- [2023-08-15，谷歌发现大模型「领悟」现象！训练久了突然不再死记硬背，多么痛的领悟](https://mp.weixin.qq.com/s/d9K5fkgmvIkGQRPGIiSPaA)
- [2023-08-17，谷歌：大模型不仅有涌现能力，训练时间长了还有「领悟」能力](https://mp.weixin.qq.com/s/FEuViHRAYvQvKS5iCtX86Q)
- 微信公众号「硅星人」
- [2022-12-03，行走的代码生成器：chatGPT要让谷歌和程序员“下岗”了](https://mp.weixin.qq.com/s/DXzZ_5RrRbVe5bWkpwFV6Q)
- [2023-01-18，微软下个十年的想象力，藏在ChatGPT里](https://mp.weixin.qq.com/s/xjNipZ77I3eKbeYU5ZztZQ)
- [2023-01-28，ChatGPT又赢了：带动股价涨三倍，成考试神器](https://mp.weixin.qq.com/s/BCfI_IhbIvLaAphYheM7yQ)
- [2023-02-07，搜索大变天！谷歌推出Bard对抗ChatGPT，打响保卫战](https://mp.weixin.qq.com/s/33-Cg7Vn3Pmzuv_2IMHLzg)
- [2023-02-16，谁拖了中国ChatGPT的后腿？](https://mp.weixin.qq.com/s/66ILghJKHjQhEVJ3r1xi7A)
- [2023-02-24，OpenAI造就硅谷新“黑帮”：ChatGPT爆火背后的神秘大佬、技术版图和资本故事](https://mp.weixin.qq.com/s/eMwGbvxE_pCr1r1k18_yrA)
- [2023-02-25，英伟达再度站上风口](https://mp.weixin.qq.com/s/_OM1_Pf1GLHW3zuF-3F93Q)
- [2023-03-03，ChatGPT的战争不会浓缩于一个晚上](https://mp.weixin.qq.com/s/GJ94vpO9sRrXttdBo9oD2w)
- [2023-03-15，OpenAI发布GPT-4：能识图能算税，ChatGPT摆脱Chat，再次进化](https://mp.weixin.qq.com/s/JahPijUPjxrzuLhq0esIUg)
- [2023-03-17，GPT-4撑腰，Office全家桶集体升级，微软向谷歌丢出“王炸”](https://mp.weixin.qq.com/s/Ef_4FesHTP83NjZ3Knu5pA)
- [2023-03-22，对抗ChatGPT，谷歌Bard公测炸场了：巨头开启AI对决](https://mp.weixin.qq.com/s/TkfaTNFz4bM6EnHygNymqw)
- [2023-03-25，联网之后的ChatGPT，已经远不止“iPhone时刻”那么简单](https://mp.weixin.qq.com/s/_vn4RAqtRaNlBNP9W1sQcA)
- [2023-03-30，AI太强，人类危险？马斯克、图灵奖得主紧急呼吁暂停GPT-4模型后续研发](https://mp.weixin.qq.com/s/QrrVefyvrOQ8IbAVzWA-6w)
- [2023-04-01，OpenAI秘史公开：马斯克和奥特曼的战争，与钱无关](https://mp.weixin.qq.com/s/h_juJuhjVt8z-uu4qjaUFw)
- [2023-04-05，这些让人惊呼好用的神器背后，原来都是ChatGPT](https://mp.weixin.qq.com/s/KL6OFAhPfr_OC80I_W6b3g)
- [2023-04-07，Meta新模型“分割一切”：抠图完成究极进化，计算机视觉迎来GPT-3时刻](https://mp.weixin.qq.com/s/UUSmg6M5F6FJDs2i_-98dQ)
- [2023-06-30，AI创投圈嗨爆了：成立仅一年超级黑马融资13亿美元，大热门却只筹到2500万？](https://mp.weixin.qq.com/s/s195icDInYks4f4ICLpgLQ)
- [2023-07-07，开发者看过来：GPT-4 API接口全面开放了！](https://mp.weixin.qq.com/s/BFbZVmwogrTJCtm28Y-wkQ)
- [2023-07-19，Meta“搞大事”了：发布GPT“平替”Llama 2，开源、免费、还可商用！](https://mp.weixin.qq.com/s/RIpYez1K-Q6_CCRpPT4aLQ)
- 微信公众号「通用人工智能联盟」
- [2023-01-31，通用人工智能技术综述（一）](https://mp.weixin.qq.com/s/s1A0dHDs0ptNLIKXNivB8g)
- [2023-02-01，通用人工智能技术综述（二）](https://mp.weixin.qq.com/s/dBAHHdcQPbogxyOv-yTvzg)
- [2023-02-02，通用人工智能综述（三）](https://mp.weixin.qq.com/s/PjUPumRc9fFCmien71odsw)
- [2023-02-04，通用人工智能技术综述（四）](https://mp.weixin.qq.com/s/3w-T6V9h3zgJUFxb2D7FXQ)
- [2023-02-08，通用人工智能技术综述（五）](https://mp.weixin.qq.com/s/Bz4-AQ6UcFKTCSKoDwUrcg)
- [2023-02-12，ChatGPT的开发及部署成本略析](https://mp.weixin.qq.com/s/cqfUl2lBGhWtVj6NbWbuew)
- 微信公众号「计算机视觉研究院」
- [2023-02-09，计算机视觉研究院亲自体验ChatGPT的感受，太疯狂了！](https://mp.weixin.qq.com/s/82Z3cODnPbwpStXIhnuJyw)
- [2023-02-16，Image GPT——手把手教你搭建](https://mp.weixin.qq.com/s/gH_K_9Qo67HoNnSOnBevqw)
- [2023-02-20，7 Papers | 超越GPT 3.5的小模型；对ChatGPT摸底考试](https://mp.weixin.qq.com/s/_HV9atcakv0sWD5X4tloPw)
- [2023-06-21，RevCol：大模型架构设计新范式，给神经网络架构增加了一个维度！](https://mp.weixin.qq.com/s/vsia8h5LI4zs-lES0u_dcw)
- [2023-06-21，走向CV的通用人工智能：从GPT和大型语言模型中汲取的经验教训 (上)](https://mp.weixin.qq.com/s/6Sl8ELrA9zulal5iJoQXJA)
- [2023-07-03，ChatGPT实践应用和大模型技术解析](https://mp.weixin.qq.com/s/4GFc1e06hRjK4crfVPc2JA)
- [2023-07-03，DeepSpeed ZeRO++：降低4倍网络通信，显著提高大模型及类ChatGPT模型训练效率](https://mp.weixin.qq.com/s/sSIw7y-_vcN_y80b1tP6oQ)
- [2023-08-16，轻量级MobileSAM：比FastSAM快4倍，处理一张图像仅需10ms（附源代码）](https://mp.weixin.qq.com/s/3VhGKWpKTFY3u8hVJUYp_A)
- 微信公众号「江大白」
- [2023-02-15，万字拆解，ChatGPT各项能力的起源追溯](https://mp.weixin.qq.com/s/l0uGPO4vdFQzwCSP-HQQgg)
- [2023-03-02，ChatGPT团队背景研究报告，大厂不再是顶尖人才第一选择！](https://mp.weixin.qq.com/s/F_9fChIMkuZLoUfhnenwAw)
- [2023-03-03，行业热点 | ChatGPT数据集深度解密](https://mp.weixin.qq.com/s/mQiZIf-1QolCkX-2jTUa5Q)
- [2023-03-13，北大团队搞出ChatExcel，说人话自动处理表格，免费且不限次使用！](https://mp.weixin.qq.com/s/H8aG9AewM0npJCpA2A0YGQ)
- [2023-03-23，脑洞大开，如何利用ChatGPT搞科研？](https://mp.weixin.qq.com/s/HZvUfwpmPQC6OOX2Qyr-JQ)
- [2023-03-29，GPT-4 的独立创业之路，一个人就是一家公司！](https://mp.weixin.qq.com/s/Qu-OXSoDS5hmdPe6EENM4w)
- [2023-03-30，开源版ChatGPT项目，30分钟训完，性能堪比GPT3.5！（附源码）](https://mp.weixin.qq.com/s/x-UYyeAQc8NF2TiW8XLJHg)
- [2023-04-03，学术版专用Chatgpt火热开源，科研工作必备，附源码！](https://mp.weixin.qq.com/s/19jGbV37DhkihhKAxqBk7w)
