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https://github.com/OpenLLMAI/OpenLLMWiki
OpenLLMWiki: Docs of OpenLLMAI. Survey, reproduction and domain/task adaptation of open source chatgpt alternatives/implementations. PiXiu-貔貅 means fortune.
https://github.com/OpenLLMAI/OpenLLMWiki
chatglm chatgpt chatgpt-alternatives chatgpt-app chatgpt-bot chatzhangsan chatzhihu chinese-chatgpt llama openllm openllmai
Last synced: 27 days ago
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OpenLLMWiki: Docs of OpenLLMAI. Survey, reproduction and domain/task adaptation of open source chatgpt alternatives/implementations. PiXiu-貔貅 means fortune.
- Host: GitHub
- URL: https://github.com/OpenLLMAI/OpenLLMWiki
- Owner: OpenLLMAI
- License: apache-2.0
- Created: 2023-04-13T17:01:13.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-12T18:18:59.000Z (4 months ago)
- Last Synced: 2024-06-06T15:59:24.018Z (about 1 month ago)
- Topics: chatglm, chatgpt, chatgpt-alternatives, chatgpt-app, chatgpt-bot, chatzhangsan, chatzhihu, chinese-chatgpt, llama, openllm, openllmai
- Homepage: https://zhuanlan.zhihu.com/p/622065348
- Size: 16.1 MB
- Stars: 244
- Watchers: 8
- Forks: 15
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Lists
- awesome-ChatGPT-repositories - ChatPiXiu - ChatPiXiu: Docs of OpenLLMAI. Survey, reproduction and domain/task adaptation of open source chatgpt alternatives/implementations. PiXiu-貔貅 means fortune. (Reimplementations)
README
ChatPiXiu
> **代码开源，大家放心使用，欢迎贡献! 注意：模型的license取决于模型提供方**
- [💥最新讯息](#最新讯息)
- [💫OpenNLP计划](#OpenNLP计划)
- [💫OpenChat-PiXiu](#ChatPiXiu项目)
- [🌟开源ChatGPT调研](#开源ChatGPT调研)
- [⛏️使用步骤](#使用步骤)
- [📄运行示例](#运行示例)
- [📄结果展示](#结果展示)
- [🛠️常见报错](#常见报错)
- [💐参考资料&致谢](#参考资料&致谢)
- [🌟赞助我们](#赞助我们)
- [🌈Starchart](#Starchart)
- [🏆Contributors](#Contributors)
## 最新讯息
- 2023/04/14：ChatPiXiu项目正式启动：
- 开源ChatGPT平替调研汇总
- 2023/05/15：60+开源项目，20+基础模型
- 2023/08/21：由羡鱼的个人项目catqaq迁移至OpenLLMAI
## OpenNLP计划
我们是谁？
我们是**羡鱼智能**【xianyu.ai】，主要成员是一群来自老和山下、西湖边上的咸鱼们，塘主叫作羡鱼，想在LLMs时代做点有意义的事！我们的口号是：**做OpenNLP和OpenX！希望在CloseAI卷死我们之前退出江湖！**
也许有一天，等到GPT-X发布的时候，有人会说NLP不存在了，但是我们想证明有人曾经来过、热爱过！在以ChatGPT/GPT4为代表的LLMs时代，在被CloseAI卷死之前，我们发起了OpenNLP计划，宗旨是OpenNLP for everyone!
