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Browse files- .gitattributes +1 -0
- README.md +274 -0
- config.json +30 -0
- generation_config.json +6 -0
- model.safetensors.index.json +1002 -0
- output-00001-of-00003.safetensors +3 -0
- output-00002-of-00003.safetensors +3 -0
- output-00003-of-00003.safetensors +3 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +42 -0
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| 1 |
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---
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license: apache-2.0
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---
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<div align="center">
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<h1>
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Chinese-Mixtral-8x7B
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</h1>
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</div>
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<div align="center">
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<a href="https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B/pulls">
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<image src="https://img.shields.io/badge/PRs-welcome-brightgreen"></image>
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<image src="https://img.shields.io/badge/License-Apache_2.0-green.svg"></image>
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</a>
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</div>
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## 🚀 介绍
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本项目基于Mistral发布的模型[Mixtral-8x7B](https://mistral.ai/news/mixtral-of-experts/)进行了中文扩词表增量预训练，希望进一步促进中文自然语言处理社区对MoE模型的研究。我们扩充后的词表显著提高了模型对中文的编解码效率，并通过大规模开源语料对扩词表模型进行增量预训练，使模型具备了强大的中文生成和理解能力。
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项目开源内容：
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- 中文Mixtral-8x7B扩词表大模型
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- 扩词表增量预训练代码
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> 请注意，Chinese-Mixtral-8x7B仍然可能生成包含事实性错误的误导性回复或包含偏见/歧视的有害内容，请谨慎鉴别和使用生成的内容，请勿将生成的有害内容传播至互联网。
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## 📥 模型下载
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本项目使用QLoRA进行训练，LoRA权重与合并权重后的模型分别开源，您可以根据自己的需求选择下载：
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| 模型名称 | 模型大小 | 下载地址 | 备注 |
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|:----------------------------:|:-----:|:-----------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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| Chinese-Mixtral-8x7B | 88GB | [🤗HuggingFace](https://huggingface.co/HIT-SCIR/Chinese-Mixtral-8x7B) | 中文扩词表完整模型，可以直接使用 |
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| Chinese-Mixtral-8x7B-adapter | 2.7GB | [🤗HuggingFace](https://huggingface.co/HIT-SCIR/Chinese-Mixtral-8x7B-adapter) | LoRA权重，需要与原版Mixtral-8x7B进行合并才可以使用，合并脚本请参考[这里](https://gist.github.com/ChrisHayduk/1a53463331f52dca205e55982baf9930) |
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## 💻 模型推理
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Chinese-Mixtral-8x7B支持完整的Mixtral-8x7B模型生态，包括使用`vLLM`、`Flash Attention 2`进行加速，使用`bitsandbytes`进行模型量化等。以下是使用Chinese-Mixtral-8x7B进行推理的代码示例。
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使用Flash Attention 2：
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "HIT-SCIR/Chinese-Mixtral-8x7B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto")
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text = "我的名字是"
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inputs = tokenizer(text, return_tensors="pt").to(0)