- [2023-04-14，阿里版GPT通义千问实测来了！数学、编程、情书全套整活](https://mp.weixin.qq.com/s/a5NRdeR703CVBsG9xYgUlA)
- [2023-05-12，MedSAM在医学领域，图像分割中的落地应用（附论文及源码）](https://mp.weixin.qq.com/s/JJ0umIzJ5VKJ87A_jnDtOw)
- [2023-05-16，算法工程师如何优雅地使用ChatGPT?](https://mp.weixin.qq.com/s/FHdwnTPM6kOsMvAPcegrwg)
- [2023-06-03，深入浅出，Stable Diffusion完整核心基础讲解](https://mp.weixin.qq.com/s/5HnOAmUKDnOtf2xDX2R9Xg)
- [2023-06-03，分割一切模型(SAM)的全面综述调研](https://mp.weixin.qq.com/s/39imonlyIdSHYW9VnQhOjw)
- [2023-06-10，万字长文，解析大模型在自动驾驶领域的应用](https://mp.weixin.qq.com/s/QGF8ssfB6Rk350ro-ohIHA)
- [2023-06-21，AIGC 10亿参数模型进手机！15秒即可出图，飞行模式也能用！](https://mp.weixin.qq.com/s/chy2qMyD5ILTP2R6DpL4Yg)
- [2023-06-23，硬核详解SAM TensorRT模型，实战转换教程](https://mp.weixin.qq.com/s/Y5Y1b3iLcJWgQ2i3pPFNyg)
- [2023-06-26，CV通用人工智能：GPT和大语言模型带来的启发和感悟](https://mp.weixin.qq.com/s/Vu7svINOBSXqz9vjMgOjSw)
- [2023-06-30，MobileSAM来啦，比SAM小60倍，速度和效果双赢（附源码）](https://mp.weixin.qq.com/s/BRv9GDle40QS--Tt-hNPjg)
- [2023-07-07，中科院多语言大模型：BayLing(百聆)，性能媲美GPT，可在线体验！](https://mp.weixin.qq.com/s/bvn70GNlU3zHJSDHV5BsRA)
- [2023-07-10，十分钟读懂Diffusion：图解Diffusion扩散模型原理](https://mp.weixin.qq.com/s/54g-3foInJWI1wnB0X4odA)
- [2023-07-14，AI算法应用，模型部署服务代码实战](https://mp.weixin.qq.com/s/vFRTHcWjerFDlgV9TV6FWQ)
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- [2023-08-15，万字长文，深入浅出Llama搭建及源码解读](https://mp.weixin.qq.com/s/qDLVH9ADKrHySvPtr3carw)
- 微信公众号「WeThinkln」
- [2023-02-12，Rocky和ChatGPT“谈笑风生”的日子 |【AI行研&商业价值分析】](https://mp.weixin.qq.com/s/rV6J6UZgsJT-4HI49GBBaw)
- [2023-02-26，深入浅出解析ChatGPT引领的科技浪潮 |【AI行研&商业价值分析】](https://mp.weixin.qq.com/s/FLLtb_9shzFmH1wpV7oP_Q)
- [2023-06-22，深入浅出解析LoRA完整核心基础知识 |【算法兵器谱】](https://mp.weixin.qq.com/s/n-17rH0PrwHYZz0g58Cyiw)
- 微信公众号「夕小瑶科技说」
- [2023-05-31，一个技巧，让ChatGPT学会复杂编程，编程水平逼近人类程序员！](https://mp.weixin.qq.com/s/QgL5-fTA99InHsoI7hJ8lw)
- [2023-07-06，刚刚！OpenAI宣布，斥巨资建立「超级对齐」团队！向人类意图看齐](https://mp.weixin.qq.com/s/K7e6mfCA7eWN_armMBH9UA)
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- [2023-07-19，更强的Llama 2开源，可直接商用：一夜之间，大模型格局变了](https://mp.weixin.qq.com/s/PJyFoLP7IBxjbswq-NBEkA)
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- [2024-02-23，符尧大佬一作发文，仅改训练数据，就让LLaMa-2上下文长度扩展20倍！](https://mp.weixin.qq.com/s/sTxoxhyG6mAm5fI8tKdMPw)
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- 微信公众号「所向披靡的张大刀」
- [2023-04-07，分割大一统——Segment Anything深度体验](https://mp.weixin.qq.com/s/qtk1Ds3hdNi4NOwrw2tDrg)
- 微信公众号「算法邦」
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- [2023-04-03，ChatGPT成功背后的秘密，开源了！](https://mp.weixin.qq.com/s/V6Qgdf6JzfT7KGWVgNqWsQ)
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- 微信公众号「极市平台」
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- 微信公众号「计算机视觉与机器学习」
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- [2023-05-15，Nature发文！ChatGPT加速科研编程](https://mp.weixin.qq.com/s/MoXAnTJIV4JTVppfmBccHA)
- 微信公众号「CV技术指南」
- [2023-04-07，3090单卡5小时，每个人都能训练专属ChatGPT，港科大开源LMFlow](https://mp.weixin.qq.com/s/h6zbAVgFpW0ccdEHjLFpdQ)
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- 微信公众号「计算机视觉工坊」
- [2023-04-07，超震撼！Meta发布「分割一切」AI 模型！](https://mp.weixin.qq.com/s/_IbadabLJnvv1_a-NsAJfg)
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- 微信公众号「新智元」
- [2023-02-03，60天月活破亿，ChatGPT之父传奇：16岁出柜，20岁和男友一同当上CEO](https://mp.weixin.qq.com/s/W1xfLgZXWL3lfP4_54SQKw)
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- [2023-07-29，ChatGPT羊驼家族全沦陷！CMU博士击破LLM护栏，人类毁灭计划脱口而出](https://mp.weixin.qq.com/s/9UaYiLoIaXixfE8Ka8um5A)
- [2023-08-18，天才少年稚晖君智元机器人走路进场！AI模型做大脑，目标售价20万以内](https://mp.weixin.qq.com/s/0SE0w0ne3npFjrEdjYhZdg)
- [2023-08-19，波士顿大学「鸭嘴兽-70B」登顶Hugging Face大模型排行榜！高效数据集+独特LoRA微调是关键](https://mp.weixin.qq.com/s/RED36cGaqrhOOC5SGD9buw)
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- [2023-09-10，H100推理飙升8倍！英伟达官宣开源TensorRT-LLM，支持10+模型](https://mp.weixin.qq.com/s/xcNQBG69XkS6mOstzqROAw)
- [2024-02-22，全球最强开源大模型一夜易主！谷歌Gemma 7B碾压Llama 2 13B，今夜重燃开源之战](https://mp.weixin.qq.com/s/fpKW9UV7_S-FiFhiIet82g)
- [2024-02-23，Stable Diffusion 3深夜横空出世！模型与Sora同架构，也能「理解」物理世界](https://mp.weixin.qq.com/s/PU_VCbFU29rkfgoIm2as0g)
- [2024-04-07，Llama提速500%！谷歌美女程序员手搓矩阵乘法内核](https://mp.weixin.qq.com/s/2ROw_Tmmh4NHf8WOiwnJLg)
