InstructGLM:基于ChatGLM-6B在指令数据集上进行微调

sockstack / 93 / 2023-11-06 23:54:44

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<a id="InstructGLM_0"></a>InstructGLM</h1> 
<blockquote> <p>基于ChatGLM-6B+LoRA在指令数据集上进行微调</p> 
</blockquote> 
<p>https://github.com/yanqiangmiffy/InstructGLM</p> 
<p><strong>本项目主要内容：</strong></p> 
<ul>
<li>🚀 2023/4/9 发布了基于100万条由BELLE项目生成的中文指令数据的Lora权重，具体可见<code>output/belle/chatglm-lora.pt</code>
</li>
<li>🚀 2023/4/8 基于deepspeed支持多卡微调，速度相比单卡提升8-9倍具体设置可见 微调3 基于DeepSpeed进行Lora微调</li>
<li>🚀 2023/3/28 开源了基于alpaca和belle数据指令微调后的lora权重，详情可见output</li>
<li>🚀 2023/3/25 针对ChatGLM-6B模型基于LoRA技术进行微调</li>
<li>🚀 2023/3/23 基于gradio的demo完善</li>
</ul> 
<h2>
<a id="Todo_13"></a>Todo</h2> 
<ul>
<li class="task-list-item">
<input type="checkbox" class="task-list-item-checkbox" checked disabled> deepspeed支持</li>
<li class="task-list-item">
<input type="checkbox" class="task-list-item-checkbox" disabled> 模型评估,如何评估微调后的模型效果</li>
</ul> 
<h2>
<a id="_17"></a>开源指令数据集</h2> 
<ul><li>斯坦福52k英文指令数据</li></ul> 
<blockquote> <p>instruction:52K 条指令中的每一条都是唯一的,答案由text-davinci-003模型生成得到的</p> 
</blockquote> 
<ul><li>BELLE项目生成的中文指令数据：0.5m&amp;1m</li></ul> 
<blockquote> <p>1百万数据：https://huggingface.co/datasets/BelleGroup/generated_train_1M_CN</p> 
</blockquote> 
<blockquote> <p>生成方式基于种子prompt，调用openai的api生成中文指令</p> 
</blockquote> 
<ul><li>GuanacoDataset 多语言指令数据集</li></ul> 
<blockquote> <p>Guanaco 是在 Meta 的 LLaMA 7B 模型上训练的指令跟随语言模型。在 Alpaca 模型原始 52K 数据的基础上，我们添加了额外的 98,369 个条目，涵盖英语、简体中文、繁体中文（台湾）、繁体中文（香港）、日语、德语以及各种语言和语法任务。通过使用这些丰富的数据重新训练和优化模型，Guanaco 在多语言环境中展示了出色的性能和潜力。项目链接可以查看<br> https://guanaco-model.github.io/</p> 
</blockquote> 
<ul><li>alpaca中文指令微调数据集</li></ul> 
<blockquote> <p>与原始alpaca数据json格式相同,数据生成的方法是机器翻译和self-instruct</p> 
</blockquote> 
<ul><li>人工精调的中文对话数据集</li></ul> 
<blockquote> <p>加入除了alpaca之外的其他中文聊天对话<br> 人工微调，部分并不中文化的问题，我们将重新询问chatgpt或文心一言，重新获取回答并覆盖掉alpaca的回答</p> 
</blockquote> 
<ul><li>firefly-train-1.1M ， 一份高质量的包含1.1M中文多任务指令微调数据集，包含23种常见的中文NLP任务的指令数据。对于每个任务，由人工书写若干指令模板，保证数据的高质量与丰富度。</li></ul> 
<h2>
<a id="1alpaca_45"></a>微调1：alpaca英文指令数据</h2> 
<p>斯坦福羊驼52k数据，原始数据格式如下：</p> 
<pre><code class="prism language-text">{"instruction": "Evaluate this sentence for spelling and grammar mistakes","input": "He finnished his meal and left the resturant","output": "He finished his meal and left the restaurant."
}
</code></pre> 
<blockquote> <p>数据集地址：https://github.com/tatsu-lab/stanford_alpaca</p> 
</blockquote> 
<h3>
<a id="1_59"></a>1.数据预处理</h3> 
<p>转化alpaca数据集为jsonl,这一步可以执行设置数据转换后格式，比如：</p> 
<pre><code class="prism language-text">###Instruction:xxx###Input:xxxx###Response:xxx
</code></pre> 
<pre><code class="prism language-shell">python cover_alpaca2jsonl.py <span class="token punctuation">\</span>--data_path data/alpaca_data.json <span class="token punctuation">\</span>--save_path data/alpaca_data.jsonl 
</code></pre> 
<p>对文本进行tokenize,加快训练速度，文本长度可根据运行资源自行设置</p> 
<pre><code class="prism language-shell">python tokenize_dataset_rows.py <span class="token punctuation">\</span>--jsonl_path data/alpaca_data.jsonl <span class="token punctuation">\</span>--save_path data/alpaca <span class="token punctuation">\</span>--max_seq_length <span class="token number">320</span>
</code></pre> 
<h3>
<a id="2__82"></a>2. 模型训练</h3> 