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outputs = model.generate(**inputs, max_new_tokens=20)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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使用4bit量化：
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "HIT-SCIR/Chinese-Mixtral-8x7B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto")
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text = "我的名字是"
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inputs = tokenizer(text, return_tensors="pt").to(0)
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outputs = model.generate(**inputs, max_new_tokens=20)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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请注意，Chinese-Mixtral-8x7B为基座模型，没有经过指令微调，因此指令遵循能力有限。您可以参考[微调](#微调)一节对模型进行微调。
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## 📈 模型性能
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### 模型综合能力
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我们分别使用以下评测数据集对Chinese-Mixtral-8x7B进行评测：
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- C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。
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- CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。
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- MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。
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- HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。
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根据Mistral发布的[技术报告](https://arxiv.org/pdf/2401.04088.pdf)，Mixtral-8x7B在推理时将激活13B参数。下表为Chinese-Mixtral-8x7B与其他13B规模的中文扩词表模型在各个评测数据集上的5-shot结果：
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| 模型名称 | 增量训练语料 | C-Eval<br>(中文) | CMMLU<br>(中文) | MMLU<br>(英文) | HellaSwag<br>(英文) |
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|:-----------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:-------------:|:------------:|:-----------------:|
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| [IDEA-CCNL/Ziya2-13B-Base](https://huggingface.co/IDEA-CCNL/Ziya2-13B-Base) | 650B Token | 59.29 | 60.93 | 59.86 | 58.90 |
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| [TigerResearch/tigerbot-13b-base-v3](https://huggingface.co/TigerResearch/tigerbot-13b-base-v3) | 500B Token | 50.52 | 51.65 | 53.46 | 59.16 |
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| [Linly-AI/Chinese-LLaMA-2-13B-hf](https://huggingface.co/Linly-AI/Chinese-LLaMA-2-13B-hf) | 11B Token | 42.57 | 41.95 | 51.32 | 59.05 |
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| [hfl/chinese-llama-2-13b](https://huggingface.co/hfl/chinese-llama-2-13b) | 约30B Token(120GB) | 41.90 | 42.08 | 51.92 | 59.28 |
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| **Chinese-Mixtral-8x7B(本项目)** | 42B Token | 52.08 | 51.08 | 69.80 | 65.69 |
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在中文知识和理解方面，我们的Chinese-Mixtral-8x7B与TigerBot-13B-Base-v3性能相当。由于Chinese-Mixtral-8x7B的训练数据量仅为TigerBot-13B-Base-v3的8%，我们的模型仍有进一步提升的空间。与此同时，得益于原版Mixtral-8x7B模型强大的性能，我们的Chinese-Mixtral-8x7B达到了各个扩词表模型的最强英文水平。
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> 由于不同版本的评测脚本实现细节有细微差异，为了保证评测结果的一致性和公平性，我们的评测脚本统一使用EleutherAI发布的lm-evaluation-harness，commit hash为[28ec7fa](https://github.com/EleutherAI/lm-evaluation-harness/tree/28ec7fa950346b5a895e85e1f3edd5648168acc4)。
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### 模型生成效果
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下表为各个扩词表模型的生成效果。由于部分模型的预训练语料未使用`eos_token`进行分隔，我们采用了`max_tokens = 100`对生成文本进行截断。我们的采样参数为`temperature = 0.8, top_p = 0.9`。
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### 中文编解码效率