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- 微信公众号「智东西」
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- 微信公众号「CSDN」
- [2023-03-25，ChatGPT 已成为下一代的新操作系统！](https://mp.weixin.qq.com/s/MwrMhVydbhpP6c0AvPp8oQ)
- [2023-04-06，CV 迎来 GPT-3 时刻，Meta 开源万物可分割 AI 模型和 1100 万张照片，1B+掩码数据集！](https://mp.weixin.qq.com/s/spBwU0UecbxbEl88SA4GJQ)
- [2023-04-11，最爱 ChatGPT，每天编码 300 行，月薪 8k-17k 占比骤减！揭晓中国开发者真实现状](https://mp.weixin.qq.com/s/P6KjP1Xv85wSWjuxvMzK7Q)
- [2023-05-10，在 GitHub 上“搞事”，Meta 开源 ImageBind 新模型，超越 GPT-4，对齐文本、音频等 6 种模态！](https://mp.weixin.qq.com/s/wd5vnGEQaVjpLGWYUAo-gA)
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- 微信公众号「刘润」
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- 微信公众号「AI算法与图像处理」
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- 微信公众号「中国图象图形学报」
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- 微信公众号「脑机接口社区」
- [2023-02-15，ChatGPT发展历程、原理、技术架构详解和产业未来](https://mp.weixin.qq.com/s/LhcqK6W7OTB0Y1LfZIsGfA)
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- 微信公众号「新机器视觉」
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- 微信公众号「机器学习算法工程师」
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- [2023-05-17，StarCoder: 最先进的代码大模型](https://mp.weixin.qq.com/s/XrY-pgBQ-DoTH_0olJ7ytw)
- 微信公众号「人工智能与算法学习」
- [2023-02-15，ChatGPT数据集之谜](https://mp.weixin.qq.com/s/CFgsiJ7a2mXQNAWkQxScYQ)
- [2023-03-10，王炸！微软发布Visual ChatGPT：视觉模型加持ChatGPT实现丝滑聊天](https://mp.weixin.qq.com/s/jQd0xujid66CrcBrhhZoLQ)
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- [2023-09-05，DoctorGPT 模型：为每个人提供一个私人医生](https://mp.weixin.qq.com/s/JAc2GlBJOA1rPfZHGVwbmQ)
- [2024-02-21，全网最细致的Sora技术推演](https://mp.weixin.qq.com/s/xl56nMgqNK5uih7uGoOU3w)
- 微信公众号「量子学派」
- [2023-02-10，封杀这个公式，ChatGPT智商将为零](https://mp.weixin.qq.com/s/l1Qxe3rGTYuIumHq02exsg)
- [2023-02-10，ChatGPT，一种更中心化的权力？](https://mp.weixin.qq.com/s/-qmccVnv_rpKVdFP6x4GNg)
- 微信公众号「42章经」
- [2023-02-13，我是怎样用一周时间研究 ChatGPT 的？](https://mp.weixin.qq.com/s/obVI3ENpMgaq4AKZs6Hw1w)
- 微信公众号「人工智能技术与咨询」
- [ChatGPT四大应用主线及相关细分场景](https://mp.weixin.qq.com/s/f8cmRVs0ys7FNyNU1qbP6g)
- 微信公众号「应用语言学研习」
- [2023-02-17，如何利用ChatGPT搞科研？](https://mp.weixin.qq.com/s/sW_utRBS_jJAaWfGo_eT5g)
- 微信公众号「机器之能」
- [2023-03-22，比尔·盖茨：AI时代已经开启，GPT是40年来最具革命性技术](https://mp.weixin.qq.com/s/j3D7g_1HeKZbznOqqU2pxw)
- [2024-04-19，开源大模型Llama 3王者归来！最大底牌4000亿参数，性能直逼GPT-4](https://mp.weixin.qq.com/s/eTN6kGFiJLoN0HKvAyWFug)
- 微信公众号「机器学习研究组订阅」
- [2023-03-26，震惊科学界！微软154页研究刷屏：GPT-4能力接近人类，「天网」初现？](https://mp.weixin.qq.com/s/C0qwDb_ASCbmP8sHgH97Jg)
- 微信公众号「浮之静」
- [2022-12-14，流量密码：ChatGPT 开源的一些思考](https://mp.weixin.qq.com/s/-lpQycfKVQ1gLKjoMrTvpA)
- [2023-02-08，ChatGPT 扫盲指南](https://mp.weixin.qq.com/s/4RczQBdAmnYSdlhMBcXcZA)
- [2023-03-01，一文读懂 OpenAI](https://mp.weixin.qq.com/s/_ovmBsJ7EQr_k4JnSKtuLw)
- [2023-03-15，AI 里程碑：GPT-4 发布了！](https://mp.weixin.qq.com/s/n8ttVSJmd44sBdpnL3Whxw)
- [2023-03-27，AI 浪潮下的一些浅思](https://mp.weixin.qq.com/s/1TYrtufxtLcMy0RolNAbhg)
- [2023-05-21，ChatGPT 探索：英语学习小助手](https://mp.weixin.qq.com/s/QGURRcD3QOM7-4x0CumX4Q)
- [2023-05-25，ChatGPT 桌面应用 v1.0.0 发布啦！](https://mp.weixin.qq.com/s/jbQCws2G8hNdytIMPHHg0w)
- [2023-06-22，GPT-4 混合模型：8 个 2200 亿参数的专家模型？](https://mp.weixin.qq.com/s/PEqusMr1p4-T5piWUzbfzA)
- [2023-07-11，ChatGPT：Code Interpreter == GPT-4.5？](https://mp.weixin.qq.com/s/cexXvkbkxZNF8-ZD9Zplyg)
- [2023-07-12，ChatGPT：GPT-4 架构揭秘](https://mp.weixin.qq.com/s/B-XQRuns_U9Li5jXW-sOuw)
- [2023-08-06，LangUI：AI 与 GPT 项目专属开源组件库](https://mp.weixin.qq.com/s/Uszrre1L__91aIYEGl32uA)
- 微信公众号「学术头条」
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- 微信公众号「人工智能研究」
- [2023-03-11，哈工大NLP研究所ChatGPT调研报告发布！](https://mp.weixin.qq.com/s/u17VEv0VM8MXYyB7jcV-yA)
- 微信公众号「OpenFPGA」
- [2023-03-13，在FPGA设计中怎么应用ChatGPT？](https://mp.weixin.qq.com/s/BvCFoAi9tAvSs4QS4BFRdA)
- [2023-03-27，ChatGPT推荐的开源项目，到底靠不靠谱？](https://mp.weixin.qq.com/s/_ERFebXaLUbF3EQs_ZyPIQ)
- 微信公众号「AI科技评论」
- [2023-03-14，何恺明 MIT 最新演讲：未来工作将聚焦 AI for science](https://mp.weixin.qq.com/s/8oiHz34DpfDJmT4IPzU8IA)
- [2023-08-10，清华提出开源工具学习框架，接入真实世界 16000+API, 效果达 ChatGPT](https://mp.weixin.qq.com/s/pg4oeybuy0tuXK_7K5zq3w)
- 微信公众号「AI科技大本营」
- [2023-07-19，微软又赢麻了！联合 Meta 发布免费商业应用的开源 AI 模型 Llama 2](https://mp.weixin.qq.com/s/gBLkqSpHkRBK6nhSUnMTUA)
