diff --git a/README.md b/README.md index 767a870..c772853 100644 --- a/README.md +++ b/README.md @@ -197,6 +197,7 @@ for Text-to-SQL - Falcon [[paper](https://arxiv.org/pdf/2306.01116.pdf)] [[code](https://huggingface.co/tiiuae/falcon-180B)] [[model](https://huggingface.co/tiiuae)] - 2023/06, United Arab Emirates proposes Falcon, an open source LLM trained solely on refinedweb datasets, with four parameter specifications of 1b, 7b, 40b and 180b. It is worth noting that the performance on model 40B exceeds that of 65B LLaMA. + - ChatGLM2[[paper](https://arxiv.org/pdf/2210.02414.pdf)] [[code](https://github.com/THUDM/ChatGLM2-6B/blob/main/README_EN.md)] [[model](https://huggingface.co/THUDM/chatglm2-6b)] - 2023/06, Tsinghua University proposes the second-generation version of ChatGLM，with the specification of 7b, which has stronger performance, longer context, more efficient inference and more open license. @@ -215,8 +216,8 @@ for Text-to-SQL - Code LLama [[paper](https://arxiv.org/pdf/2308.12950.pdf)] [[code](https://github.com/facebookresearch/codellama)] [[model](https://huggingface.co/codellama)] - 2023/08, Meta AI proposes Code LLama, based on Llama 2. Code Llama reaches state-of-the-art performance among open models on several code benchmarks. There are foundation models (Code Llama), Python specializations (Code Llama - Python), and instruction-following models, with 7B, 13B and 34B parameters each. -- Qwen-7B [[paper](https://github.com/QwenLM/Qwen-7B/blob/main/tech_memo.md)] [[code](https://github.com/QwenLM/Qwen-7B)] [[model](https://modelscope.cn/models/ccyh123/Qwen-7B-Chat/summary)] - - 2023/08, Alibaba Cloud proposes the 7b-parameter version of the large language model series Qwen-7B (abbr. Tongyi Qianwen), is pretrained on a large volume of data, including web texts, books, codes, etc,which has open sourced two models with Qwen-7B and Qwen-7B-Chat. +- Qwen [[paper](https://github.com/QwenLM/Qwen-7B/blob/main/tech_memo.md)] [[code](https://github.com/QwenLM/Qwen)] [[model](https://huggingface.co/Qwen)] + - 2023/08, Alibaba Cloud proposes the 7b-parameter version of the large language model series Qwen-7B (abbr. Tongyi Qianwen), is pretrained on a large volume of data, including web texts, books, codes, etc, which has open sourced two models with Qwen-7B and Qwen-7B-Chat. 2023/09, Alibaba Cloud updated the Qwen-7B and Qwen-7B-Chat and open sourced Qwen-14B and Qwen-14B-Chat. - Baichuan 2 [[code](https://github.com/baichuan-inc/Baichuan2)] [[model](https://huggingface.co/baichuan-inc)] - 2023/09, Baichuan Intelligent Technology proposes the new generation of open-source large language models Baichuan 2, trained on a high-quality corpus with 2.6 trillion tokens, which has base and chat versions for 7B and 13B, and a 4bits quantized version for the chat model. diff --git a/README.zh.md b/README.zh.md index ddf9fcd..ad76b5c 100644 --- a/README.zh.md +++ b/README.zh.md @@ -223,8 +223,8 @@ for Text-to-SQL - 2023年8月，Meta AI 在 Llama 2 的基础上提出 Code LLama。Code Llama 在多个代码基准测试中达到了开放模型中最先进的性能。有基础模型 (Code Llama)、Python 专业化 (Code Llama - Python) 和指令跟踪模型（instruction-following models），每个模型都有 7B、13B 和 34B 参数。 -- Qwen-7B [[paper](https://github.com/QwenLM/Qwen-7B/blob/main/tech_memo.md)] [[code](https://github.com/QwenLM/Qwen-7B)] [[model](https://modelscope.cn/models/ccyh123/Qwen-7B-Chat/summary)] - - 2023年8月，阿里云提出大语言模型系列Qwen-7B（简称通义千问），在海量数据上进行预训练，包括网页文本、书籍、代码等，开源了两个版本Qwen-7B和Qwen-7B-Chat。 +- Qwen [[paper](https://github.com/QwenLM/Qwen-7B/blob/main/tech_memo.md)] [[code](https://github.com/QwenLM/Qwen)] [[model](https://huggingface.co/Qwen)] + - 2023年8月，阿里云提出大语言模型系列Qwen-7B（简称通义千问），在海量数据上进行预训练，包括网页文本、书籍、代码等，开源了两个版本Qwen-7B和Qwen-7B-Chat。 2023年9月，阿里云更新了Qwen-7B和Qwen-7B-Chat，并开源了Qwen-14B和Qwen-14B-Chat。 - Baichuan 2 [[code](https://github.com/baichuan-inc/Baichuan2)] [[model](https://huggingface.co/baichuan-inc)] - 2023年9月，百川智能提出新一代开源大语言模型Baichuan 2，在2.6万亿个tokens的高质量语料上训练，有7B和13B的基础版和聊天版，以及4bits量化版聊天模型。 @@ -306,3 +306,5 @@ for Text-to-SQL [![GitHub Repo stars](https://img.shields.io/github/stars/luban-agi/Awesome-AIGC-Tutorials?style=social)](https://github.com/luban-agi/Awesome-AIGC-Tutorials/stargazers) ![last commit](https://img.shields.io/github/last-commit/luban-agi/Awesome-AIGC-Tutorials?color=green) - Awesome AIGC Tutorials 包含一系列精选的教程和资源，涵盖大型语言模型、AI 绘画和相关领域。探索适合初学者和高级人工智能爱好者的深入见解和知识。 + +[![Star History Chart](https://api.star-history.com/svg?repos=eosphoros-ai/Awesome-Text2SQL&type=Date)](https://star-history.com/#eosphoros-ai/Awesome-Text2SQL)