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针对中文编解码效率，我们使用各个扩词表模型的分词器对[SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B)数据集的一个切片（2023-06_zh_head_0000.jsonl）进行编码，对比了各个分词器输出的中文文本Token量：
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| 模型名称 | 模型类别 | 词表大小 | 中文文本Token量 | 编解码效率 |
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|:----------------------------------:|:-------:|:-----:|:----------:|:-------:|
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| meta-llama/Llama-2-13B-hf | LLaMA | 32000 | 780M | 低 |
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| mistralai/Mixtral-8x7B-v0.1 | Mixtral | 32000 | 606M | 低 |
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| Linly-AI/Chinese-LLaMA-2-13B-hf | LLaMA | 40076 | 532M | 中 |
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| IDEA-CCNL/Ziya2-13B-Base | LLaMA | 39424 | 532M | 中 |
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| hfl/chinese-llama-2-13b | LLaMA | 55296 | 365M | 高 |、
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| TigerResearch/tigerbot-13b-base-v3 | LLaMA | 65112 | 342M | 高 |
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| **Chinese-Mixtral-8x7B(本项目)** | Mixtral | 57000 | 355M | 高 |
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在约1.4GB的测试文本中，我们的Chinese-Mixtral-8x7B中文编解码效率仅次于TigerBot-13B-Base-v3，较原模型提高了41.5%。这有利于加速中文文本的推理速度，并在In-Context Learning、Chain-of-Thought等场景中节省序列长度，有利于提高复杂推理任务的性能。
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## ⚙️ 训练细节
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<details>
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<summary>
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### 词表扩充
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</summary>
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我们使用`sentencepiece`在12G知乎数据和2G悟道数据上训练中文BPE词表。我们在训练词表时分别枚举了中文单字Token数量以及中文总Token数量，并对二者进行组合，得到了数百个大小、内容各异的词表。为了得到最适合的词表，我们通过Zheng Bo等人提出的[ALP](https://arxiv.org/pdf/2109.07306.pdf)计算这些词表的中文词汇能力。ALP通过计算特定语言的子词切分粒度，并对词表的中低频子词进行惩罚，是一种方便快捷的衡量特定语言词汇能力的指标。
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| 137 |
+
我们在书籍和百科语料上评估了不同词表的ALP值。图示中，四条曲线分别代表四种中文单字Token数量的词表（4451、5435、6414和7434）。为了避免词表过小导致中文压缩率过低，以及词表过大导致embedding层过于稀疏，我们选取ALP曲线的拐点，对应向词表中新增25000个中文Token。在此基础上，我们选择了四条曲线中ALP最大者，即新增6414个中文单字Token的词表，作为最终Chinese-Mixtral-8x7B选用的词表。
|
| 138 |
+
|
| 139 |
+
|
| 140 |
+
|
| 141 |
+
在获得新词表后，我们需要对embedding和lm_head层进行扩充和初始化。我们使用新Token在旧embedding层中的词嵌入平均值对扩充部分进行初始化。在我们的前期实验中，这种方法略优于HuggingFace的默认实现，即使用固定的正态分布进行初始化。
|
| 142 |
+
|
| 143 |
+
</details>
|
| 144 |
+
|
| 145 |
+
<details>
|
| 146 |
+
<summary>
|
| 147 |
+
|
| 148 |
+
### 增量预训练
|
| 149 |
+
|
| 150 |
+
</summary>
|
| 151 |
+
|
| 152 |
+
Mixtral-8x7B模型参数量为46.7B，全参数训练需要同时使用多种并行策略，在训练资源受限的情况下时间成本过高。因此我们采用HuggingFace官方推荐的方法，使用QLoRA对模型进行训练。QLoRA在LoRA低秩分解的基础上，通过引入4位量化、双重量化和利用NVIDIA统一内存进行分页，进一步减少了训练所需显存，同时保持了与全参数训练相当的性能。
|
| 153 |
+
|
| 154 |
+
我们参考Yiming Cui等人[对LoRA的设置](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/blob/main/scripts/training/run_pt.sh)，对原模型所有Linear层应用低秩分解，并将扩增后的embedding和lm_head层的参数设置为可训练。对于模型主体，我们采用NF4格式进行量化，这种格式可以使得量化后的数据与量化前具有同等的数据分布，模型的权重信息损失更少。
|
| 155 |
+
|
| 156 |
+
#### 环境准备
|
| 157 |
+
|
| 158 |
+
我们建议使用Python 3.10 + torch 2.0.1
|
| 159 |
+
|
| 160 |
+
```shell
|
| 161 |
+
# Pytorch + Transformers
|
| 162 |
+
$ pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
|
| 163 |
+
$ pip install transformers==4.36.2 datasets evaluate peft accelerate gradio optimum sentencepiece
|
| 164 |
+
$ pip install jupyterlab scikit-learn pandas matplotlib tensorboard nltk rouge bitsandbytes fire
|
| 165 |
+
# DeepSpeed
|
| 166 |
+
$ git clone https://github.com/microsoft/DeepSpeed.git
|
| 167 |
+
$ cd DeepSpeed
|
| 168 |
+
$ DS_BUILD_FUSED_ADAM=1 pip3 install .