- 微信公众号「HelloGitHub」
- [2023-03-17，GPT-4 来了！这些开源的 GPT 应用又要变强了](https://mp.weixin.qq.com/s/MeexLX_aOyUKHtaiyuwMTA)
- 微信公众号「脚本之家」
- [2023-03-23，GPT-4 Copilot X震撼来袭！AI写代码效率10倍提升，码农遭降维打击](https://mp.weixin.qq.com/s/XCBPSCLSDUSiu3CP54PfWg)
- 微信公众号「FightingCV」
- [2023-03-23，OpenAI重磅研究：ChatGPT可能影响80%工作岗位，收入越高影响越大](https://mp.weixin.qq.com/s/DUiEqgz-Ytf6c8NU8f7O3w)
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- 微信公众号「科金中心」
- [2023-03-22，今日关注 | 比尔盖茨：超级人工智能还没来 GPT模型是40余年来最革命性技术进步](https://mp.weixin.qq.com/s/vBkbE04Oz0ssYqjsvIacPg)
- 微信公众号「findyi」
- [2023-04-06，ChatGPT！王炸级更新！！！](https://mp.weixin.qq.com/s/F3gSN_GWvvCOR2zGva4Oew)
- 微信公众号「AI能力站」
- [2023-04-01，AIGC、ChatGPT和LLM三者之间的联系](https://mp.weixin.qq.com/s/O-A3uU1g8_LkOO1VhxYX4Q)
- 微信公众号「孙立平社会观察」
- [2023-04-07，霍金：失控的人工智能很难被阻止住](https://mp.weixin.qq.com/s/Zd4o3p4ysTJ7_kNzGivKPA)
- 微信公众号「世界经济论坛」
- [2023-04-01，比尔·盖茨：人工智能变革前夜的展望](https://mp.weixin.qq.com/s/O-AUjuVgfcDk2OrxBOcL_g)
- 微信公众号「新华数字」
- [2022-12-06，AIGC：ChatGPT的未来展望](https://mp.weixin.qq.com/s/sZUwvE6kehkTuZ1wuXzn2g)
- 微信公众号「猫说AI」
- [2023-04-04，ChatGPT开源平替--ChatGLM](https://mp.weixin.qq.com/s/sCTuMgbGK6N_bThOhJJ9-w)
- 微信公众号「资本实验室」
- [2023-02-13，ChatGPT爆火之下，生成式人工智能的「远忧近虑」| 海外周选](https://mp.weixin.qq.com/s/hrIwPA_eBu2sUmfW7mYlsw)
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- 微信公众号「空中机器人前沿」
- [2023-03-22，在「机器人领域」使用ChatGPT提高生产力](https://mp.weixin.qq.com/s/MB9pcqzLHb_oNNdDYa2oSA)
- 微信公众号「CVHub」
- [2023-04-06，《万字长文带你解读AIGC》系列之技术篇](https://mp.weixin.qq.com/s/6jMCd9yn_vBLiLJGBpSB2g)
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- [2023-06-05，X-AnyLabeling: 一款多SOTA模型集成的高精度自动标注工具！](https://mp.weixin.qq.com/s/Fi7i4kw0n_QsA7AgmtP-JQ)
- [2023-06-07，三万字长文带你全面解读生成式AI](https://mp.weixin.qq.com/s/BDYHCnkihSChKBJHVxqywA)
- [2023-06-08，微软发布医学多模态大模型LLaVA-Med | 基于LLaVA的医学指令微调](https://mp.weixin.qq.com/s/gzyVtbMArWDnfSzfCkxl9w)
- [2023-06-13，VisorGPT: 如何基于 GPT 和 AIGC 模型定制一个可控的生成模型](https://mp.weixin.qq.com/s/0XHjkGz7XN5jZZi2mvEKxA)
- [2023-07-30，大连理工联合阿里达摩院发布HQTrack | 高精度视频多目标跟踪大模型](https://mp.weixin.qq.com/s/Jl2mr7tszulZX19Fx4ZNgw)
- [2023-08-07，万字长文带你全面解读视觉大模型](https://mp.weixin.qq.com/s/aA_f4ZPWquoYbbPRqiv60g)
- [2024-04-04，具身智能论文巡礼 - 开篇](https://mp.weixin.qq.com/s/T3oKepEReqSlntYiyeHGBw)
- 微信公众号「芯榜」
- [2023-04-16，思特威：人工智能浪潮，将机器视觉冲向新蓝海](https://mp.weixin.qq.com/s/jtJvltmjSeCi47XiVOzzdw)
- 微信公众号「数智前线」
- [2023-04-12，阿里通义千问，通向企业](https://mp.weixin.qq.com/s/L3FCVJVbMdKdeP6m8B9Lmg)
- [2023-04-18，解码商汤大模型体系](https://mp.weixin.qq.com/s/3mkYe-UAy3dJFMBbPvgbrA)
- 微信公众号「智能进化论」
- [2023-04-18，AI大模型内卷加剧，商汤凭什么卷进来](https://mp.weixin.qq.com/s/-az_NylC3EyqN4iYx8Sbrw)
- 微信公众号「深蓝AI」
- [2023-04-23，最新综述！AIGC到底是什么？都有哪些应用？一文尽览！](https://mp.weixin.qq.com/s/rp9XVUBrh17Wr57SPFgTvg)
- 微信公众号「人工智能前沿讲习」
- [2023-04-23，【综述专栏】“ChatGPT的问题、风险与机遇”会议综述](https://mp.weixin.qq.com/s/-Gi4xMUXYiI13DaTVgwUdQ)
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- [2023-08-18，【【综述专栏】可信赖的大型语言模型](https://mp.weixin.qq.com/s/K3wWV6l7q_acKp2cEezakw)
- 微信公众号「澎湃新闻」
- [2023-05-17，莫言给余华写颁奖词，找ChatGPT帮忙](https://mp.weixin.qq.com/s/ym0w_1ftIw5BpPnGSDLsYg)
- 微信公众号「宅码」
- [2023-04-18，【知出乎争】GPT的变现和技术介绍](https://mp.weixin.qq.com/s/yWTriSW7CGndHraJXAi3FQ)
- 微信公众号「Web3天空之城」
- [2023-05-07，AI教父最新MIT万字访谈: 人类可能只是AI演化过程中的一个过渡阶段](https://mp.weixin.qq.com/s/VxlyLOUP_CIyMvGCBGimCQ)
- [2023-05-17，Sam Altman 国会质询2.5万字全文：如果这项技术出错，它会出错得很严重](https://mp.weixin.qq.com/s/DqPTN8pADPWGjMSiO3__2w)
- 微信公众号「AI前线」
- [2023-05-03，7天花5万美元，我们成功复制了 Stable Diffusion，成本大降88%！训练代码已开源](https://mp.weixin.qq.com/s/KYhjUOhi3dBvGptBiBlW8A)
- [2023-06-21，微软也搞起了开源小模型！利用OpenAI的ChatGPT和GPT-4 训练，实力碾压当前最强开源模型](https://mp.weixin.qq.com/s/RRdrSeI2ux5QE6MqJ8opSg)
- [2023-08-11，Python 失宠！Hugging Face 用 Rust 新写了一个 ML框架，现已低调开源](https://mp.weixin.qq.com/s/YMmYnODJObYplDolnhtJZw)
- [2023-08-21，开源打败闭源？Meta即将推出开源代码生成平台Code Llama，剑指OpenAI Codex](https://mp.weixin.qq.com/s/jKjgvMNy-UYOVMYE0dbo2w)
- 微信公众号「AI工程化」
- [2023-08-11，Hugging Face偷偷放大招了，Rust版本的ML框架Candle曝光](https://mp.weixin.qq.com/s/iwrV35oq_j8-SqUIMk-m0A)
- 微信公众号「CVer」
- [2023-05-03，代季峰教授：超大规模视觉通用模型最新研究成果分享](https://mp.weixin.qq.com/s/RYCHY0CrFbnM88ORegED1A)
