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--- |
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license: apache-2.0 |
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language: |
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- zh |
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- en |
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library_name: transformers |
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tags: |
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- qihoo360 |
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- 奇虎360 |
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- zhinao |
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- 360Zhinao |
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- pretrain |
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--- |
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<p align="left"> |
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中文 ｜   <a href="./README.md">English</a></a>  |
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</p> |
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<br> |
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<div align="center"> |
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<h1> |
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360智脑 |
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</h1> |
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</div> |
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<div align="center"> |
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🤗 <a href="https://huggingface.co/qihoo360">Hugging Face</a>   |    |
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🤖 <a href="https://modelscope.cn/organization/360zhinao">ModelScope</a>   ｜    |
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💬 <a href="./assets/WeChat.png">WeChat (微信)</a>   |
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</div> |
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<br> |
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<p align="center"> |
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欢迎访问360智脑官网<a href="https://ai.360.com"> https://ai.360.com </a>体验更多更强大的功能。 |
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</p> |
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<br> |
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# 模型介绍 |
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🎉🎉🎉我们开源了360智脑大模型的系列工作，本次开源了以下模型： |
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- **360Zhinao2-7B-Base** |
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- **360Zhinao2-7B-Chat-4K** |
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- **360Zhinao2-7B-Chat-32K** |
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- **360Zhinao2-7B-Chat-360K** |
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360智脑大模型特点如下： |
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- **基础模型**：采⽤当前主流的两阶段训练⽅法，第⼀阶段采用cosine学习率总共训练10T |
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token，第二阶段我们加⼤了⾼质量数据的占⽐，训练了100B⾼质量token，学习率LR直接decay到0。**360Zhinao2-7B总共训练数据量达10.1T token**。 |
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- **对话模型**：具有强大的对话能力，开放4K、32K、360K三种不同文本长度。 |
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<br> |
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# 更新信息 |
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- [2024.11.18] 🔥🔥🔥我们发布了360Zhinao2-7B，同时开放Base模型和4K、32K、360K三种文本长度的Chat模型。 |
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- [2024.05.23] 我们发布了360Zhinao-search以及360Zhinao-1.8B-Reranking两个模型，分别在[C-MTEB 榜单](https://huggingface.co/spaces/mteb/leaderboard)的Retrieval和Reranking任务上排名第一。 |
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- [2024.05.20] 我们将llama3的窗口长度扩展到360k并发布了**llama3-8B-360Zhinao-360k-Instruct**<a href="https://huggingface.co/qihoo360/llama3-8B-360Zhinao-360k-Instruct">🤗</a> |
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- [2024.04.12] 我们发布了360Zhinao-7B 1.0版本，同时开放Base模型和4K、32K、360K三种文本长度的Chat模型。 |
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技术报告详见[arXiv](https://arxiv.org/abs/2405.13386)。 |
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<br> |
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# 目录 |
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- [下载地址](#下载地址) |
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- [模型评估](#模型评估) |
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- [快速开始](#快速开始) |
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- [模型推理](#模型推理) |
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- [模型微调](#模型微调) |
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- [许可证](#许可证) |
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<br> |
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# 下载地址 |
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本次发布版本和下载链接见下表： |