- 【P0】[OpenTextClassification](https://github.com/catqaq/OpenTextClassification)：打造一流的文本分类项目，已开源
- 综述：done
- 开源项目：done
- papers解读：doing
- 炼丹术：done
- 【P0】OpenSE：句嵌入，自然语言处理的核心问题之一，doing
- 【P0】[ChatPiXiu](https://github.com/catqaq/ChatPiXiu)：ChatGPT开源平替及领域适应，doing
- 【P1】OpenLLMs：大语言模型，doing
- 【P2】OpenTextTagger：文本标注，分词、NER、词性标注等
- OpenX：任重而道远
## ChatPiXiu项目
ChatPiXiu项目为OpenNLP计划的第2个正式的开源项目，旨在Open ChatGPT for everyone！在以ChatGPT/GPT4为代表的LLMs时代，在被OpenAI卷死之前，做一点有意义的事情！未来有一天，等到GPT-X发布的时候，或许有人会说NLP不存在了，但是我们想证明有人曾来过！
### 1.开发计划
本项目的开发宗旨，打造全面且实用的ChatGPT模型库和文档库。**Eat every ChatGPT - Output your own chatbot!**
目前我们正在启动V1版本的开发，整体的开发计划如下，主要包括了文档和代码两类任务，数据的部分我们暂时将其分散到了各个子任务中。
**V1版本：资料调研+通用最小实现+领域/任务适配**
#### 1.1 文档分支
文档分支主要负责项目文档的建设，包括通用技术文档和项目相关文档。
**dev_for_docs**：文档分支，主要负责资料调研（算力有限，有调查才有训练权）：
1. 【P0】开源ChatGPT调研：持续更新，doing
2. 【P0】训练技术调研：持续更新，doing
3. 【P0】数据调研：doing
4. 【P1】部署技术调研：TODO
5. 【P2】基础模型调研：目前以LLaMA和GLM为主，doing
6. 【P3】技术解读/教程：doing
#### 1.2 代码分支
代码分支，负责具体的开发工作，包括数据处理、算法开发、算法评测等，分成通用最小实现和领域/任务适应两种，具体的：
**dev_for_chatmini**：通用最小实现分支，尽可能支持不同的基础模型和训练方式，提供可比较的实现。
1. 【P0】ChatGPT最小复现：完整的RLHF复现SFT-RM-PPO，doing
2. 【P0】适配不同的基座模型
3. 适配不同的PEFT算法
4. 【P2】探索新的训练方式
5. 【P3】探索知识迁移：比如蒸馏
**dev_for_chatzhihu**：知乎及问答领域适配，主要想解决一些知乎使用过程中的痛点，比如问题冗余、回答太多等等。
1. 【P0】收集知乎数据收集及处理
1. SFT数据
2. RLHF数据：答案打分
3. 摘要数据：答案/观点汇总、摘要
2. 【P0】基于知乎数据做SFT
3. 【P1】基于知乎数据做RLHF
4. 【P2】输出知乎LoRA
5. 【P3】和知乎热榜聊天的demo
**dev_for_chatzhangsan**：法律领域适配，张三犯了什么罪？
1. 【P0】法律领域数据收集及处理
2. 法律条文解释
3. 【P1】罪名判定：张三犯了什么罪？
更多领域，敬请期待！
ChatPiXiu-Eat every ChatGPT - Output your own chatbot!
### 2.加入我们
OpenNLP计划的其他内容尚在筹备中，暂时只开源了本项目和[OpenTextClassification](https://github.com/catqaq/OpenTextClassification)项目。欢迎大家积极参与ChatPiXiu的建设和讨论，一起变得更强！
加入方式：
- **项目建设**：可以在前面列出的开发计划中选择自己感兴趣的部分进行开发，建议优先选择高优先级的任务。包括资料调研和算法开发等工作。
- OpenLLM技术交流群：知识在讨论中发展，QQ群：740679327
- 技术分享和讨论：输出倒逼输入，欢迎投稿，稿件会同步到本项目的docs目录和知乎专栏OpenNLP. 同时也欢迎大家积极的参与本项目的讨论https://github.com/catqaq/ChatPiXiu/discussions。
## 开源ChatGPT调研
### 1.开源ChatGTP平替
注：开源类ChatGPT/LLM汇总，持续更新中，欢迎贡献! 现已超过60+！
| 项目 | 基础模型 | lang | 机构 | 数据集 | license | 介绍 | 备注 |
| ------------------------------------------------------------ | --------------------------------------------- | ----------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| [LLaMA](https://github.com/facebookresearch/llama) | LLaMA | Multi | meta | CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. | [Apache-2.0 license](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE) | 可能是目前开源ChatGPT用的最多的基础模型 | 支持多语言，但以英文为主 |
| [stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca) Alpaca | LLaMA | eng | stanford | [alpaca_data](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json) | [Apache-2.0 license](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE) | 指令调优的 LLaMA 模型: An Instruction-following LLaMA Model. 让 OpenAI 的 text-davinci-003 模型以 self-instruct 方式生成 52K 指令样本，SFT | FT模型语言以数据为准 |
| [ChatLLaMA](https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama) | LLaMA | | Nebuly+AI | - | [license](https://github.com/nebuly-ai/nebullvm/blob/main/apps/accelerate/chatllama/LICENSE) | 数据集创建、使用 RLHF 进行高效训练以及推理优化。 | |
| [Chinese-LLaMA-Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | LLaMA | mutli | [ymcui](https://github.com/ymcui) | - | [Apache-2.0 license](https://github.com/ymcui/Chinese-LLaMA-Alpaca/blob/main/LICENSE.md) | Chinese LLaMA & Alpaca LLMs; 中文词表扩充 | |