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- 微信公众号「Jack Cui」
- [2023-05-04，新项目又火了，已开源！gpt4免费了...](https://mp.weixin.qq.com/s/f6Sxc1ZYWguYkiFV3atI3g)
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- 微信公众号「AI数据派」
- [2023-05-05，UC伯克利发布大语言模型排行榜！Vicuna夺冠，清华ChatGLM进前5](https://mp.weixin.qq.com/s/JS2ISYUOiSQKECYuXB8h5A)
- 微信公众号「我爱计算机视觉」
- [2023-05-05，图文理解能力强大！多模态对话生成模型：mPLUG-Owl，已开源！](https://mp.weixin.qq.com/s/tQYV54g6aMJxogmI3MzmiA)
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- 微信公众号「计算机视觉联盟」
- [2023-05-10，北大、西湖大学等开源PandaLM](https://mp.weixin.qq.com/s/mKq56QrTWTd7IiXcmYqSFA)
- [2023-08-05，综述！LLM的当前挑战和应用](https://mp.weixin.qq.com/s/LhykEJ2SXxMZlRQm2g91JQ)
- 微信公众号「机器学习与AI生成创作」
- [2023-05-09，借助通用分割大模型！半自动化标注神器，Label-Studio X SAM（附源码）](https://mp.weixin.qq.com/s/2qPiEkuruIVZk1HcTqHYjg)
- 微信公众号「差评」
- [2023-04-17，我有个周入百万的项目：教人用ChatGPT。](https://mp.weixin.qq.com/s/awfe5Hb2_g-EZ-rHJY-SBw)
- 微信公众号「程序员的那些事」
- [2023-05-16，Midjourney 5.1 震撼更新！逼真到给跪，中国情侣细节惊艳，3D视频大片马上来](https://mp.weixin.qq.com/s/IViZPmfKlzgc83ozuj-zcg)
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- 微信公众号「51CTO技术栈」
- [2023-05-19，Stability AI开源一系列人工智能应用](https://mp.weixin.qq.com/s/QOT7ycS5MuobPW2XeYWLWw)
- [2023-05-16，入驻QQ一天就爆满！Midjourney中文版来了！](https://mp.weixin.qq.com/s/2eLc_vIUIdR9wKIUzOxZ0A)
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- [2023-05-18，人手一个 Midjourney，StableStudio 重磅开源！](https://mp.weixin.qq.com/s/SbW3drfTmXyoeuwpDg5o2w)
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- 微信公众号「CreateAMind」
- [2023-05-20，改进GPT的底层技术](https://mp.weixin.qq.com/s/5zZrol7CLHD-kEMejwHimw)
- 微信公众号「深度学习与NLP」
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- [2023-05-26，大模型上不了车](https://mp.weixin.qq.com/s/guxGFY5Jg_YdWDxnIyTZsA)
- 微信公众号「芯东西」
- [2023-06-14，1530亿颗晶体管！AMD甩出最强AI芯片，单个GPU跑大模型](https://mp.weixin.qq.com/s/b47zVOa_KGEN47_d3Dlibw)
- 微信公众号「开源技术服务中心」
- [2023-05-31，河套IT WALK(总第64期)：AI与自动驾驶科技：打造未来生活方式](https://mp.weixin.qq.com/s/wGupibJ9cKrjdSbUv9cQgQ)
- 微信公众号「OneFlow」
- [2023-06-09，GPT总设计师：大型语言模型的未来](https://mp.weixin.qq.com/s/DAV4ZQ5HVKw3z-mQnM7cWA)
- 微信公众号「AINLP」
- [2023-08-06，Llama深入浅出](https://mp.weixin.qq.com/s/grayNg0IvAmILTF1dCEWTA)
- [2023-08-06，哈工大开源“活字”对话大模型](https://mp.weixin.qq.com/s/gmKjMjr7VVESPEAWIQW3wQ)
- [2024-05-15，手撕Flash Attention！原理解析及代码实现](https://mp.weixin.qq.com/s/2hSNQk1y99YM4TX0D96POQ)
- 微信公众号「AINLPer」
- [2023-06-05，近乎完美！最强算术语言模型: Goar-7B，干翻GPT-4，怒越PaLM-540B！24G可训练](https://mp.weixin.qq.com/s/_haINkHNV4bMszm9F41yXA)
- [2023-06-06，Amazon | 深入研究LLMs与AutoGPT的结合：揭示出GPT-4惊人的人类决策能力！](https://mp.weixin.qq.com/s/Gbz7ZVVdeTq64mj1-__aQA)
- [2023-06-16，FinGPT：一个「专用于金融领域」的开源大语言模型（LLM）框架，源码公开！](https://mp.weixin.qq.com/s/A9euFin675nxGGciiX6rJQ)
- [2023-06-26，ChatGLM2-6B 发布：性能大幅提升，8-32k上下文，推理提速42%](https://mp.weixin.qq.com/s/zDf9YbOEc681Otcjh0FJxw)
- 微信公众号「ArronAI」
- [2023-06-13，高性能支持LLM的机器学习Tensor库](https://mp.weixin.qq.com/s/hdwWP39BHb68VHtCcUcM7Q)
- [2023-07-19，Meta发布升级大模型LLaMA 2：开源可商用](https://mp.weixin.qq.com/s/cahpaMKbdKNMJCp1Rot5KA)
- [2023-07-30，大模型部署框架 FastLLM 实现细节解析](https://mp.weixin.qq.com/s/AFUZC9RAgA7_Mj6KsgYqSw)
- [2023-07-31，ChatGLM-6B VS 昆仑万维天工对比](https://mp.weixin.qq.com/s/I4RdHFzOhyxzOYkVGMH-og)
- 微信公众号「DataLearner」
- [2023-05-19，ChatGLM-6B重磅升级！清华大学开源VisualGLM-6B技术解析：一个可以在本地运行的读懂图片的语言模型！](https://mp.weixin.qq.com/s/nZwiNk_80uTPcS2QrofnrQ)
- [2023-05-27，Falcon-40B：截止目前最强大的开源大语言模型，超越MetaAI的LLaMA-65B的开源大语言模型](https://mp.weixin.qq.com/s/Vy_xWBuZU0AaaPMCIhKIyw)
- [2023-06-13，国产开源大模型再添重要玩家：BAAI发布开源可商用大模型Aquila](https://mp.weixin.qq.com/s/n8GwkDt9wXI9nNfFTIRcBQ)
- [2023-06-25，重磅！第二代ChatGLM-6B发布！清华大学THUDM发布ChatGLM2-6B：更快更准，更低资源更长输入！](https://mp.weixin.qq.com/s/7Y6_jqj0RBq82hEggFHTgg)
- [2023-07-09，使用LangChain做大模型开发的一些问题：来自Hacker News的激烈讨论~ ](https://mp.weixin.qq.com/s/GKF28C1yzWZDtCXjJQ52hg)
- [2023-07-14，重磅！清华大学第二代大模型ChatGLM2-6B现在转为免费商用授权协议了~](https://mp.weixin.qq.com/s/FpRAA2b3o6pj8gNpeSWb4g)
- [2023-07-15，GPT4All发布可以在CPU上生成embeddings向量的模型：低成本、高质量、易上手的embedding模型新选择](https://mp.weixin.qq.com/s/hPQlthpVVlxjHhkSKLU0GA)
- [2023-07-18，如何让开源大模型支持Code Interpreter：基于LangChain的开源项目Code Interpreter API](https://mp.weixin.qq.com/s/q5D4k4ZFxjRKX7LrKk3SEA)
