高质量中文预训练模型;大模型;多模态模型;大语言模型集合.zip

# Awesome Pretrained Chinese NLP Models[](https://awesome.re)  <div align="center"> 图片来自于论文: [A Survey of Large Language Models](https://arxiv.org/pdf/2303.18223.pdf) </div> 在自然语言处理领域中，预训练语言模型（Pretrained Language Models）已成为非常重要的基础技术，本仓库主要收集目前网上公开的一些高质量中文预训练模型、中文多模态模型、中文大语言模型等内容(感谢分享资源的大佬)，并将持续更新...... # Expand Table of Contents + [更新日志](#更新) + [基础大模型](#LLM) + [对话大模型](#ChatLLM) + [多模态对话大模型](#MultiModal-ChatLLM) + [大模型评估基准](#大模型评估基准) + [在线体验大模型](#在线体验大模型) + [开源模型库平台](#开源模型库平台) + [开源数据集库](#开源数据集库) + [开源中文指令数据集](#中文指令数据集) + [Other-Awesome](#other-awesome) + <details><summary>NLU系列</summary> - [BERT](#BERT) - [RoBERTa](#RoBERTa) - [ALBERT](#ALBERT) - [NEZHA](#NEZHA) - [XLNET](#XLNET) - [MacBERT](#MacBERT) - [WoBERT](#WoBERT) - [ELECTRA](#ELECTRA) - [ZEN](#ZEN) - [ERNIE](#ERNIE) - [ERNIE3](#ERNIE3) - [RoFormer](#RoFormer) - [StructBERT](#StructBERT) - [Lattice-BERT](#Lattice-BERT) - [Mengzi-BERT](#Mengzi-BERT) - [ChineseBERT](#ChineseBERT) - [TaCL](#TaCL) - [MC-BERT](#MC-BERT) - [二郎神](#二郎神) - [PERT](#PERT) - [MobileBERT](#MobileBERT) - [GAU-α](#GAU-α) - [DeBERTa](#DeBERTa) - [GlyphBERT](#GlyphBERT) - [CKBERT](#CKBERT) - [LERT](#LERT) - [RoCBert](#RoCBert) - [m3e](#M3E) - [LEALLA](#LEALLA) </details> + <details><summary>NLG系列</summary> - [GPT](#GPT) - [GPT-3](#GPT-3) - [NEZHA-GEN](#NEZHA-GEN) - [CPM-Generate](#CPM-Generate) - [T5](#T5) - [T5-PEGASUS](#T5-PEGASUS) - [Mengzi-T5](#Mengzi-T5) - [盘古α](#PanGu-Alpha) - [EVA](#EVA) - [BART](#BART) - [闻仲](#闻仲) - [余元](#余元) - [RWKV](#RWKV) - [Bloom](#Bloom) - [PromptCLUE](#PromptCLUE) - [ChatYuan](#ChatYuan) - [SkyText](#SkyText) - [ProphetNet](#ProphetNet) </details> + <details><summary>NLU-NLG系列</summary> - [UniLM](#UniLM) - [Simbert](#Simbert) - [RoFormer-sim](#RoFormer-sim) - [CPM-2](#CPM-2) - [CPT](#CPT) - [周文王](#周文王) - [GLM](#GLM) - [PLUG](#PLUG) - [OPD](#OPD) </details> + <details><summary>Multi-Modal</summary> - [WenLan](#WenLan) - [CogView](#CogView) - [紫东太初](#紫东太初) - [Mengzi-oscar](#Mengzi-oscar) - [R2D2](#R2D2) - [Chinese-CLIP](#Chinese-CLIP) - [TaiYi-CLIP](#TaiYi-CLIP) - [AltCLIP](#AltCLIP) - [AltDiffusion](#AltDiffusion) - [Taiyi-Stable-Diffusion](#Taiyi-Stable-Diffusion) - [wukong](#wukong) - [OFA](#OFA) - [QA-CLIP](#QA-CLIP) </details> + <details><summary>Table</summary> - [SDCUP](#SDCUP) </details> ## LLM > 大规模基础模型：表格中只罗列出参数量`大于7B`以上模型。 > > ND: Non-Causal Decoder or Prefix LM > > CD: Causal Decoder > > ED: Encoder-Decoder | 模型 | 大小 | 时间 | 语言 | 领域 | 下载 | 项目地址 | 机构/个人 | 架构 | 文献 | 备注 | | :---------------: | :---: | ------- | :--: | ---- | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :--: | :----------------------------------------------------------: | ---- | | Aquila-Base-33B | 33B | 2023-08 | 中英 | 通用 | [TODO]() | [Aquila](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/Aquila) | [FlagAI](https://github.com/FlagAI-Open) | CD | | | | Chinese-LLaMA-2 | 13B | 2023-08 | 中英 | 通用 | [ckpt](https://huggingface.co/ziqingyang/chinese-llama-2-lora-13b) | [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)[](https://camo.githubusercontent.com/7ff8758af0b2252b116aa7edcb250296666b1aa7b628a867f518dc87357cfc48/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f796d6375692f4368696e6573652d4c4c614d412d416c706163612d322e7376673f7374796c653d736f6369616c266c6162656c3d53746172) | [Yiming Cui](https://github.com/ymcui) | CD | | | | TigerBot-Base-13B | 13B | 2023-08 | 多语 | 通用 | [ckpt](https://huggingface.co/TigerResearch/tigerbot-13b-base) | [TigerBot](https://github.com/TigerResearch/TigerBot) | [虎博科技](https://github.com/TigerResearch) | CD | | | | 通义千问-base | 7B | 2023-08 | 中英 | 通用 | [ckpt](https://huggingface.co/Qwen/Qwen-7B) | [Qwen-7B](https://github.com/QwenLM/Qwen-7B) | [阿里云](https://github.com/QwenLM) | CD | [Report](https://github.com/QwenLM/Qwen-7B/blob/main/tech_memo.md) | | | Linly-Chinese-LLaMA-2 | 7/13B | 2023-07 | 中英 | 通用 | [ckpt-7B](https://huggingface.co/Linly-AI/Chinese-LLaMA-2-7B-hf) [ckpt-13B](https://huggingface.co/Linly-AI/Chinese-LLaMA-2-13B-hf) | [Linly](https://github.com/CVI-SZU/Linly)[](https://camo.githubusercontent.com/e3473350860cf86aa25f4d45ae282f1bdd4ca7f86121f5b69e81b64bf2aa780c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4356492d535a552f4c696e6c792e7376673f7374796c653d736f6369616c266c6162656c3d53746172) | [深圳大学计算机视觉研究所](https://github.com/CVI-SZU) | CD | | | | Chinese-LLaMA-2 | 7B | 2023-07 | 中英 | 通用 | [ckpt](https://huggingface.co/ziqingyang/chinese-llama-2-7b) | [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) | [Yiming Cui](https://github.com/ymcui) | CD | | | | Jiang-base | 13B | 2023-07 | 中文 | 通用 | [ckpt](https://huggingface.co/kdf/jiang-base) | / | [知未智能](https://huggingface.co/kdf) | CD | | | | bwx | 7/13B | 2023-07 | 中文 | 通用 | [ckpt-7B](https://huggingface.co/BlueWhaleX/bwx-7B-hf) [ckpt-13B](https://huggingface.co/BlueWhaleX/bwx-13B-hf) | / | [蓝鲸国数](https://huggingface.co/BlueWhaleX) | CD | | | | Llama2 | 7/13/70B | 2023-07 | 多语 | 通用 | [ckpt-7B](https://huggingface.co/llamaste/Llama-2-7b) [ckpt-13B](https://huggingface.co/llamaste/Llama-2-13b) [ckpt-70B](https://huggingface.co/llamaste/Llama-2-70b) | [llama](https://github.com/facebookresearch/llama) | [Meta](https://github.com/facebookresearch) | CD | [Paper](https://scontent-hkg4-1.xx.fbcdn.net/v/t39.2365-6/10000000_663429262362723_1696968207443577320_n.pdf?_nc_cat=101&ccb=1-7&_nc_sid=3c6