| [alpaca-lora](https://github.com/tloen/alpaca-lora) | LLaMA | | stanford | [LLaMA-GPT4 dataset](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) | [Apache-2.0 license](https://github.com/tloen/alpaca-lora/blob/main/LICENSE) | [LoRA](https://zhuanlan.zhihu.com/p/620327907) | |
| Chinese-alpaca-lora Luotuo-Chinese-LLM | LLaMA | | - | | | LoRA | |
| [ChatGLM](https://github.com/THUDM/ChatGLM-6B) | GLM | cn/eng | 清华 | 1T 标识符的中英双语数据 | [Apache-2.0 license](https://github.com/THUDM/ChatGLM-6B/blob/main/LICENSE) | 监督微调、反馈自助、人类反馈强化学习 | [PROJECT.md](https://github.com/THUDM/ChatGLM-6B/blob/main/PROJECT.md) |
| [FastChat](https://github.com/lm-sys/FastChat) Vicuna | LLaMA | eng | UC Berkeley, CMU, Stanford, UCSD and MBZUAI | ShareGPT, 70k问答指令数据 | [Apache-2.0 license](https://github.com/lm-sys/FastChat/blob/main/LICENSE) | SFT，使用GPT-4作为评判标准，结果显示Vicuna-13B在超过90%的情况下实现了与ChatGPT和Bard相匹敌的能力。 | |
| Chinese-Vicuna | LLaMA | cn | - | - | [Apache-2.0 license](https://github.com/Facico/Chinese-Vicuna/blob/master/LICENSE) | LoRA | |
| [EasyLM](https://github.com/young-geng/EasyLM) Koala考拉 | LLaMA multi | eng | UC伯克利 | ChatGPT数据和开源数据（Open Instruction Generalist (OIG)、斯坦福 Alpaca 模型使用的数据集、Anthropic HH、OpenAI WebGPT、OpenAI Summarization） | [Apache-2.0 license](https://github.com/Facico/Chinese-Vicuna/blob/master/LICENSE) | SFT/13B/500k条数据 | |
| [ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) | LLaMA | | ColossalAI | [InstructionWild](https://github.com/XueFuzhao/InstructionWild):104K bilingual datasets | [LICENSE](https://github.com/hpcaitech/ColossalAI/blob/main/applications/Chat/LICENSE) | SFT-RM-RLHF | |
| [ChatRWKV](https://github.com/BlinkDL/ChatRWKV) | RWKV | | [BlinkDL](https://github.com/BlinkDL) | - | [Apache-2.0 license](https://github.com/BlinkDL/ChatRWKV/blob/main/LICENSE) | ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source. | |
| [ChatYuan](https://github.com/clue-ai/ChatYuan) | T5 | eng/cn | 元语智能 | PromptClue | [LICENSE](https://github.com/clue-ai/ChatYuan/blob/main/LICENSE) | 基于PromptClue进行了监督微调 | |
| [OpenChatKit](https://github.com/togethercomputer/OpenChatKit) | GPT-NoX-20B | | Together+LAION+Ontocord.ai | OIG-43M | [Apache-2.0 license](https://github.com/togethercomputer/OpenChatKit/blob/main/LICENSE) | 60亿参数的审核模型，对不合适或者是有害的信息进行过滤 | |
| BELLE | Bloom LLama | cn | [LianjiaTech](https://github.com/LianjiaTech) | 10M-ChatGPT生成的数据 | [Apache-2.0 license](https://github.com/LianjiaTech/BELLE/blob/main/LICENSE) | SFT | |
| PaLM-rlhf-pytorch | PaLM | | [lucidrains](https://github.com/lucidrains) | - | [MIT license](https://github.com/lucidrains/PaLM-rlhf-pytorch/blob/main/LICENSE) | RLHF | PaLM太大了 |
| [dolly](https://github.com/databrickslabs/dolly) | v1:GPT-J-6B v2:pythia | eng | Databricks | The Pile+databricks-dolly-15k | [MIT license](https://github.com/lucidrains/PaLM-rlhf-pytorch/blob/main/LICENSE) | 参考Alpaca; dolly-v2-12b based on pythia-12b | |
| LMFlow | LLaMA | | [OptimalScale](https://github.com/OptimalScale) | | | An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Model for All. LLaMA-7B，一张3090耗时 5 个小时 | |
| GPTrillion | - | | - | - | - | 1.5万亿，多模态 | |
| [open_flamingo](https://github.com/mlfoundations/open_flamingo) | LLaMA CLIP | | LAION | [Multimodal C4](https://github.com/allenai/mmc4) | [MIT license](https://github.com/mlfoundations/open_flamingo/blob/main/LICENSE) | | |
