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 Large Language Model & GPT-4 Tech and Industry Resource Map ================================ (大语言模型 & 多模态大模型 & 生成式预训练Transformer-N 技术与产业资源汇总) | | | | -------------------------------------- | ----------------------------------------------------- | | **1. Discussion Groups(讨论小组汇总)** | **5. Model Resources(模型资源)** | | **2. Tech Guide(技术入门)** | **6. Application Open Source Projects(应用开源项目)** | | **3. Investment Analysis(投资分析)** | **7. Related Discussion(相关讨论)** | | **4. Industry Analysis(产业分析)** | **8. Web& Paper (网页论文资源)** | This resource map is for my child and young developers that will face a AGI world in the future. It has been created to help equip children and young developers with the knowledge and skills that will be necessary to navigate the rapidly-evolving world of AGI. As we continue to develop and rely on AI technologies, it is becoming increasingly important for younger generations to be prepared for the challenges and opportunities that lie ahead. In order to ensure that they are ready for this future, we have compiled a comprehensive list of resources and tools that will help them to understand the basics of AGI. ## 1. Discussion Groups(讨论小组汇总) | 大模型算法技术 | 大模型投资创业 | 大模型应用方案 | 大模型算力供需 | | -------------------------------------- | -------------------------------------- | ---------------------------- | ---------------------------- | | AI大模型·GPT-4算法技术讨论群-2 | AI大模型·GPT-4创业投资讨论群-2 | AI大模型·GPT-4应用方案讨论群 | AI大模型·GPT-4算力供需交流群 | | http://c.nxw.so/cgpt 点入链接加群助手 | http://c.suo.nz/cinv 点入链接加群助手 | | | | **GPGPU** | **存算一体/存储器** | **车规与域控芯片** | **EDA大模型** | | -------------------------- | ---------------------------- | ---------------------------- | ------------------------ | | GPGPU与先进GPU设计讨论群-2 | 存算一体与存储器技术讨论群-2 | 车规与域控制器芯片设计讨论群 | 开源EDA与EDA大模型讨论群 | | | | | | | **DSA** | **AI芯片与GPGPU编译器** | **RISC-V** | | | --------------------- | ------------------------- | ---------------------- | ---- | | AI芯片与DSA设计讨论群 | AI芯片与GPGPU编译器讨论群 | RISC-V架构与设计讨论群 | | | | | | | ## 2. Tech Guide(技术入门) ### 2.1 GPT-4 Tech Report(GPT-4技术报告) | Classification | Article | |----------------|---------------------------------------------------------------------------------------| | | 陈巍：GPT-4核心技术分析报告（2）——GPT-4的技术分析（收录于GPT-4/ChatGPT技术与产业分析） | | | https://zhuanlan.zhihu.com/p/620087339 | | | 陈巍：GPT-4核心技术分析报告（5）——GPT-4的算力要点与芯片（收录于GPT-4/ChatGPT技术与产业分析）| | | https://zhuanlan.zhihu.com/p/611464068 | | | 陈巍谈芯：GPT-4大模型硬核解读 （收录于GPT-4/ChatGPT技术与产业分析） | | | https://mp.weixin.qq.com/s/nV2ynNtKmMNkADA8Wg4TVQ | ### 2.2 ChatGPT Tech Report(ChatGPT技术报告) | Classification | Article | | -------------- | ------------------------------------------------------------ | | | 陈巍：ChatGPT发展历程、原理、技术架构和产业未来 （收录于GPT-4/ChatGPT技术与产业分析） | | | <https://zhuanlan.zhihu.com/p/590655677> | | | 陈巍：ChatGPT报告：技术详解和产业未来（slide形式，替换了一些新的内容） | | | <https://zhuanlan.zhihu.com/p/608917240> | | | ChatGPT/InstructGPT详解 | | | <https://zhuanlan.zhihu.com/p/590311003> | | | 【强化学习 229】ChatGPT/InstructGPT | | | <https://zhuanlan.zhihu.com/p/589827115> | | | OpenAI的AGI语言智能演进之路：GPT1到ChatGPT | | | <https://zhuanlan.zhihu.com/p/597263206> | ### 2.3 Large Language Model Technoglogy(大模型技术) | Classification | Article | | ----------------- | ------------------------------------------------------------ | | Basic | 通向AGI之路：大型语言模型（LLM）技术精要 | | | https://zhuanlan.zhihu.com/p/597586623 | | RLHF | 解读 ChatGPT 背后的技术重点：RLHF、IFT、CoT、红蓝对抗 | | | https://zhuanlan.zhihu.com/p/602458131 | | Training(训练) | 为什么chatgpt的上下文连续对话能力得到了大幅度提升？ | | | https://www.zhihu.com/question/575481512/answer/2852937178 | | PPO算法讲解 | Proximal Policy Optimization (PPO) Explained | | | https://towardsdatascience.com/proximal-policy-optimization-ppo-explained-abed1952457b | | PPO算法讲解 | PARL框架下简单入门 Proximal Policy Optimization (PPO) | | | https://aistudio.baidu.com/aistudio/projectdetail/632270 | | Transformer | Transformer 之功能概览 | | | https://zhuanlan.zhihu.com/p/604444663 | | Transformer | Transformer模型详解 | | | https://zhuanlan.zhihu.com/p/338817680 | | Prompt | Prompt-based Language Models：模版增强语言模型小结 | | | https://zhuanlan.zhihu.com/p/366771566 | | Chain of Thoughts | 有了Chain of Thought Prompting，大模型能做逻辑推理吗？ | | | https://zhuanlan.zhihu.com/p/589087074 | | Knowledge Base | Quivr 基于OpenAI Embeddings构建本地知识库 | | | https://zhuanlan.zhihu.com/p/631038668 | | GPT Cache | GPTCache：通过缓存LLM查询成本降低 10 倍，速度提高 100 倍 | | | https://zhuanlan.zhihu.com/p/645601760 | ### 2.4 Application Guide(应用指南) | Classification | Article | | -------------- | ------------------------------------------------------------ | | Basic | LLMsPracticalGuide | | | https://github.c