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Model: itpossible/Chinese-Mistral-7B-Instruct-v0.1 Source: Original Platform
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<div align="center">
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<h1>
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Chinese-Mistral
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</h1>
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</div>
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## 🎉 新闻
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- [2024-04-04] 发布Chinese-Mistral指令精调模型。
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- [2024-03-31] 发布Chinese-Mistral基座模型。
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## 🚀 介绍
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随着Mistral AI公司开源其七十亿参数模型[Mistral-7B](https://huggingface.co/meta-llama/Llama-2-7b-hf)，该模型超越[Llama](https://huggingface.co/meta-llama)，成为当前最强大的开源模型之一。Mistral-7B在各类基准测试中，不仅超过了Llama2-13B，而且在推理、数学、代码生成任务中超过Llama2-34B。
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然而，Mistral-7B的训练语料主要为英文文本，其中文能力较为欠缺。其次，Mistral-7B的词表不支持中文，导致其对中文的编码和解码效率较低，限制了在中文场景的应用。<br>
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为了克服这一局限，清华大学地球系统科学系地球和空间信息科学实验室基于Mistral-7B进行了中文词表扩充和增量预训练，增强了Mistral-7B在中文任务上的表现，并提高了其对中文文本的编解码效率。<br>
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项目地址：https://github.com/THU-ESIS/Chinese-Mistral
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## 📥 模型下载
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本项目开源了Chinese-Mistral-7B与Chinese-Mistral-7B-instruct：
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| 模型 | 下载地址 | 说明 |
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|:-----------------------------:|:------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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| Chinese-Mistral-7B | [HuggingFace](https://huggingface.co/itpossible/Chinese-Mistral-7B-v0.1)<br>[wisemodel](https://wisemodel.cn/models/itpossible/Chinese-Mistral-7B-v0.1)<br>[ModelScope](https://www.modelscope.cn/models/itpossible/Chinese-Mistral-7B-v0.1) | 完整基座模型 |
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| Chinese-Mistral-7B-Instruct | [HuggingFace](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.1)<br>[wisemodel](https://wisemodel.cn/models/itpossible/Chinese-Mistral-7B-Instruct-v0.1)<br>[ModelScope](https://www.modelscope.cn/models/itpossible/Chinese-Mistral-7B-Instruct-v0.1) | 完整指令精调模型<br>中英文alpaca_gpt4进行lora微调|
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## 📈 模型性能
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### 模型综合能力
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我们采用C-Eval、CMMLU和MMLU三个评测数据集全面评估Chinese-Mistral-7B：
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- C-Eval：它是一个全面的中文基础模型评估套件。包含13948个多项选择题，涵盖52个学科和四个难度级别。它旨在评估模型在人文、社科、理工等多个学科大类上的知识和推理能力。
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- CMMLU：它是一个综合性的中文评估基准。涵盖了从基础学科到高级专业水平的67个主题。它专门用于评估语言模型在中文语境下的知识和推理能力。
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- MMLU：它是一个包含了57个子任务的英文评测数据集。涵盖了从初等数学、美国历史、计算机科学到法律等多个领域，难度覆盖高中水平到专家水平，有效地衡量了模型在人文、社科和理工等多个学科大类中的综合知识能力。
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下表展示了开源社区较流行的中文Llama2、中文Mistral与我们发布的Chinese-Mistral-7B的评测结果。评测方式采用5-shot，采用opencompass在相同的实验条件下进行评测。
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| 模型名称 | C-Eval | CMMLU | MMLU | 平均得分 |
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|:-----------------------------------------------------------------------------------------------:|:-------------:|:-------------:|:------------:|:-----------------:|
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| [Linly-Al/Chinese-LLaMA-2-7B-hf](https://huggingface.co/Linly-Al/Chinese-LLaMA-2-7B-hf) | 31.2 | 30.14 | 35.09 | 32.14 |
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| [hfl/chinese-llama-2-7b](https://huggingface.co/hfl/chinese-llama-2-7b) | 27.4 | 33.38 | 37.25 | 32.68 |
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| [Linly-Al/Chinese-LLaMA-2-13B-hf](https://huggingface.co/Linly-Al/Chinese-LLaMA-2-13B-hf) | 39.9 | 42.48 | 52.54 | 44.97 |
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| [hfl/chinese-llama-2-13b](https://huggingface.co/hfl/chinese-llama-2-13b) | 41.0 | 43.25 | 52.94 | 45.73 |
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| [gywy/Mistral-7B-v0.1-chinese](https://huggingface.co/gywy/Mistral-7B-v0.1-chinese) | 37.4 | 36.45 | 37.38 | 37.08 |