| [baize-chatbot](https://github.com/project-baize/baize-chatbot) | LLaMA | eng | [project-baize](https://github.com/project-baize) | 100k dialogs generated by letting ChatGPT chat with itself. | [GPL-3.0 license](https://github.com/project-baize/baize-chatbot/blob/main/LICENSE) | LoRA | |
| [ChatPiXiu](https://github.com/catqaq/ChatPiXiu) | multi | | 羡鱼智能 | - | - | LoRA | 筹备阶段 |
| [stackllama](https://huggingface.co/blog/stackllama) | LLaMA | | Hugging Face | - | - | 用RLHF训练LLaMA的实践指南 | |
| [Lit-LLaMA](https://github.com/Lightning-AI/lit-llama) | LLaMA | multi | lightening-ai | - | [Apache-2.0 license](https://github.com/Lightning-AI/lit-llama/blob/main/LICENSE) | 重写重训LLaMA,绕开license: Implementation of the LLaMA language model based on nanoGPT. | 可商用版LLaMA |
| [OPT](https://arxiv.org/abs/2205.01068) | OPT | eng | meta | - | [MIT license](https://github.com/facebookresearch/metaseq/blob/main/LICENSE) | 当年对标GPT3的模型 | |
| [Cerebras-GPT](https://huggingface.co/cerebras/Cerebras-GPT-13B) | Cerebras-GPT | eng | Cerebras | The Pile | [Apache-2.0 license](https://github.com/Cerebras/modelzoo/blob/main/LICENSE) | GPT-3 style; 最小 1.11 亿，最大 130 亿，共 7 个模型 | |
| BLOOM | BLOOM | multi | [bigscience](https://huggingface.co/bigscience) | Total seen tokens: 366B | 代码：[Apache-2.0 license ](https://github.com/huggingface/transformers-bloom-inference/blob/main/LICENSE)模型：RAIL License v1.0 | 176B；46 种自然语言（包括中文）和 13 种编程语言 | |
| GPT-J | GPT-3 | multi | EleutherAI | The Pile | [apache-2.0](https://huggingface.co/models?license=license:apache-2.0) | based on GPT-3; | |
| GPT-2 | | | | | | | |
| [RWKV](https://github.com/BlinkDL/RWKV-LM) | [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) | cn/eng | [BlinkDL](https://github.com/BlinkDL) | | | | 纯RNN |
| [鹏程・盘古 α](https://www.oschina.net/p/pangu-alpha) | | cn | 鹏城 | 2TB | [Apache License 2.0](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha/src/branch/master/LICENSE) | | |
| [鹏程・盘古对话](https://www.oschina.net/p/pangu-dialog) | | cn | 鹏城 | | | | |
| [悟道](https://www.oschina.net/p/wudao-model) | | cn/eng | BAAI(智源) | | | 多模态; 1.75 万亿参数; 图文：CogView、BriVL；文本：GLM、CPM、Transformer-XL、EVA、Lawformer；生物：ProtTrans | |
| [MOSS](https://www.oschina.net/p/moss) | MOSS | cn/eng | [OpenLMLab](https://github.com/OpenLMLab) | 700B tokens | 代码Apache 2.0，数据CC BY-NC 4.0，模型权重GNU AGPL 3.0 | 支持中英双语和多种插件; 基座moss-moon-003-base | |
| [伶荔 (Linly)](https://www.oschina.net/p/linly) | LLaMA | cn | [CVI-SZU](https://github.com/CVI-SZU) | | Apache Licence 2.0 | 33B 的 Linly-Chinese-LLAMA 是目前最大的中文 LLaMA 模型 | |
| [华驼 (HuaTuo)](https://www.oschina.net/p/huatuo-llama) | LLaMA | cn/eng | [SCIR-HI](https://github.com/SCIR-HI) | | [Apache-2.0 license](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese/blob/main/LICENSE) | | 医学 |
| [BBT-2](https://www.oschina.net/p/bbt-2) | | cn/eng | | | | 120 亿参数的通用大语言模型 | |
| [CodeGeeX](https://www.oschina.net/p/codegeex) | - | code | 鹏城 | | | 130 亿参数的多编程语言代码生成预训练模型 | code |
| [RedPajama](https://www.oschina.net/p/redpajama) | gpt-neox | eng | Together、Ontocord.ai、ETH DS3Lab、斯坦福大学 CRFM、Hazy Research 和 MILA 魁北克 AI 研究所 | 800B/1T | Apache-2.0 | 开源地全面对齐LLaMA的训练数据集 | |
| [OpenAssistant](https://www.oschina.net/p/open-assistant) | | eng | [LAION-AI](https://github.com/LAION-AI) | | [Apache-2.0 license](https://github.com/LAION-AI/Open-Assistant/blob/main/LICENSE) | OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so. | |
