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‎LICENSE‎

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‎MODEL_LICENSE.pdf‎

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‎README.md‎

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<div align="center">
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<h1>
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XVERSE-13B
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</h1>
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</div>
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<p align="center">
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<a href="https://huggingface.co/xverse/XVERSE-13B">🤗 HuggingFace</a>&nbsp ｜ &nbsp<a href="resources/wechat.png">💬 微信社区</a>
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</p>
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<h4 align="left">
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<p>
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<b>中文</b> |
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<a href="README_EN.md">English</a>
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<p>
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</h4>
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## 模型介绍
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**XVERSE-13B** 是由深圳元象科技自主研发的支持多语言的大语言模型（Large Language Model），主要特点如下：
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- **模型结构**：XVERSE-13B 使用主流 Decoder-only 的标准 Transformer 网络结构，支持 8K 的上下文长度（Context Length），为同尺寸模型中最长，能满足更长的多轮对话、知识问答与摘要等需求，模型应用场景更广泛。
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- **训练数据**：构建了 1.4 万亿 token 的高质量、多样化的数据对模型进行充分训练，包含中、英、俄、西等 40 多种语言，通过精细化设置不同类型数据的采样比例，使得中英两种语言表现优异，也能兼顾其他语言效果。
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- **分词**：基于 BPE（Byte-Pair Encoding）算法，使用上百 GB 语料训练了一个词表大小为 100,278 的分词器，能够同时支持多语言，而无需额外扩展词表。
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- **训练框架**：经过对算子、通信、并行策略及调度等的优化，千卡集群上的峰值算力利用率达到 58.5%，达到业界前列。
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## 评测结果
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为验证模型的各项能力，我们选取了多个学科综合能力评测集，包括 [MMLU](https://arxiv.org/abs/2009.03300)（英文）、 [C-Eval](https://cevalbenchmark.com/)（中文）、[AGIEval](https://arxiv.org/abs/2304.06364)（中英） 、[GAOKAO-Bench](https://github.com/OpenLMLab/GAOKAO-Bench)（中英）、[GAOKAO-English](https://github.com/ExpressAI/AI-Gaokao)（英文），评测结果如下：
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| 模型\数据集 | MMLU | C-Eval | AGIEval<sup>1</sup> | GAOKAO-Bench<sup>1</sup> | GAOKAO-English<sup>1</sup> |
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| :------------------------: | :--------------: | :--------------: | :-----------------: | :----------------------: | :------------------------: |
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| Baichuan-13B | 51.6<sup>2</sup> | 53.6<sup>3</sup> | 40.5 | 45.9 | 56.9 |
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| Llama-1-13B | 46.9<sup>4</sup> | 28.8 | 27.3 | 26.4 | 38.1 |
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| Llama-2-13B | 54.8<sup>4</sup> | 35.6 | 33.4 | 35.4 | 60.6 |
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| moss-moon-003-base (16B) | 24.7 | 33.1<sup>3</sup> | 26.8 | 28.5 | 34.7 |
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| OpenLLaMA-13B | 42.4 | 24.7 | 24.0 | 25.6 | 33.3 |
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| OPT-13B | 25.2 | 25.0 | 24.2 | 24.4 | 31.1 |
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| Pythia-12B | 25.1 | 26.2 | 25.3 | 25.3 | 26.8 |
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| Ziya-LLaMA-13B-Pretrain-v1 | 43.9 | 30.2 | 27.2 | 26.4 | 37.6 |
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| **XVERSE-13B** | **55.1** | **54.7** | **41.4** | **53.9** | **66.5** |
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> <sup>1：只针对其中的单项选择题进行测试，即排除了填空题、开放性问题和多项选择题</sup>
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> <sup>2：来源于 [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) 的汇报结果</sup>
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> <sup>3：来源于 [C-Eval](https://cevalbenchmark.com/) 的汇报结果</sup>
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> <sup>4：来源于[Llama 2 论文](https://arxiv.org/abs/2307.09288)的汇报结果</sup>
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>
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> 对于 MMLU ，我们采用作者提供的[评测工具](https://github.com/hendrycks/test)，C-Eval、AGIEval、GAOKAO-Bench、GAOKAO-English 与 MMLU 的评测方式相同，且统一采用 **5-shot** 构造测试样本。
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### MMLU 各类别指标
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| 模型\类别 | Average | STEM | Social Science | Humanities | Others |
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| :------------------------: | :------: | :------: | :------------: | :--------: | :------: |
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| Baichuan-13B | 51.6 | 41.6 | 60.9 | 47.4 | 58.5 |
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| Llama-1-13B | 46.9 | 35.8 | 53.8 | 45.0 | 53.3 |
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| Llama-2-13B | 54.8 | 44.1 | 62.6 | 52.8 | 61.1 |
