add open_llama
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|[privateGPT](https://github.com/imartinez/privateGPT)||基于 Llama 的本地私人文档助手|-|
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|[rebuff](https://github.com/woop/rebuff) ||Rebuff.ai - Prompt Injection Detector.|Prompt 攻击检测,内容检测|
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|[text-generation-webui](https://github.com/oobabooga/text-generation-webui)||-|一个用于运行大型语言模型(如LLaMA, LLaMA .cpp, GPT-J, Pythia, OPT和GALACTICA)的 web UI。|
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|[MLC LLM](https://github.com/mlc-ai/mlc-llm)||Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.|陈天奇大佬力作——MLC LLM,在各类硬件上原生部署任意大型语言模型。可将大模型应用于移动端(例如 iPhone)、消费级电脑端(例如 Mac)和 Web 浏览器。|
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|[languagemodels](https://github.com/jncraton/languagemodels)||Explore large language models on any computer with 512MB of RAM.|在512MB RAM的计算机上探索大型语言模型使用|
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@ -24,12 +24,16 @@ OpenAI 的 ChatGPT 大型语言模型(LLM)并未开源,这部分收录一
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|[FreedomGPT](https://github.com/ohmplatform/FreedomGPT) ||-|自由无限制的可以在 windows 和 mac 上本地运行的 GPT,基于 Alpaca Lora 模型。|
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|[FinGPT](https://github.com/AI4Finance-Foundation/FinGPT)||Data-Centric FinGPT. Open-source for open finance! Revolutionize 🔥 We'll soon release the trained model.|金融领域大模型|
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|[baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) ||A large-scale 7B pretraining language model developed by Baichuan |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权威 benchmark(C-EVAL/MMLU)上均取得同尺寸最好的效果。|
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|[open_llama](https://github.com/openlm-research/open_llama) ||OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset. |OpenLLaMA,允许开源复制Meta AI的LLaMA-7B 模型,在red睡衣数据集上训练得到。|
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### 大模型训练和微调
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|名称|Stars|简介| 备注 |
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|-------|-------|-------|------|
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|[transformers](https://github.com/huggingface/transformers) |  | 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. |HuggingFace 经典之作, Transformers 模型必用库|
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|[peft](https://github.com/huggingface/peft) |  | PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. |HuggingFace 出品——PEFT:最先进的参数高效微调。|
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|[OpenLLM](https://github.com/bentoml/OpenLLM) |  |An open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease. |微调,服务,部署和监控所有LLMS。用于运营大型语言模型(LLM)的开放平台。|
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|[MLC LLM](https://github.com/mlc-ai/mlc-llm)||Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.|陈天奇大佬力作——MLC LLM,在各类硬件上原生部署任意大型语言模型。可将大模型应用于移动端(例如 iPhone)、消费级电脑端(例如 Mac)和 Web 浏览器。|
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|[languagemodels](https://github.com/jncraton/languagemodels)||Explore large language models on any computer with 512MB of RAM.|在512MB RAM的计算机上探索大型语言模型使用|
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|[ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning) |  | Fine-tuning ChatGLM-6B with PEFT | 基于 PEFT 的高效 ChatGLM 微调|
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|[LLaMA-Efficient-Tuning](https://github.com/hiyouga/LLaMA-Efficient-Tuning) |  | Fine-tuning LLaMA with PEFT (PT+SFT+RLHF with QLoRA) |支持多种模型 LLaMA (7B/13B/33B/65B) ,BLOOM & BLOOMZ (560M/1.1B/1.7B/3B/7.1B/176B),baichuan (7B),支持多种微调方式LoRA,QLoRA|
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|[微调中文数据集 COIG](https://github.com/BAAI-Zlab/COIG) |  | Chinese Open Instruction Generalist (COIG) project aims to maintain a harmless, helpful, and diverse set of Chinese instruction corpora. |中文开放教学通才(COIG)项目旨在维护一套无害、有用和多样化的中文教学语料库。|
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