| [StableLM](https://www.oschina.net/p/stablelm) | pythia | eng | [Stability-AI](https://github.com/Stability-AI) | | [Apache-2.0 license](https://github.com/Stability-AI/StableLM/blob/main/LICENSE) | Stability AI Language Models; max len 4096 | |
| [StarCoder](https://www.oschina.net/p/starcoder) | | code | [bigcode-project](https://github.com/bigcode-project) | | [Apache-2.0 license](https://github.com/bigcode-project/starcoder/blob/main/LICENSE) | | code |
| [SantaCoder](https://www.oschina.net/p/santacoder) | | code | [bigcode](https://huggingface.co/bigcode) | The Stack（v1.1） | the BigCode OpenRAIL-M v1 license | 轻量级 AI 编程模型，1.1B | code |
| [MLC LLM](https://www.oschina.net/p/mlc-llm) | | - | [mlc-ai](https://github.com/mlc-ai) | - | [Apache-2.0 license](https://github.com/mlc-ai/mlc-llm/blob/main/LICENSE) | 本地大语言模型部署；Enable everyone to develop, optimize and deploy AI models natively on everyone's devices. | |
| [Web LLM](https://www.oschina.net/p/web-llm) | | | [mlc-ai](https://github.com/mlc-ai) | | [Apache-2.0 license](https://github.com/mlc-ai/web-llm/blob/main/LICENSE) | Bringing large-language models and chat to web browsers. | |
| [WizardLM](https://www.oschina.net/p/wizardlm) | LLaMA | eng | [nlpxucan](https://github.com/nlpxucan) | | | Evol-Instruct | |
| [YaLM 100B](https://www.oschina.net/p/yalm-100b) | | eng/russian | [yandex](https://github.com/yandex) | | [Apache-2.0 license](https://github.com/yandex/YaLM-100B/blob/main/LICENSE) | | |
| [OpenLLaMA](https://www.oschina.net/p/openllama) | | multi | [s-JoL](https://github.com/s-JoL) | | [MIT license](https://github.com/s-JoL/Open-Llama/blob/main/LICENSE) | LLaMA 开源复现版 | |
| BiLLa | LLaMA | cn/eng | [Neutralzz](https://github.com/Neutralzz) | | | A Bilingual LLaMA with Enhanced Reasoning Ability | |
| pandallm | LLaMA | cn/eng | [dandelionsllm](https://github.com/dandelionsllm) | | Apache-2.0 license | | |
| pandalm | | | [WeOpenML](https://github.com/WeOpenML) | | [Apache-2.0 license](https://github.com/WeOpenML/PandaLM/blob/main/LICENSE) | PandaLM：可重现和自动化的语言模型评估 | |
| [gpt4all](https://github.com/nomic-ai/gpt4all) | | | [nomic-ai](https://github.com/nomic-ai) | | [MIT license](https://github.com/nomic-ai/gpt4all/blob/main/LICENSE.txt) | gpt4all: an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue | |
| [stable-vicuna](https://huggingface.co/CarperAI/stable-vicuna-13b-delta) | | eng | [CarperAI](https://huggingface.co/CarperAI) | | [CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) | StableVicuna-13B is a Vicuna-13B v0 model fine-tuned | |
| [MPT](https://huggingface.co/mosaicml/mpt-7b) | MPT | eng | [mosaicml](https://huggingface.co/mosaicml) | 1T tokens of English text and code | Apache-2.0 | ALiBi保证了良好的长度外推性 | |
| ImageBind | | 多模态 | meta | | | One embedding space to bind them all. | |
| [Phoenix](https://github.com/FreedomIntelligence/LLMZoo) | | multi | CUHK | | | 7B/BLOOMZ + 微调 | |
| [ChatPLUG](https://github.com/X-PLUG/ChatPLUG) | | | Alibaba | | | Encoder-Decoder/3.7B | |
| [BLOOMZ](https://github.com/bigscience-workshop/xmtf) | | multi | BigScience | | | BLOOM + 多任务微调 | |
| [CPM-Ant+](https://github.com/OpenBMB/CPM-Live/tree/cpm-ant-plus/cpm-live) | | cn/eng | OpenBMB | | | 10B/Decoder-only(UniLM) | |
| [PaLM](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html) | | multi | Google | | - | pathways/540B | 未开源 |