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|[OpenBuddy/openbuddy-mistral-7b-v13-base](https://huggingface.co/OpenBuddy/openbuddy-mistral-7b-v13-base)| 44.4 | 46.32 | 57.79 | 49.50 |
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| **[Chinese-Mistral-7B (本模型)](https://huggingface.co/itpossible/Chinese-Mistral-7B-v0.1)** | **47.5** | **47.52** | **58.29** | **51.10** |
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由上表可知，Chinese-Mistral-7B的中文和英文通识能力不仅超过同等参数量的中文Llama2模型，而且在多项评测中优于130亿参数量的中文Llama2。同时，Chinese-Mistral-7B的评测表现高于开源社区其他同等参数量的中文Mistral。
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### 中文编解码效率
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我们从WuDaoCorpus2中采样训练数据，使用sentencepiece训练中文BPE词表，并人工选取部分其他优秀中文词表进行词表融合。经过严格的人工审核，最终形成的词表大小为63776。为了提高模型计算效率，我们在词表末尾添加<|sym1|>、……、<|sym96|>，使得词表大小为128的倍数，最终得到的词表大小为63872。
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我们随机选取了WuDaoCorpus2_part-2021278643作为测试数据以评测分词效果。经统计，测试数据包括67013857个单词，我们用单词数量除以分词后的Token数量，计算压缩率。压缩率越大，表明分词效果越好，在中文场景的编解码效率越高。
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| 模型名称 | 模型类型 | 词表大小 | Token数量 | 压缩率 |
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|:-----------------------------------------------------------------------------------------------:|:-------------:|:-------------:|:------------:|:-----------------:|
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| [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) | Llama | 32000 | 97406876 | 0.6880 |
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| [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | Mistral | 32000 | 76269008 | 0.8787 |
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| [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b) | GLM | 64789 | 43487673 | 1.5410 |
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| [Linly-Al/Chinese-LLaMA-2-13B-hf](https://huggingface.co/Linly-Al/Chinese-LLaMA-2-13B-hf) | Llama | 40076 | 65402900 | 1.0246 |
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| [hfl/chinese-llama-2-13b](https://huggingface.co/hfl/chinese-llama-2-13b) | Llama | 55296 | 45763513 | 1.4644 |
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| [OpenBuddy/openbuddy-mistral-7b-v13-base](https://huggingface.co/OpenBuddy/openbuddy-mistral-7b-v13-base) | Mistral | 36608 | 65329642 | 1.0256 |
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|[gywy/Mistral-7B-v0.1-chinese](https://huggingface.co/gywy/Mistral-7B-v0.1-chinese)| Mistral | 48593 | 46670146 | 1.4359 |
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| **[Chinese-Mistral-7B (本模型)](https://huggingface.co/itpossible/Chinese-Mistral-7B-v0.1)** | Mistral | 63872 | **43044156** | **1.5569** |
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由上表可知，Chinese-Mistral-7B在可观的词表大小条件下，取得了最高的压缩率，表明其能够高效处理中文文本。
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## 💻 模型推理
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如下是使用Chinese-Mistral-7B进行推理的代码示例。
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
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model_path = "itpossible/Chinese-Mistral-7B-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map=device)
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text = "我是一个人工智能助手，我能够帮助你做如下这些事情："
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inputs = tokenizer(text, return_tensors="pt").to(device)
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outputs = model.generate(**inputs, max_new_tokens=120, do_sample=True)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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如下是使用Chinese-Mistral-7B-Instruct进行推理的代码示例。
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
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model_path = "itpossible/Chinese-Mistral-7B-Instruct-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map=device)
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text = "请为我推荐中国三座比较著名的山"
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messages = [{"role": "user", "content": text}]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
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outputs = model.generate(inputs, max_new_tokens=300, do_sample=True)
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outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
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print(outputs)
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```