- [2023-07-19，重磅！Meta发布LLaMA2，最高700亿参数，在2万亿tokens上训练，各项得分远超第一代LLaMA~完全免费可商用！](https://mp.weixin.qq.com/s/I-zU5n_dXKKMa2x9wyxYgw)
- [2023-07-22，关于大语言模型的11个应用方向和16个挑战总结：来自来自伦敦大学、MetaAI等机构合作的688篇参考文献与业界实践](https://mp.weixin.qq.com/s/fnyTrTAqFonrt1IxZnHRVw)
- [2023-07-23，一文总结13个国内外ChatGPT平替产品：是时候可以不那么依赖ChatGPT了~](https://mp.weixin.qq.com/s/QvVkTYDT6k2eado1HEWLbg)
- [2023-07-27，如何基于Gradio构建生成式AI的应用：吴恩达联合HuggingFace推出最新1小时短课](https://mp.weixin.qq.com/s/N0R2yC_zcmbWlbZZmXKwBQ)
- [2023-07-29，Open ChatGPT：一个整合了GPT-4和多模态能力的ChatGTP服务商](https://mp.weixin.qq.com/s/23_3sFZhIxP6FDiFsNwr4w)
- [2023-08-02，Megatron-LLM：支持大规模分布式语言模型(LLM)预训练和微调的库](https://mp.weixin.qq.com/s/WsK1MgMxIRf6RNWKzOUkOA)
- [2023-08-03，生成式AI领域拓展！MetaAI开源AudioCraft：一个支持AudioGen、MusicGen等模型的音频生成开发框架](https://mp.weixin.qq.com/s/OLLCiMqKHQJxGGR1sPA3qw)
- [2023-08-07，MetaGPT技术全解析：另一个AutoGPT，一个可以替代小型软件开发团队的LLM框架，产品经理、系统设计、代码实现一条龙](https://mp.weixin.qq.com/s/OteOLYsO6WoAjA1j3HMrbg)
- [2023-08-09，ChatGLM团队发布AI Agent能力评测工具AgentBench：GPT-4一骑绝尘，开源模型表现非常糟糕！](https://mp.weixin.qq.com/s/wUuAHsiZJmpCPn_3uvT4Aw)
- [2023-08-10，《流浪地球2》的数字生命计划可能快实现了！HeyGen即将发布下一代AI真人视频生成技术，效果逼真到无法几乎分辨！](https://mp.weixin.qq.com/s/70Fj9HCe3ruiI43WmMZLjQ)
- [2023-08-16，国产大模型与全球最强大模型大比拼：语义理解、数学推理同台竞技，究竟谁更厉害~](https://mp.weixin.qq.com/s/lVQorSHWUmYjDK2MgVm9bg)
- [2023-08-20，需要多少GPU显存才能运行预训练大语言模型？大语言模型参数规模与显存大小的关系估算方法~](https://mp.weixin.qq.com/s/-f9AY-nYRKaWKjDKhSW2iw)
- [2023-08-24，大规模中文开源数据集发布！2TB、几十亿条可商用的中文数据集书生·万卷 1.0开源~中文大模型能力可能要更上一层楼了！](https://mp.weixin.qq.com/s/ImCt2OgIt8W7-off8W7hxQ)
- [2024-04-06，高产的阿里！Qwen1.5系列再次更新：阿里开源320亿参数Qwen1.5-32B，评测超Mixtral MoE，性价比更高！](https://mp.weixin.qq.com/s/e_djuVBXtfttGmgCpw6UOw)
- [2024-04-10，重磅！Google开源CodeGemma编程大模型和基于RNN架构的新型大模型RecurrentGemma，同等参数规模表现优秀](https://mp.weixin.qq.com/s/58y65bUKFGYLo42nOXaGWQ)
- [2024-04-19，开源王者！全球最强的开源大模型Llama3发布！15万亿数据集训练，最高4000亿参数，数学评测超过GPT-4，全球第二！](https://mp.weixin.qq.com/s/m3rEZY-BFumitxBqg17Epw)
- 微信公众号「算法美食屋」
- [2023-07-03，60分钟吃掉ChatGLM2-6b微调范例~](https://mp.weixin.qq.com/s/Lf70i8M0KNDs9ZB8H32h4w)
- [2023-07-08，单样本微调给ChatGLM2注入知识~](https://mp.weixin.qq.com/s/hANR9OVDVEZMMvK8uxtChA)
- [2023-07-16，用Kaggle免费GPU微调ChatGLM2](https://mp.weixin.qq.com/s/PSWSN5OJfaSU8tLqOaZE3A)
- [2023-07-23，微调BaiChuan13B来做命名实体识别](https://mp.weixin.qq.com/s/ElEkYqRiEI8gKtO-cgnaXw)
- [2023-08-21，BaiChuan13B多轮对话微调范例](https://mp.weixin.qq.com/s/4RUP7VaHwn11UCogyjlb7g)
- [2023-09-03，9个范例带你入门LangChain](https://mp.weixin.qq.com/s/qHUxO6Ml-O1PCK1bc9uD7g)
- 微信公众号「KBQA沉思录」
- [2023-06-14，【中文医疗大模型】训练全流程源码剖析](https://mp.weixin.qq.com/s/DTHIxyDb9vG793hAKGLt2g)
- 微信公众号「技术狂潮AI」
- [2023-05-31，基于ChatGLM-6B构建本地私有化离线知识库](https://mp.weixin.qq.com/s/2TVP0WcLfLdnDQw88eGIGg)
- [2023-06-23，ChromaDB：开源向量嵌入数据库，让你的AI应用程序拥有记忆力](https://mp.weixin.qq.com/s/kqd41FeuQcy8ag8jQwEQNg)
- [2023-08-21，GPT-LLM-Trainer：如何使用自己的数据轻松快速地微调和训练LLM](https://mp.weixin.qq.com/s/9asqLJtvPins9NlZvaFziA)
- [2023-08-27，LangChain-Chatchat：基于LangChain和ChatGLM2-6B构建本地离线私有化知识库](https://mp.weixin.qq.com/s/dfJ2qajJrmu1kaAqyijLaw)
- 微信公众号「NLP日志录」
- [2023-06-16，WorkGPT：一个智能体框架，类似于AutoGPT或LangChain](https://mp.weixin.qq.com/s/OdRrAQcEMfuuT8xLFPijZQ)
- [2023-06-19，Awesome-Chinese-LLM：整理开源的中文大语言模型](https://mp.weixin.qq.com/s/bn97j_OKWPakwMDYQYEgyw)
- [2023-06-25，LLaMA Server：将LLaMA C++和Chatbot UI结合的LLaMA服务](https://mp.weixin.qq.com/s/-kNS6WX4OVCWS_mEHBL_rQ)
- [2023-06-26，什么是HuggingFace](https://mp.weixin.qq.com/s/EscXWBLM09bgfgfUT66C9Q)
- [2023-07-05，ChatGenTitle：使用百万arXiv论文信息在LLaMA模型上进行微调的论文题目生成模型](https://mp.weixin.qq.com/s/p3nxReh3-syDPSu6tK4PbA)
- [2023-07-14，eigenGPT：GPT2的最小化C++实现](https://mp.weixin.qq.com/s/ivVQxXUI-RP0rsYkSKg3zQ)
- [2023-07-17，开源版的OpenAI ChatGPT Code interpreter实现](https://mp.weixin.qq.com/s/7iDXnRm3j4-xkJLDxfVS_A)
- [2023-07-28，Chidori是一个LangChain的替代品](https://mp.weixin.qq.com/s/2p00yh65pb4dcDUTfRwJjQ)
- [2023-08-12，小米发布了他们的大模型MiLM-6B](https://mp.weixin.qq.com/s/kLpgRzy3j6fAqhM50cC2xg)
- [2023-08-14，VirtualWife - 一个虚拟主播项目](https://mp.weixin.qq.com/s/QgVfKx2CkUwDUIRTqFELqA)
- [2023-08-14，MeChat：中文心理健康支持对话大模型与数据集](https://mp.weixin.qq.com/s/yKxXi6SiIJpBhLozqe_XYQ)