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| moss-moon-003-base (16B) | 24.7 | 23.0 | 24.0 | 25.2 | 26.3 |
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| OpenLLaMA-13B | 42.4 | 34.7 | 48.6 | 40.0 | 47.1 |
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| OPT-13B | 25.2 | 23.9 | 24.1 | 25.9 | 26.3 |
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| Pythia-12B | 25.1 | 24.8 | 23.0 | 26.1 | 26.0 |
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| Ziya-LLaMA-13B-Pretrain-v1 | 43.9 | 36.3 | 48.8 | 41.1 | 50.3 |
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| **XVERSE-13B** | **55.1** | **44.5** | **64.4** | **50.5** | **62.9** |
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### C-Eval 各类别指标
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| 模型\类别 | Average | STEM | Social Science | Humanities | Others |
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| :------------------------: | :------: | :------: | :------------: | :--------: | :------: |
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| Baichuan-13B | 53.6 | 47.0 | 66.8 | 57.3 | 49.8 |
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| Llama-1-13B | 28.8 | 27.5 | 33.9 | 27.7 | 27.7 |
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| Llama-2-13B | 35.6 | 34.5 | 39.8 | 36.2 | 33.2 |
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| moss-moon-003-base (16B) | 33.1 | 31.6 | 37.0 | 33.4 | 32.1 |
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| OpenLLaMA-13B | 24.7 | 25.5 | 23.5 | 24.2 | 24.7 |
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| OPT-13B | 25.0 | 24.4 | 24.6 | 25.9 | 25.4 |
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| Pythia-12B | 26.2 | 26.8 | 25.1 | 26.7 | 25.4 |
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| Ziya-LLaMA-13B-Pretrain-v1 | 30.2 | 27.8 | 34.3 | 32.0 | 29.0 |
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| **XVERSE-13B** | **54.7** | **45.6** | **66.2** | **58.3** | **56.9** |
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## 使用方法
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### 环境安装
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1. 下载本仓库：
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```shell
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git clone https://github.com/xverse-ai/XVERSE-13B
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cd XVERSE-13B
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```
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2. 使用 pip 安装依赖：
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```shell
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pip install -r requirements.txt
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```
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### Transformers 加载方式
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可通过以下代码加载 XVERSE-13B 模型进行推理：
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```python
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>>> from transformers import AutoTokenizer, AutoModelForCausalLM
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>>> tokenizer = AutoTokenizer.from_pretrained("xverse/XVERSE-13B")
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>>> model = AutoModelForCausalLM.from_pretrained("xverse/XVERSE-13B", trust_remote_code=True).half().cuda()
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>>> model = model.eval()
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>>> inputs = tokenizer('北京的景点：故宫、天坛、万里长城等。\n深圳的景点：', return_tensors='pt').input_ids
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>>> inputs = inputs.cuda()
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>>> generated_ids = model.generate(inputs, max_new_tokens=64, eos_token_id=tokenizer.eos_token_id)
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>>> print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True))
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```
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### 网页 Demo
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可通过以下代码启动一个web server，在浏览器输入访问地址后，可使用 XVERSE-13B 模型进行推理：
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```shell
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python text_generation_demo.py --port='port' --model_path='/path/to/model/' --tokenizer_path='/path/to/tokenizer/'
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```
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## 局限性与免责申明
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XVERSE-13B 与其他所有 LLM 一样，在某些情况下可能会产生不准确、有偏见或其他令人反感的内容。因此，请谨慎使用模型生成的内容，请勿将生成的有害内容进行传播，在部署任何 XVERSE-13B 的应用之前，开发人员应根据其具体应用对模型进行安全测试和调优。
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我们强烈警告不要将 XVERSE-13B 模型用于制造或传播有害信息，或进行任何可能损害公众、国家、社会安全或违反法规的活动。如果使用 XVERSE-13B 模型产生任何问题，无论是数据安全问题、公共舆论风险，还是模型被误解、滥用、传播或不合规使用所引发的任何风险和问题，我们将不承担任何责任。
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## 模型开源协议
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使用本仓库的源码需要遵循 [Apache-2.0](LICENSE) 开源协议，使用 XVERSE-13B 的模型权重则需要遵循[模型许可协议](MODEL_LICENSE.pdf)。
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XVERSE-13B 模型权重对学术研究**完全开放**，并且支持**免费商用**，商用需申请商业使用授权，可以发送邮件到 <opensource@xverse.cn> 进行申请。
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