<pre><code class="prism language-shell">python train_lora.py <span class="token punctuation">\</span>--dataset_path data/alpaca <span class="token punctuation">\</span>--lora_rank <span class="token number">8</span> <span class="token punctuation">\</span>--per_device_train_batch_size <span class="token number">2</span> <span class="token punctuation">\</span>--gradient_accumulation_steps <span class="token number">1</span> <span class="token punctuation">\</span>--max_steps <span class="token number">52000</span> <span class="token punctuation">\</span>--save_steps <span class="token number">1000</span> <span class="token punctuation">\</span>--save_total_limit <span class="token number">2</span> <span class="token punctuation">\</span>--learning_rate 2e-5 <span class="token punctuation">\</span>--fp16 <span class="token punctuation">\</span>--remove_unused_columns <span class="token boolean">false</span> <span class="token punctuation">\</span>--logging_steps <span class="token number">50</span> <span class="token punctuation">\</span>--output_dir output
</code></pre> 
<h2>
<a id="2BELLE_100"></a>微调2:BELLE中文指令数据</h2> 
<p>包含543314条由BELLE项目生成的中文指令数据,数据格式如下：</p> 
<table>
<thead><tr>
<th>input</th>
<th>target</th>
</tr></thead>
<tbody><tr>
<td>用一句话描述地球为什么是独一无二的。\n</td>
<td>地球上有适宜生命存在的条件和多样化的生命形式</td>
</tr></tbody>
</table>
<blockquote> <p>数据集地址：https://huggingface.co/datasets/BelleGroup/generated_train_0.5M_CN</p> 
</blockquote> 
<h3>
<a id="1_110"></a>1.数据预处理</h3> 
<p>转化bell数据集为jsonl</p> 
<pre><code class="prism language-shell">
python cover_alpaca2jsonl.py <span class="token punctuation">\</span>--dataset_name BelleGroup/generated_train_0.5M_CN <span class="token punctuation">\</span>--save_path data/belle_data.jsonl 
</code></pre> 
<p>文本长度统计</p> 
<pre><code class="prism language-text">count    543314.000000
mean         83.536944
std          95.665178
min           4.000000
25%          33.000000
50%          51.000000
75%          88.000000
90%         194.000000
max        4410.000000
Name: input_len, dtype: float64count    543314.000000
mean        121.079030
std         165.472722
min           1.000000
25%          27.000000
50%          67.000000
75%         151.000000
90%         296.000000
max        9463.000000
Name: target_len, dtype: float64
</code></pre> 
<p>分词处理</p> 
<pre><code class="prism language-shell">python tokenize_dataset_rows.py <span class="token punctuation">\</span>--jsonl_path data/belle_data.jsonl <span class="token punctuation">\</span>--save_path data/belle <span class="token punctuation">\</span>--max_seq_length <span class="token number">320</span>
</code></pre> 
<p>转换后的数据：</p> 
<pre><code class="prism language-text">                                           input_ids  seq_len                                                                                                                   
0  [20005, 92863, 20012, 20005, 83864, 87784, 871...       20
1  [20005, 92863, 20012, 20005, 91432, 86523, 885...       80
2  [20005, 92863, 20012, 104069, 85056, 86334, 89...       61
3  [20005, 92863, 20012, 91492, 89122, 83866, 852...       24
4  [20005, 92863, 20012, 20005, 83834, 99899, 927...       24
</code></pre> 
<h3>
<a id="2__167"></a>2. 模型训练</h3> 
<ul><li>基于原始chatglm-6b训练</li></ul> 
<pre><code class="prism language-shell">python train_lora.py <span class="token punctuation">\</span>--dataset_path data/belle <span class="token punctuation">\</span>--lora_rank <span class="token number">8</span> <span class="token punctuation">\</span>--per_device_train_batch_size <span class="token number">2</span> <span class="token punctuation">\</span>--gradient_accumulation_steps <span class="token number">1</span> <span class="token punctuation">\</span>--max_steps <span class="token number">52000</span> <span class="token punctuation">\</span>--save_steps <span class="token number">1000</span> <span class="token punctuation">\</span>--save_total_limit <span class="token number">2</span> <span class="token punctuation">\</span>--learning_rate 2e-5 <span class="token punctuation">\</span>--fp16 <span class="token punctuation">\</span>--remove_unused_columns <span class="token boolean">false</span> <span class="token punctuation">\</span>--logging_steps <span class="token number">50</span> <span class="token punctuation">\</span>--output_dir output