| [PaLM 2](https://blog.google/technology/ai/google-palm-2-ai-large-language-model/) | | multi | Google | | - | pathways; 改进的多语言、推理和编码能力 | 未开源 |
| | | | | | | | |
### 2.基础模型
注：基础LLM汇总，持续更新中，欢迎贡献! 现已超过15+！个人的工作和研究兴趣会更关注基础模型相关技术及其应用！
| model | Architecture/task | lang | tokenizer | vocab | PE | max len | size | org | data | license | intro | notes |
| ------------------------------------------------------------ | --------------------------- | ------ | --------- | ---------------------------------------- | -------------------- | ------------------------------ | --------------------------------------- | --------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| [LLaMA](https://huggingface.co/decapoda-research/llama-7b-hf) | decoder/LM | multi | BBPE | 32K | RoPE | 2048 | 7B/13B/33B/65B | meta | 1.4万亿 tokens | [GPL-3.0 license](https://github.com/facebookresearch/llama/blob/main/LICENSE) | Meta 大语言模型: The model comes in different sizes: 7B, 13B, 33B and 65B parameters. | |
| GLM | mix/自回归式填空，prefix LM | cn/eng | multi | multi | RoPE | 2048 for 130B 1024 for ChatGLM | 6B/10B/130B | 智谱 | 中英文token各2000亿 | [Apache-2.0 license](https://github.com/THUDM/ChatGLM-6B/blob/main/LICENSE) | ChatGLM-6B: 1T data | |
| GPT1 | decoder/LM | eng | BPE | 40478 | learned | 512 | | OpenAI | BooksCorpus/5 G | [MIT License](https://github.com/openai/finetune-transformer-lm/blob/master/LICENSE) | GPT系列的起源 | 分词部分有特殊处理，详见tokenization_openai.py： - lowercases all inputs, - uses `SpaCy` tokenizer and `ftfy` for pre-BPE tokenization if they are installed, fallback to BERT's `BasicTokenizer` if not. |
| [GPT2](https://huggingface.co/gpt2-xl) | decoder/LM | multi | BBPE | 50257 | learned | 1024 | 124M/355M/774M/1.5B | OpenAI | WebText/ 40G | [Modified MIT License](https://github.com/openai/gpt-2/blob/master/LICENSE) | 主打zero-shot | GPT系列开源的最后一舞 |
| GPT3 | decoder/LM | multi | BBPE | 50257/davinci | learned | 2048 | 175B | OpenAI | 570G | | sparse attention；主打few-shot/in-context learning | 关上了GPT系列的大门 |
| GPT3.5 | decoder/LM | multi | BBPE | 100256 | ~learned | 4096 | | OpenAI | - | - | InstructGPT等一系列模型 | 未开源 |
| GPT4 | decoder/LM | multi | BBPE | 100256 | ~learned | 32768 | | OpenAI | - | - | 多模态 | 未开源 |
| [BLOOM](https://huggingface.co/bigscience/bloom) | decoder/LM | multi | BBPE | 250,680 | ALiBi | 2048 | 176B | bigscience | [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus) | RAIL License v1.0 | Modified from Megatron-LM GPT2; StableEmbedding | 包括Training logs |
| PaLM | | | | | RoPE | | | | | | | |
| Chinchilla | | | | | transformer-XL style | | | | | | | |
| OPT | | eng | | | learned | | | | | | | |
| [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6b) | decoder/LM | multi | BBPE | 50257/50400† (same tokenizer as GPT-2/3) | RoPE | 2048 | 6B | EleutherAI | The Pile | [apache-2.0](https://huggingface.co/models?license=license:apache-2.0) | based on GPT-3; | |
| [Lit-LLaMA](https://github.com/Lightning-AI/lit-llama) | | | | | | | | | | | Implementation of the LLaMA language model based on nanoGPT. | |
| [Cerebras-GPT](https://huggingface.co/cerebras/Cerebras-GPT-13B) | decoder | eng | BPE | 50257 | Learned | 2048 | | [Cerebras Systems](https://www.cerebras.net/) | The Pile | Apache 2.0 | The family includes 111M, 256M, 590M, 1.3B, 2.7B, 6.7B, and 13B models. All models in the Cerebras-GPT family have been trained in accordance with Chinchilla scaling laws (20 tokens per model parameter) which is compute-optimal. | |