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| Size | Model | BF16 | Int4| |
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|:-:|-|:-:|:-:| |
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| 7B | 360Zhinao2-7B-Base | <a href="https://modelscope.cn/models/360zhinao/360Zhinao2-7B-Base/summary">🤖</a> <a href="https://huggingface.co/qihoo360/360Zhinao2-7B-Base">🤗</a> | | |
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| 7B | 360Zhinao2-7B-Chat-4K | <a href="https://modelscope.cn/models/360zhinao/360Zhinao2-7B-Chat-4K/summary">🤖</a> <a href="https://huggingface.co/qihoo360/360Zhinao2-7B-Chat-4K">🤗</a> | <a href="https://modelscope.cn/models/360zhinao/360Zhinao2-7B-Chat-4K-Int4/summary">🤖</a> <a href="https://huggingface.co/qihoo360/360Zhinao2-7B-Chat-4K-Int4">🤗</a> | |
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| 7B | 360Zhinao2-7B-Chat-32K | <a href="https://modelscope.cn/models/360zhinao/360Zhinao2-7B-Chat-32K/summary">🤖</a> <a href="https://huggingface.co/qihoo360/360Zhinao2-7B-Chat-32K">🤗</a> | <a href="https://modelscope.cn/models/360zhinao/360Zhinao2-7B-Chat-32K-Int4/summary">🤖</a> <a href="https://huggingface.co/qihoo360/360Zhinao2-7B-Chat-32K-Int4">🤗</a> | |
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| 7B | 360Zhinao2-7B-Chat-360K | <a href="https://modelscope.cn/models/360zhinao/360Zhinao2-7B-Chat-360K/summary">🤖</a> <a href="https://huggingface.co/qihoo360/360Zhinao2-7B-Chat-360K">🤗</a> | <a href="https://modelscope.cn/models/360zhinao/360Zhinao2-7B-Chat-360K-Int4/summary">🤖</a> <a href="https://huggingface.co/qihoo360/360Zhinao2-7B-Chat-360K-Int4">🤗</a> | |
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<br> |
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|
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# 模型评估 |
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|
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## 基础模型 |
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我们使⽤了开源⼯具opencompass对模型进⾏评估，对⽐了近半年国内外开源的10B以下模型， |
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360Zhinao2-7B具备较强的竞争⼒。360Zhinao2-7B在CEval（中⽂ |
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考试）、C3（中⽂阅读理解）、lcsts（中⽂短⽂本摘要）等中⽂benchmark上表现不俗，中⽂ |
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benchmark均分排名第⼀。在挑战性的竞赛数学数据集math上，同样排名第⼀。**360Zhinao2-7B模 |
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型在中⽂处理能⼒、复杂数学推理能⼒两个⽅⾯，具备优势。** |
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|
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<table> |
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<tr> |
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<td>Type</td><td>Datasets</td><td>language</td><td>glm4-9b</td><td>Qwen2.5-7B</td><td>internlm2.5-7b</td><td>Yi1.5-9B</td><td>gemma2-9b</td><td>Llama3.1-8B</td><td>360Zhinao2-7B</td> |
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</tr> |
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<tr> |
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<td rowspan="5">Exam</td><td>ceval</td><td>zh</td><td>75.83</td><td>81.41</td><td>77.71</td><td>73.51</td><td>56.36</td><td>51.67</td><td><strong>83.04</strong></td> |
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</tr> |
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<tr> |
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<td>mmlu</td><td>en</td><td>75.5</td><td>75.5</td><td>71.55</td><td>71.43</td><td>72.22</td><td>66.75</td><td>67.84</td> |
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</tr> |
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<tr> |
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<td>cmmlu</td><td>zh</td><td>74.24</td><td>81.79</td><td>78.77</td><td>74.2</td><td>58.89</td><td>52.49</td><td>73.8</td> |
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</tr> |
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<tr> |
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<td>ARC-c</td><td>en</td><td>94.92</td><td>80</td><td>85.08</td><td>87.46</td><td>77.63</td><td>80.68</td><td>87.12</td> |
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</tr> |
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<tr> |
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<td>ARC-e</td><td>en</td><td>98.41</td><td>84.83</td><td>95.24</td><td>94.53</td><td>78.84</td><td>89.77</td><td>92.77</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Language</td><td>WiC</td><td>en</td><td>51.57</td><td>52.82</td><td>50.78</td><td>50.63</td><td>50.47</td><td>50</td><td>49.84</td> |
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</tr> |
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<tr> |
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<td>WSC</td><td>en</td><td>68.27</td><td>68.27</td><td>69.23</td><td>66.35</td><td>68.27</td><td>67.31</td><td>65.38</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Knowledge</td> |