|
| 169 |
+
# Flash Attention
|
| 170 |
+
$ pip install flash-attn --no-build-isolation
|
| 171 |
+
```
|
| 172 |
+
|
| 173 |
+
#### 数据集下载
|
| 174 |
+
|
| 175 |
+
我们基于现有的开源数据集训练了Chinese-Mixtral-8x7B，数据集包括：
|
| 176 |
+
|
| 177 |
+
| 数据集名称 | 数据集语言 |使用数据量| 备注 |
|
| 178 |
+
|:----------------------------------------------------------------------------:|:-----:|:----------------:|:-----:|
|
| 179 |
+
| [Skywork/SkyPile-150B](https://huggingface.co/datasets/Skywork/SkyPile-150B) | 中文 |30B| 仅使用2022 + 2023年的数据 |
|
| 180 |
+
| [DKYoon/SlimPajama-6B](https://huggingface.co/datasets/DKYoon/SlimPajama-6B) | 英文 |12B| 数据集重复2 Epoch |
|
| 181 |
+
|
| 182 |
+
通过`data/download.py`将数据集下载到`data`中。针对Slimpajama数据集，需要使用`data/parquet2jsonl.py`将原始数据集转换为`jsonl`格式。
|
| 183 |
+
|
| 184 |
+
下载后的数据集为多个jsonl文件的分片，使用`cat`将多个分片合并为一个jsonl文件。
|
| 185 |
+
|
| 186 |
+
```shell
|
| 187 |
+
$ cat *.jsonl > all.jsonl
|
| 188 |
+
```
|
| 189 |
+
|
| 190 |
+
通过`split`将jsonl切分为train和valid集合。本项目中train和valid的行数比例为999:1。
|
| 191 |
+
|
| 192 |
+
```shell
|
| 193 |
+
$ wc -l all.jsonl # 计算数据集总行数
|
| 194 |
+
$ split -l <lines> all.jsonl # 按999:1计算train/valid行数，进行切分
|
| 195 |
+
$ mv xaa DKYoon-SlimPajama-6B-train.jsonl # 重命名
|
| 196 |
+
$ mv xab DKYoon-SlimPajama-6B-dev.jsonl
|
| 197 |
+
```
|
| 198 |
+
|
| 199 |
+
#### 数据集预处理
|
| 200 |
+
|
| 201 |
+
将数据集名称和路径注册到`data/datasets.toml`中：
|
| 202 |
+
|
| 203 |
+
```toml
|
| 204 |
+
[DKYoon-SlimPajama-6B] # 数据集名称
|
| 205 |
+
splits = ["train", "dev"] # 数据集train/valid集合
|
| 206 |
+
root = "{DATA_DIR}/en/{name}" # 数据集根目录
|
| 207 |
+
doc = "{name}-{split}" # 数据集文件名
|
| 208 |
+
encoded = "encoded-{name}-{split}" # 预处理保存位置
|
| 209 |
+
```
|
| 210 |
+
|
| 211 |
+
使用`data/preprocess_datasets.py`对数据集进行子词切分，从而加快训练速度。
|
| 212 |
+
|
| 213 |
+
```shell
|
| 214 |
+
$ python data/preprocess_datasets.py --ds_name SkyPile-150B-2023 --tokenizer_name_or_path tokenizer/Mixtral-8x7B-v0.1-vocab
|
| 215 |
+
$ python data/preprocess_datasets.py --ds_name DKYoon-SlimPajama-6B --tokenizer_name_or_path tokenizer/Mixtral-8x7B-v0.1-vocab
|
| 216 |
+
```
|
| 217 |
+
|
| 218 |
+
在进行子词切分后，可以使用`data/utils.py`查看各个数据集的token总量：
|
| 219 |
+
|
| 220 |
+
```shell
|
| 221 |
+
$ python data/utils.py
|
| 222 |
+
```
|
| 223 |
+
|
| 224 |
+
#### 开始训练
|
| 225 |
+
|
| 226 |
+
训练启动脚本为`scripts/train.sh`。可以通过修改其中的`TRAIN_DATASETS`修改训练数据集和数据集比例：
|
| 227 |
+
|
| 228 |
+
```shell
|
| 229 |
+
TRAIN_DATASETS=(
|
| 230 |
+
1:SkyPile-150B-2022 # 使用全量SkyPile-150B-2022
|
| 231 |
+
0.1:SkyPile-150B-2023 # 使用SkyPile-150B-2023的10%数据
|
| 232 |
+
1:DKYoon-SlimPajama-6B # 使用全量DKYoon-SlimPajama-6B
|
| 233 |
+
)
|
| 234 |
+
```
|
| 235 |
+
|
| 236 |
+
如果您使用SLURM集群管理系统，可以通过`sbatch`进行提交：
|