| [RWKV](https://github.com/BlinkDL/RWKV-LM) | RNN | eng/cn | | | None(pure RNN) | 1024/4096/8192 | [multi](https://huggingface.co/BlinkDL) | | the Pile | | 结合了 RNN 和 Transformer 的语言模型，适合长文本，运行速度较快，拟合性能较好，占用显存较少，训练用时较少。RWKV 整体结构依然采用 Transformer Block 的思路，相较于原始 Transformer Block 的结构，RWKV 将 self-attention 替换为 Position Encoding 和 TimeMix，将 FFN 替换为 ChannelMix。其余部分与 Transfomer 一致。 | |
| CoLT5 | | | | | T5 bias style | | | | | | | |
| MOSS | decoder/LM | cn/eng | | | | 2048 | 16B | [OpenLMLab](https://github.com/OpenLMLab) | 700B tokens | 代码Apache 2.0，数据CC BY-NC 4.0，模型权重GNU AGPL 3.0 | 支持中英双语和多种插件 | |
| [MPT](https://huggingface.co/mosaicml/mpt-7b) | decoder/LM | eng | BBPE | 50432 | ALiBi | 2048/65k/84k | 7B | [mosaicml](https://huggingface.co/mosaicml) | 1T tokens+各种FT数据 | Apache-2.0 | Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. The model vocabulary size of 50432 was set to be a multiple of 128 (as in MEGATRON-LM) | |
| [mt5](https://huggingface.co/google/mt5-xxl) | encoder-decoder | multi | | | | | 1.2B/3.7B/13B | Google | mC4 | | | |
| [Wenzhong2.0-GPT2-3.5B-chinese](https://huggingface.co/IDEA-CCNL/Wenzhong2.0-GPT2-3.5B-chinese) | decoder/LM | cn | | | | | 3.5B | IDEA-CCNL | | | | |
| CPM-Generate | | cn | | | | | 2.6B | TsinghuaAI | 100GB Chinese training data | | | |
| [bloom-zh](https://huggingface.co/Langboat/bloom-1b4-zh) | decoder/LM | cn | BBPE | 46145 | ALiBi | 2048 | 1.4B/2.5B/6.4B | Langboat | - | | 词表裁剪，保留中文: 250880 to 46145 | |
| [GPT-2B](https://huggingface.co/nvidia/GPT-2B-001) | decoder/LM | eng | BBPE | | RoPE | 4096 | | HuggingFace+Nvidia | 1.1T tokens | | | |
| | | | | | | | | | | | | |
### 3.数据
| dataset | type | 机构 | 大小 | license | 介绍 | 备注 |
| --------------------------- | ----------- | ----------------------------------- | ---- | ------- | ---- | ---- |
| alpaca_data | Instruction | stanford | | | | |
| alpaca_chinese_dataset | \- | hikariming | | | | |
| Multilingual Instruction | | Guanaco | | | | |
| alpaca_chinese_dataset | | carbonz0 | | | | |
| 0.5M+1M chinese instruction | | LianjiaTech | | | | |
| shareGPT | | [lm-sys](https://github.com/lm-sys) | | | | |
| | | | | | | |
### 4.产品
注：为了文档的完整性，将工业界的ChatGPT也进行了汇总，只做介绍不做比较，以免争议!
| model | org | intro | notes |
| ------------------------------------------------- | --------- | ----- | ----- |
| [ChatGPT-GPT-3.5-turbo](https://chat.openai.com/) | OpenAI | | |
| [ChatGPT-GPT-4](https://chat.openai.com/) | OpenAI | | |
| Claude | Anthropic | | |
| 文心一言 | 百度 | | |
| 星火人知大模型 | 讯飞 | | |
| ChatGLM | 清华/智谱 | | |
| MiniMax | MiniMax | | |
| 通义千问 | 阿里 | | |
| Bard | Google | | |
| | | | |
### 5.训练&部署
#### 5.1 训练
| 框架 | type | 机构 | 兼容性 | license | 介绍 | 备注 |
| ------------------------------------------------------------ | ------- | ----------------------------------------- | ---------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---- |
| [ColossalAI](https://github.com/hpcaitech/ColossalAI) | general | [hpcaitech](https://github.com/hpcaitech) | 高 | [Apache-2.0 license](https://github.com/hpcaitech/ColossalAI/blob/main/LICENSE) | Colossal-AI: Making large AI models cheaper, faster, and more accessible
支持ChatGPT完整复现 | |
| [RLHF](https://github.com/sunzeyeah/RLHF) | RL | [sunzeyeah](https://github.com/sunzeyeah) | 基于transformers库实现 | \- | Implementation of Chinese ChatGPT.