- [2023-08-16，原Langchain-ChatGLM项目正式发布v0.2.0版本](https://mp.weixin.qq.com/s/fBPEE34_EBf_2-RM4ZqAzg)
- [2023-08-16，Llama2模型的优化版本：Llama-2-Onnx](https://mp.weixin.qq.com/s/Z2nBkaIgtLIa8OEeFJP4jg)
- [2023-08-16，妙鸭相机开源版工具FaceChain](https://mp.weixin.qq.com/s/qF7WVqHpMN1zODTe_W8J7A)
- [2023-08-18，仲景：首个实现从预训练到 RLHF 全流程训练的中文医疗大模型](https://mp.weixin.qq.com/s/Rhir7Il0NnsetzpX03Cjjw)
- [2023-08-18，书生·万卷多模态语料库](https://mp.weixin.qq.com/s/spl-N87mySAkRpBoMIYPuA)
- 微信公众号「NLP工程化」
- [2023-08-22，基于MovieChat视频理解的问答，能够在24GB显卡上处理10K帧视频](https://mp.weixin.qq.com/s/gBEmygej9mauJ-DMzVefjQ)
- [2023-08-22，llama2.c for Dummies：llama2.c手把手代码解析](https://mp.weixin.qq.com/s/AiFY_uu48KFX0hv3eDdAwQ)
- [2024-04-16，本地 LLM 推理项目大列表](https://mp.weixin.qq.com/s/3O6nAO8GN4eYQDefqJCfQQ)
- [2024-04-25，llama2.cpp：C++版本的Llama 2推理库](https://mp.weixin.qq.com/s/mr0aKhxV9V-vKeaJXOMijQ)
- [2024-05-17，LangChain Rust：用 Rust 语言实现的库](https://mp.weixin.qq.com/s/h82CidF97OAoQ-PmhzLBiQ)
- 微信公众号「NewBeeNLP」
- [2023-02-07，ChatGPT Prompt工程：设计、实践与思考](https://mp.weixin.qq.com/s/a8hjzZ_Rzl6pOU1PRAARJQ)
- [2023-07-19，谁才是真正的 OpenAI？更大更强的Llama 2来啦，可直接商用](https://mp.weixin.qq.com/s/2kN6hI17VpKEMgvK8iEqDg)
- [2023-02-07，ChatGPT Prompt工程：设计、实践与思考](https://mp.weixin.qq.com/s/a8hjzZ_Rzl6pOU1PRAARJQ)
- [2023-08-07，大模型时代必看！Open AI创始人演讲《State Of GPT》](https://mp.weixin.qq.com/s/gQ4LnMebEHvtBVt52oxT-w)
- [2023-04-11，OpenAI创始大神手搓千行C代码训练GPT，附PyTorch迁移教程](https://mp.weixin.qq.com/s/OBNqoZ6Iq9BVUWVrobYe7A)
- [2024-04-13，中山大学：“梗王”大模型，靠讲笑话登上CVPR](https://mp.weixin.qq.com/s/AeWCbKByO-fYFThSxOb43A)
- 微信公众号「AI寒武纪」
- [2023-06-19，重磅：未来10年生成式人工智能将这样影响所有人](https://mp.weixin.qq.com/s/qsLOke8-jckhF1XYxswFtQ)
- [2023-08-31，国内首批8个大模型正式获批上线](https://mp.weixin.qq.com/s/ElUjTKpDFG4vYmOjSZfM3g)
- [2024-04-10，【太疯狂了】用 1000 行纯 C 代码实现 GPT-2 训练：Andrej Karpathy重塑LLM训练格局](https://mp.weixin.qq.com/s/hNKWVqepbega6YPf48b8ag)
- [2024-04-12，【重磅】谷歌重塑Transformer：无限记忆力，无限长输入，LLM基础研究重大突破](https://mp.weixin.qq.com/s/bV2b9uJ4GFQPhhggHT3VIA)
- [2024-04-14，【全球黑客加持】Karpathy 1000行纯C训练大模型速度已追平PyTorch](https://mp.weixin.qq.com/s/VvwDhMmq80yN-Wcb8s3aiQ)
- 微信公众号「毫末智行」
- [2023-06-13，自动驾驶大模型亮相2023北京智源大会！顾维灏：DriveGPT将重塑汽车智能化技术路线](https://mp.weixin.qq.com/s/ybtjyY7gjgywl6Jvjd5RMg)
- 微信公众号「智源研究院」
- [2023-06-11，悟道·天鹰 Aquila + 天秤 FlagEval，打造大模型能力与评测标准双标杆](https://mp.weixin.qq.com/s/8oP9nongpkkfHuE1RsKx8A)
- [2023-08-15，FlagEval 8月榜单：新增通义千问、Llama2等多个模型评测，新增基座模型代码生成能力评测](https://mp.weixin.qq.com/s/RYccZXQNs9hHHNRJI9tLgg)
- 微信公众号「CLUE中文语言理解测评基准」
- [2023-06-19，最新大模型排名！中文大模型评测基准SuperCLUE发布6月榜单](https://mp.weixin.qq.com/s/lTqAOO8iqKUW3B_4VMswtw)
- [2023-07-20，Meta开源免费商用大模型Llama2-13B测评揭晓 | SuperCLUE](https://mp.weixin.qq.com/s/ZowePHkDouP8AiZshR-MXw)
- 微信公众号「AI范儿」
- [2023-06-09，Midjourney指令的终极列表：完整指南](https://mp.weixin.qq.com/s/wyAe6hDDusbSC6M2naAHVA)
- [2023-07-19，细观察 - Llama-2开源新闻刷屏背后...... 商用？没戏，“中文”被排除在外！](https://mp.weixin.qq.com/s/imVXxEJ4TJL3kRP2Aze2nA)
- 微信公众号「机器学习实验室」
- [2023-06-26，Midjourney 5.2震撼发布！](https://mp.weixin.qq.com/s/l8a6T2ha4q13go3dRbt8pA)
- [2023-07-06，「分割一切」视频版SAM来了！](https://mp.weixin.qq.com/s/FdbOe_kvFwDJxF2KMzUO5g)
- [2023-07-09，ChatGPT神器Code Interpreter来了！奉上一份保姆级教程](https://mp.weixin.qq.com/s/-PhTEwe8xZ3pXRck7imYsA)
- [2023-07-20，Meta 开源 Llama 2！大模型竞争格局变了](https://mp.weixin.qq.com/s/EesOpLmGDyvKSkiu2OlcgQ)
- 微信公众号「无数据不智能」
- [2023-05-31，WebGLM：10B 堪比 webGPT 175B](https://mp.weixin.qq.com/s/3bXpWUq6twqBmumU1xH0yg)
- [2023-06-14，一份大模型应用到各领域的综述，包括法律、金融、教育、软件工程、生物等等](https://mp.weixin.qq.com/s/dui1xcCIIVyBv-sLslHeTg)
- [2023-06-16，H2OGPT：开源代码仓库套件,开源GPT替代品,包括可商用的代码、数据、模型、微调工具](https://mp.weixin.qq.com/s/QIPMIqG8C8rNJqSKTjFWxg)
- [2023-06-17，macaw-llm：开源图像、音频、视频和文本的多模态语言建模模型](https://mp.weixin.qq.com/s/O3ryffaCghfU3_tUUu2TIA)
- [2023-07-05，GPT-Migrate：让你的项目轻松更换语言或框架](https://mp.weixin.qq.com/s/Cl5jvzoKe6kU7zeTi4plqA)
- [2023-07-09，让每个人都可以轻松、快速、廉价地使用vLLM进行服务](https://mp.weixin.qq.com/s/N1ursW7evovFsYKEc_x6NA)
- [2023-07-09，InternLM：强大的开源模型和弹性工作流程建设工具](https://mp.weixin.qq.com/s/OQLy7ZM81Cde0-Qba4sHMg)
- [2023-07-19，deepspeed发布0.10.0，加入ZeRO++：降低4倍网络通信，显著提高大模型及类ChatGPT模型训练效率](https://mp.weixin.qq.com/s/GWauayszfYWDV2pZr9Wf5g)
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- 微信公众号「AI浪潮时代」