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<td>BoolQ</td><td>en</td><td>81.8</td><td>83.88</td><td>89.51</td><td>84.46</td><td>85.6</td><td>82.2</td><td>88.29</td> |
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</tr> |
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<tr> |
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<td>commonsense_qa</td><td>en</td><td>71.17</td><td>73.22</td><td>68.55</td><td>71.58</td><td>68.47</td><td>71.25</td><td>69.78</td> |
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</tr> |
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<tr> |
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<td rowspan="6">Understanding</td> |
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<td>C3</td><td>zh</td><td>91.51</td><td>92</td><td>93.04</td><td>85.86</td><td>81.64</td><td>83.51</td><td><strong>93.26</strong></td> |
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</tr> |
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<tr> |
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<td>race-middle</td><td>en</td><td>91.99</td><td>91.02</td><td>92.06</td><td>91.16</td><td>88.09</td><td>81.69</td><td>90.46</td> |
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</tr> |
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<tr> |
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<td>race-high</td><td>en</td><td>90.71</td><td>87.91</td><td>90.08</td><td>88.34</td><td>82.08</td><td>78.73</td><td>86.74</td> |
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</tr> |
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<tr> |
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<td>lcsts</td><td>zh</td><td>18.29</td><td>15.82</td><td>15.96</td><td>16.49</td><td>10.62</td><td>17.29</td><td><strong>18.61</strong></td> |
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</tr> |
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<tr> |
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<td>eprstmt-dev</td><td>zh</td><td>91.88</td><td>86.88</td><td>91.25</td><td>91.88</td><td>48.12</td><td>83.12</td><td>90</td> |
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</tr> |
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<tr> |
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<td>lambada</td><td>en</td><td>71.67</td><td>71.14</td><td>69.98</td><td>70.64</td><td>75.43</td><td>74.23</td><td>72.56</td> |
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</tr> |
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<tr> |
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<td rowspan="3">Reasoning</td> |
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<td>hellaswag</td><td>en</td><td>70.25</td><td>72.76</td><td>70.38</td><td>71.55</td><td>66.83</td><td>74.65</td><td>71.49</td> |
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</tr> |
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<tr> |
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<td>siqa</td><td>en</td><td>81.73</td><td>72.52</td><td>78.97</td><td>76.2</td><td>58.96</td><td>64.18</td><td>77.12</td> |
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</tr> |
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<tr> |
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<td>bbh</td><td>en</td><td>73.68</td><td>54.63</td><td>59.43</td><td>67.86</td><td>68.45</td><td>59.9</td><td>46.54</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Code</td> |
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<td>humaneval</td><td>en</td><td>69.51</td><td>75</td><td>60.37</td><td>26.22</td><td>5.49</td><td>27.44</td><td>60.98</td> |
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</tr> |
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<tr> |
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<td>mbpp</td><td>en</td><td>60</td><td>60</td><td>43.6</td><td>56.8</td><td>51.2</td><td>42.6</td><td>54</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Math</td> |
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<td>math</td><td>en</td><td>26.86</td><td>38</td><td>27.14</td><td>27.06</td><td>28.52</td><td>15.32</td><td><strong>38.34</strong></td> |
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</tr> |
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<tr> |
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<td>gsm8k</td><td>en</td><td>78.54</td><td>79.76</td><td>52.54</td><td>71.11</td><td>73.09</td><td>56.25</td><td>75.51</td> |
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</tr> |
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<tr> |
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<td rowspan="2">Overall</td> |
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<td>avg_zh</td><td></td><td>70.35</td><td>71.58</td><td>71.35</td><td>68.39</td><td>51.13</td><td>57.62</td><td><strong>71.74</strong></td> |