SFT、Reward Model和RLHF | |
| trlx | RL | [CarperAI](https://github.com/CarperAI) | 强大的transformer 强化学习库 | [MIT license](https://github.com/CarperAI/trlx/blob/main/LICENSE) | A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
不支持自定义预训练模型。 | |
| [trl](https://github.com/lvwerra/trl) | RL | Hugging Face | 基于transformers | [Apache-2.0 license](https://github.com/lvwerra/trl/blob/main/LICENSE) | 只要是基于ransformers 库开发的预训练库，均可适配，强烈推荐 | |
| [DeepSpeed-Chat](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat) | general | [microsoft](https://github.com/microsoft) | 基于DeepSpeed | [Apache-2.0 license](https://github.com/microsoft/DeepSpeedExamples/blob/master/LICENSE) | **训练速度**大幅提升 | |
| [nanoGPT](https://github.com/karpathy/nanoGPT) | GPT | [karpathy](https://github.com/karpathy) | | [MIT license](https://github.com/karpathy/nanoGPT/blob/master/LICENSE) | The simplest, fastest repository for training/finetuning medium-sized GPTs. | |
| | | | | | | |
#### 5.2 部署
## 使用步骤：TODO
1.克隆本项目
`git clone https://github.com/catqaq/ChatPiXiu.git`
2.准备数据
3.运行示例
## 结果展示
## 常见报错
## 参考资料&致谢
【OpenLLM 011】ChatPiXiu项目-可能是全网最全的ChatGPT复现调研：54+开源ChatGPT平替项目，15+基础模型，8+ ChatGPT产品！ - 羡鱼智能的文章 - 知乎 https://zhuanlan.zhihu.com/p/629364056
开源ChatGPT替代模型项目整理https://zhuanlan.zhihu.com/p/618790279
平替chatGPT的开源方案 https://zhuanlan.zhihu.com/p/618926239ChatGPT/GPT4
开源“平替”汇总https://zhuanlan.zhihu.com/p/621324917
完整版 ChatGPT 克隆方案，开源了！https://zhuanlan.zhihu.com/p/617996976
ColossalChat：完整RLHF平替ChatGPT的开源方案 https://zhuanlan.zhihu.com/p/618048558ChatGPT
开源平替来了，开箱即用！前OpenAI团队打造，GitHub刚发布就揽获800+星https://zhuanlan.zhihu.com/p/613556853
LoRA:大模型的低秩适配-最近大火的lora到底是什么东西？为啥stable diffusion和开源ChatGPT复现都在用？https://zhuanlan.zhihu.com/p/620327907?
成本不到100美元！UC伯克利再开源类ChatGPT模型「考拉」：数据量大没有用，高质量才是王道https://zhuanlan.zhihu.com/p/621078208
ChatGPT平替方案汇总https://zhuanlan.zhihu.com/p/618839784
微软宣布开源 Deep Speed Chat，可将训练速度提升 15 倍以上，哪些信息值得关注？https://www.zhihu.com/question/595311294
总结当下可用的大模型LLMshttps://zhuanlan.zhihu.com/p/611403556
可能是最全的开源 LLM （大语言模型）整理
https://my.oschina.net/oscpyaqxylk/blog/8727824
[资源整理]2023-05-11比较全的LLMs的资源整理 - 迷途小书僮的文章 - 知乎 https://zhuanlan.zhihu.com/p/628637821
中文开源1B以上大模型汇总 - nghuyong的文章 - 知乎 https://zhuanlan.zhihu.com/p/613239726
https://github.com/CLUEbenchmark/SuperCLUE
https://github.com/THUDM/ChatGLM-6B
一文汇总开源大语言模型，人人都可以拥有自己的ChatGPT - 无忌的文章 - 知乎 https://zhuanlan.zhihu.com/p/622370602
新发布的一些开源商用模型 - LokLok的文章 - 知乎 https://zhuanlan.zhihu.com/p/627785493
最新发布！截止目前最强大的最高支持65k输入的开源可商用AI大模型：MPT-7B！ - 数据学习的文章 - 知乎 https://zhuanlan.zhihu.com/p/627420365
复旦团队大模型 MOSS 开源了，有哪些技术亮点值得关注？ - 孙天祥的回答 - 知乎 https://www.zhihu.com/question/596908242/answer/2994534005
暴击专家模型！Meta最新多模态大模型ImageBind已开源 - 新智元的文章 - 知乎 https://zhuanlan.zhihu.com/p/628370318
整理开源可用的中文大模型LLMs - 罗胤的文章 - 知乎 https://zhuanlan.zhihu.com/p/616812772
大模型热点论文：谷歌推出 PaLM 2、Meta 开源 ImageBind - MegEngine Bot的文章 - 知乎 https://zhuanlan.zhihu.com/p/628941792
如何评价Google最新发布的PaLM2，效果反超GPT4？ - 一堆废纸的回答 - 知乎 https://www.zhihu.com/question/600311066/answer/3022625910
两大可商用开源大模型同时发布！性能不输LLaMA，羊驼家族名字都不够用了 - 量子位的文章 - 知乎 https://zhuanlan.zhihu.com/p/627454901
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