| 237 |
+
|
| 238 |
+
```shell
|
| 239 |
+
$ sbatch scripts/train.sh
|
| 240 |
+
```
|
| 241 |
+
|
| 242 |
+
如果没有SLURM或希望通过命令行启动训练，您可以直接提取`scripts/train.sh`中的`torchrun`开始训练。
|
| 243 |
+
|
| 244 |
+
</details>
|
| 245 |
+
|
| 246 |
+
<details>
|
| 247 |
+
<summary>
|
| 248 |
+
|
| 249 |
+
### 微调
|
| 250 |
+
|
| 251 |
+
</summary>
|
| 252 |
+
|
| 253 |
+
本项目发布的Chinese-Mixtral-8x7B为基座模型，没有经过微调。如果您希望使用Chinese-Mixtral-8x7B进行下游任务微调或SFT，可以参考HuggingFace给出Mixtral-8x7B的QLoRA微调脚本进行训练：[HuggingFace的官方示例代码](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)。
|
| 254 |
+
|
| 255 |
+
</details>
|
| 256 |
+
|
| 257 |
+
## ✒️ 引用
|
| 258 |
+
|
| 259 |
+
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代码，请引用本项目：
|
| 260 |
+
|
| 261 |
+
```bibtex
|
| 262 |
+
@misc{Chinese-Mixtral-8x7B,
|
| 263 |
+
author = {HIT-SCIR},
|
| 264 |
+
title = {Chinese-Mixtral-8x7B: An Open-Source Mixture-of-Experts LLM},
|
| 265 |
+
year = {2024},
|
| 266 |
+
publisher = {GitHub},
|
| 267 |
+
journal = {GitHub repository},
|
| 268 |
+
howpublished = {\url{https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B}}
|
| 269 |
+
}
|
| 270 |
+
```
|
| 271 |
+
|
| 272 |
+
## 🌟 Star History
|
| 273 |
+
|
| 274 |
+
[](https://star-history.com/#HIT-SCIR/Chinese-Mixtral-8x7B&Date)
|
config.json
ADDED
|
@@ -0,0 +1,30 @@
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|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "/ssd/home/syji/hf_model/mistralai/Mixtral-8x7B-v0.1-vocab",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"MixtralForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 1,
|
| 8 |
+
"eos_token_id": 2,
|
| 9 |
+
"hidden_act": "silu",
|
| 10 |
+
"hidden_size": 4096,
|
| 11 |
+
"initializer_range": 0.02,
|
| 12 |
+
"intermediate_size": 14336,
|
| 13 |
+
"max_position_embeddings": 32768,
|
| 14 |
+
"model_type": "mixtral",
|
| 15 |
+
"num_attention_heads": 32,
|
| 16 |
+
"num_experts_per_tok": 2,
|
| 17 |
+
"num_hidden_layers": 32,
|
| 18 |
+
"num_key_value_heads": 8,
|
| 19 |
+
"num_local_experts": 8,
|
| 20 |
+
"output_router_logits": false,
|
| 21 |
+
"rms_norm_eps": 1e-05,
|
| 22 |
+
"rope_theta": 1000000.0,
|
| 23 |
+
"router_aux_loss_coef": 0.02,
|
| 24 |
+
"sliding_window": null,
|
| 25 |
+
"tie_word_embeddings": false,
|
| 26 |
+
"torch_dtype": "bfloat16",
|
| 27 |
+
"transformers_version": "4.36.1",
|
| 28 |
+
"use_cache": true,
|
| 29 |
+
"vocab_size": 57000
|
| 30 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 1,
|
| 4 |
+
"eos_token_id": 2,
|
| 5 |
+
"transformers_version": "4.36.1"
|
| 6 |
+
}
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,1002 @@
|
|
|
|
|
|
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