</code></pre> 
<ul><li>基于alpaca的lora继续微调</li></ul> 
<pre><code class="prism language-shell">python train_lora.py <span class="token punctuation">\</span>--dataset_path data/belle <span class="token punctuation">\</span>--lora_rank <span class="token number">8</span> <span class="token punctuation">\</span>--per_device_train_batch_size <span class="token number">8</span> <span class="token punctuation">\</span>--gradient_accumulation_steps <span class="token number">1</span> <span class="token punctuation">\</span>--max_steps <span class="token number">52000</span> <span class="token punctuation">\</span>--save_steps <span class="token number">10000</span> <span class="token punctuation">\</span>--save_total_limit <span class="token number">2</span> <span class="token punctuation">\</span>--learning_rate 2e-5 <span class="token punctuation">\</span>--fp16 <span class="token punctuation">\</span>--remove_unused_columns <span class="token boolean">false</span> <span class="token punctuation">\</span>--logging_steps <span class="token number">50</span> <span class="token punctuation">\</span>--output_dir output/belle <span class="token punctuation">\</span>--is_resume True <span class="token punctuation">\</span>--resume_path output/alpaca/chatglm-lora.pt
</code></pre> 
<h2>
<a id="3DeepSpeedLora_207"></a>微调3:基于DeepSpeed进行Lora微调</h2> 
<p>支持多卡+zero方案，训练速度可提高8倍左右</p> 
<pre><code class="prism language-shell">accelerate launch --config_file config/default_config.yaml train_new.py
</code></pre> 
<h2>
<a id="_214"></a>实验环境</h2> 
<ul>
<li>安装所需要的包：pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple</li>
<li>显卡：2xA100 80G</li>
</ul> 
<h2>
<a id="_219"></a>实验结果</h2> 
<ul><li>训练好的lora权重</li></ul> 
<pre><code class="prism language-text">└─output├─alpaca:基于52k微调的lora权重├─belle：:基于52k微调的lora权重+belle微调的权重52000steps└─belle_raw：belle微调的权重104000steps</code></pre> 
<pre><code>
链接：https://pan.baidu.com/s/1c-zRSEUn4151YLoowPN4YA?pwd=hxbr
提取码：hxbr
--来自百度网盘超级会员V3的分享
</code></pre> 
<ul><li>alpaca数据微调效果</li></ul> 
<p><img referrerpolicy="no-referrer"  src="https://img-blog.csdnimg.cn/img_convert/b10015459be4c2074a751c2a07146929.png" alt=""></p> 
<ul><li>belle数据微调效果</li></ul> 
<p><img referrerpolicy="no-referrer"  src="https://img-blog.csdnimg.cn/img_convert/d3d14536b7ba0037f49b94aaa66a1cd3.jpeg" alt=""></p> 
<h2>
<a id="Reference_247"></a>Reference</h2> 
<blockquote> <p>非常感谢以下作者的无私开源</p> 
</blockquote> 
<ul>
<li>https://github.com/mymusise/ChatGLM-Tuning</li>
<li>https://huggingface.co/BelleGroup/BELLE-7B-2M</li>
<li>https://github.com/LianjiaTech/BELLE</li>
<li>https://huggingface.co/datasets/BelleGroup/generated_train_0.5M_CN</li>
<li>https://huggingface.co/datasets/JosephusCheung/GuanacoDataset</li>
<li>https://guanaco-model.github.io/</li>
<li>https://github.com/carbonz0/alpaca-chinese-dataset</li>
<li>https://github.com/THUDM/ChatGLM-6B</li>
<li>https://huggingface.co/THUDM/chatglm-6b</li>
<li>https://github.com/lich99/ChatGLM-finetune-LoRA</li>
</ul> 
<h2>
<a id="Bugs_262"></a>Bugs</h2> 
<ul><li>gcc版本升级</li></ul> 
<pre><code>yum install centos-release-scl -y
yum install devtoolset-9 -y#临时覆盖系统原有的gcc引用
scl enable devtoolset-9 bash# 查看gcc版本
gcc -v
</code></pre>
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作者

sockstack

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2023-11-06

修改于

2024-07-27

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InstructGLM:基于ChatGLM-6B在指令数据集上进行微调

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