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Chinese-Mistral-7B 与Chinese-Mistral-7B-Instruct生成效果示例如下：
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| 模型名称 | Chinese-Mistral-7B | Chinese-Mistral-7B-Instruct |
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|--- |--- |--- |
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| 用户输入 | <div style="width: 270pt">我是一个人工智能助手，我能够帮助你做如下这些事情： | <div style="width: 270pt">请为我推荐中国三座比较著名的山 |
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| 模型输出 | <div style="width: 270pt">1、搜索资料，我可以帮你搜索任何你需要的资料。比如搜索某本书的内容，搜索某某地的地图，搜索某部电影的播放地址等。<br>2、计算，我可以帮你计算各种数学题，比如加法，减法，乘积，除法等等。计算各种不同科目的公式，比如化学方程式的计算，物理公式的计算等。<br>3、做笔记，你可以把你的想法记录在我的字典里面，或者记录日程安排。<br>4、翻译，你可以把的设置为你的即时翻译器，将你要翻译的内容放到信息框里面，然后点击发送就可以。 | <div style="width: 270pt">中国的山川风景十分美丽，以下三座山是国内比较著名的景点：<br>1.泰山：泰山，位于山东省泰安市北部，历史悠久，是我国五大名山之一，海拔约1545米，其雄伟的地势和壮丽的风光吸引了不少游客前来游览。<br>2.黄山：黄山位于安徽省东南部，因独特的山水风光和丰富的文化和历史积淀而闻名于世，这里悬崖峭壁，奇峰怪石，云海雾海，景色奇特秀丽，被誉为“天下第一奇山”。<br>3.峨眉山：峨眉山位于四川省峨眉山市东北部，是中国四大佛教名山之一，因雄伟壮观的山峰和丰富多彩的森林资源而闻名于世。这里气候湿润，植被覆盖率极高，景色秀丽，被赞誉为“峨眉天下秀”。 |
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## 📝 训练数据
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训练数据采样于WanJuan、baike2018qa、Dolma、gutenberg-books等高质量开源数据集。我们对这些数据集进行细粒度清洗，并充分考虑训练数据集中不同类别数据的占比。
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## ⚠️ 局限性
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Chinese-Mistral-7B的开发旨在为开源社区提供一个性能优越的中文大语言模型。请注意，由于模型大小及训练数据规模限制，本模型仍可能生成误导性内容或者有害内容。因此，在部署任何由Chinese-Mistral系列模型驱动的应用程序之前，开发人员必须进行安全测试，对模型进行相应调整，以满足安全性需求。
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## ✒️ 引用
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如果您觉得本项目对您的研究有所帮助或使用了本项目的模型，请引用本项目：
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```bibtex
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@misc{Chinese-Mistral,
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author = {Zhou, Chen and Yuqi, Bai},
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title = {Chinese-Mistral: An Efficient and Effective Chinese Large Language Model},
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year = {2024},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/THU-ESIS/Chinese-Mistral}}
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}
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```
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## 结语
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我们欢迎社区的支持和合作，共同推动通用大语言模型和领域大语言模型的发展。联系方式：<br>
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白玉琪，清华大学地球系统科学系长聘教授，实验室负责人，yuqibai@tsinghua.edu.cn<br>
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陈舟，清华大学地球系统科学系博士生，大语言模型组组长，chenz22@mails.tsinghua.edu.cn
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{
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"_name_or_path": "/home/chenzhou/Project/LLaMA-Factory/saves/pt/full/Mistral-7B-v0.1-expand_voc_embedding_init-expand_voc-0111",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.36.2",
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"use_cache": true,
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"vocab_size": 63872
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}
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{"framework":"Pytorch","task":"text-generation"}
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.36.2"
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}
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size 3895582632
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version https://git-lfs.github.com/spec/v1
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}
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||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
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}
|
||||
}
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:561d3879ed270f6b6f8e1ff5411eac1c315935e5f0b2318ca92ed6d1531c07f7
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size 991499
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43
tokenizer_config.json
Normal file
43
tokenizer_config.json
Normal file
@@ -0,0 +1,43 @@
|
||||
{
|
||||
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||||
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"sp_model_kwargs": {},
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||||
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"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
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||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user