- [2023-06-18，150个ChatGPT角色扮演指令，全网的角色扮演指令都在这里！让你的ChatGPT成为任何领域的专家（1/15）](https://mp.weixin.qq.com/s/T8A_FpFwOHHwsyvNggf7yA)
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- [2023-07-07，重大消息！GPT-4.0API，即将全面开发使用](https://mp.weixin.qq.com/s/sJT8Kj5GPxfLoaB4hCsueg)
- 微信公众号「深度学习自然语言处理」
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- 微信公众号「集智书童」
- [2023-06-28，MobileSAM来啦 | 比SAM小60倍，比FastSAM快4倍，速度和效果双赢](https://mp.weixin.qq.com/s/gTsdqVNgKpfnU-4S7DJhnA)
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- 微信公众号「分布式实验室」
- [2023-07-11，万字长文详解GPT](https://mp.weixin.qq.com/s/sBKaW5W_uyXxzUVx3nMYsg)
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- [2023-06-26，利用 GPT-4 & LangChain 本地部署企业知识库(技术篇)](https://mp.weixin.qq.com/s/-UNRLV9ttgI79A5iFmO7zQ)
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- [2023-07-06，SolidUI AI生成可视化，开创性开源项目，版本0.1.0 功能讲解](https://mp.weixin.qq.com/s/X0wxx9ZN982iOY6JzFBmAA)
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- [2023-07-11，华为盘古大模型3.0发布！](https://mp.weixin.qq.com/s/G9OEi27CeZJq7KVNF1U2sA)
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- 微信公众号「山行AI」
- [2023-06-17，基于LangChain的优秀项目资源库](https://mp.weixin.qq.com/s/G9aqBFzd5j8wVPTH160pZA)
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- 微信公众号「凤凰网科技」
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- 微信公众号「证券时报」
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- 微信公众号「关于NLP那些你不知道的事」
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- 微信公众号「前端立志传」
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- 微信公众号「AI的潜意识」
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- 微信公众号「HsuDan」
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- [2023-07-19，大模型社区再掀波澜，Meta重磅开源LLAMA-2，性能升级可商用](https://mp.weixin.qq.com/s/Eqh-ED4BgiR4BBQQbwXAmA)
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- [2023-07-19，高通携手Meta利用Llama 2赋能终端侧AI应用](https://mp.weixin.qq.com/s/LwWoDUMUN6Isdee2vzpUwg)
- 微信公众号「pythonLLM智能」
- [2023-07-19，更强的Llama 2开源，可直接商用](https://mp.weixin.qq.com/s/GcDo9jRv8xPhtuS30HNSNg)
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- [2023-07-26，欢迎加入Llama中文社区！](https://mp.weixin.qq.com/s/mYdQ8L-J9hD8g3kesjDYmw)
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- [2023-12-16，中文大模型预训练数据集介绍](https://zhuanlan.zhihu.com/p/672560962)
- [知乎「猛猿」](https://www.zhihu.com/people/lemonround)
- [2023-02-25，ChatGPT技术解析系列之：GPT1、GPT2与GPT3](https://zhuanlan.zhihu.com/p/609367098)
- [华尔街见闻](https://wallstreetcn.com/)
- [2023-07-12，5年20亿美元！毕马威与微软签了大单，会计师事务所要All In AI了](https://wallstreetcn.com/articles/3693053)
- [Jay Alammar](https://jalammar.github.io/)
- [2018-06-27，The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)
- 「[The official LangChain blog](https://blog.langchain.dev/)」
- [2023-07-18，Announcing LangSmith, a unified platform for debugging, testing, evaluating, and monitoring your LLM applications](https://blog.langchain.dev/announcing-langsmith/)
## Videos
- bilibili「OpenMMLab」
- [2024-01-03，书生·浦语大模型全链路开源体系](https://www.bilibili.com/video/BV1Rc411b7ns/)
- bilibili「ChatGLM」
- [2023-07-19，【官方教程】ChatGLM2-6B 部署与微调](https://www.bilibili.com/video/BV1D94y1i7Qp/)
- bilibili「二次元的Datawhale」
- [2023-04-25，学会如何使用大模型，让创意有能力落地成应用：HuggingLLM，Hugging未来](https://www.bilibili.com/video/BV1ek4y1J7Rd/)
## Jobs and Interview
- 微信公众号「CVHub」
- [2024-04-12，自变量机器人（X Square）- 具身智能企业招聘 | 机器人大模型、多模态、SLAM、机器人开发、仿真、ROS](https://mp.weixin.qq.com/s/H8r-KEiLso7JZTKO-PrjHA)
- 微信公众号「美团技术团队」
- [2024-03-21，美团自动配送车2024春季招聘 | 社招专场](https://mp.weixin.qq.com/s/2e0g-7fD8Fbp65LbjGdVnA)
- 微信公众号「AINLP」
- [2024-04-03，蚂蚁集团智能引擎团队招聘大模型相关算法工程师](https://mp.weixin.qq.com/s/Vre7ANtyAhTuQTxtRhUn9Q)
- [2024-04-19，【社招】NewsBreak北京招聘：多模态内容理解算法工程师（北京）](https://mp.weixin.qq.com/s/OOn5Y2_BBJyq4wuvHmU--A)
- 微信公众号「大模型生态圈」
- [2024-04-21，推理部署工程师面试题库](https://mp.weixin.qq.com/s/q46vKFPlQhcN7LyZNTRhXA)
- 微信公众号「AIGC小白入门记」
- [2024-04-20，面试小米汽车，不想去，拒了offer。。。](https://mp.weixin.qq.com/s/8UIlJwz3AJTTZNvwxLamtg)
- [2024-04-21，算法工程师面试常考手撕题（更新）](https://mp.weixin.qq.com/s/UlmNOIwohQJjl_UTpcw_uw)
- [2024-04-21，算法工程师面试题笔记](https://mp.weixin.qq.com/s/IKaLrqAeWyYes9mKMKZh0g)
## Star History