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</tr> |
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<tr> |
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<td>avg_all</td><td></td><td>73.11</td><td>71.78</td><td>69.60</td><td>68.88</td><td>61.60</td><td>62.32</td><td>70.61</td> |
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</tr> |
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</table> |
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|
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|
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## Chat模型 |
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|
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### 后训练数据 |
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高质量微调数据50w，该数据综合考虑大模型通用技能及360垂直业务数据，生成方法如下： |
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1. 数据多样性：根据360自有标签体系进行领域，意图，难度，长度的分层采样，确保指令多样性 |
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2. 数据质量：使用偏序数据训练360gpt-pro-rm（reward bench得分92.59），用该模型进行样本筛选，过滤掉低质数据 |
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3. 复杂指令进化：使用进化方式做复杂指令优化，优化指令跟随能力 |
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|
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### 训练方法 |
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1. 全参数微调 |
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基于通用后训练数据，进行全参数微调,选择最优checkpoint作为sft-base。 |
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2. Lora offline DPO强化 |
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使用人类标注好的偏好pair对，采用Lora方法对sft-base进行lora微调，然后进行lora DPO训练。 |
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3. Iterative on-policy DPO 全参数强化 |
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使用sft-base模型在训练prompt上采样多个答案，用360gpt-pro-rm打分，取最高最低分组pair进行DPO训练。我们迭代地使用这种on-policy DPO提升模型效果。 |
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4. 模型合并 |
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在360公司白盒评测集合4上，针对上述3个模型做自动评测，发现不同模型各有其优势技能，考虑模型合并方案，得到最终的Chat模型. |
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### 模型效果 |
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我们在IFEval、MT-bench、CF-Bench三个经典任务上对 360Zhinao2-7B-Chat-4k 模型进行了评测，模型效果具备较强竞争力。IFEval (prompt strict) 仅次于GLM4-9B,在7B开源模型中得分最高，详细结果如下表: |
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|
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| Model | MT-bench | IFEval(strict prompt) | CFBench(CSR,ISR,PSR) | | | |
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|----------------------|----------|-----------------------|----------------------|------|------| |
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| Qwen2.5-7B-Instruct | **8.07** | 0.556 | **0.81** | 0.46 | 0.57 | |
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| Yi-9B-16k-Chat | 7.44 | 0.455 | 0.75 | 0.4 | 0.52 | |
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| GLM4-9B-Chat | **8.08** | **0.634** | **0.82** | 0.48 | 0.61 | |
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| InternLM2.5-7B-Chat | 7.39 | 0.540 | 0.78 | 0.4 | 0.54 | |
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| 360Zhinao2-7B-Chat-4k| 7.86 | **0.577** | 0.8 | 0.44 | 0.57 | |
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### 长文本微调 |
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与360Zhinao1开源时的做法基本一致，我们将RoPE base依次扩大为1000,000和50,000,000，混合长短文本的SFT数据依次拼接至32k和360k，将gradient checkpointing、ZeRO3 offload和ring attention等技术结合，依次微调得到32k和360k长文本模型。在各个32k benchmark上位列第一梯队。 |
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| Model | LooGLE-长依赖QA | Loong-Set 1 (32k) | LongBench-Chat (32k截断) | LEval-96题子集胜率 | LEval-客观题均分 | |
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|------------------------------|-----------------|-------------------|--------------------------|--------------------|------------------| |
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| GLM4-9B-Chat | 0.36 | 55.24 | 6.60 | 0.49 | 63.96 | |
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| InternLM2.5-7B-Chat | 0.39 | 42.76 | 5.70 | 0.44 | 61.64 | |
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| 360Zhinao2-7B-Chat-32k | 0.33 | 39.37 | 5.44 | 0.44 | 60.48 | |
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| 360Zhinao2-7B-Chat-360k | 0.34 | 32.16 | 5.08 | 0.38 | 53.00 | |
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| Yi-1.5-9B-Chat | 0.25 | 32.77 | 4.70 | 0.37 | 56.22 | |
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<br> |
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|
|
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|
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# 快速开始 |
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简单的示例来说明如何利用🤖 ModelScope和🤗 Transformers快速使用360Zhinao2-7B-Base和360Zhinao2-7B-Chat |
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|
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## 依赖安装 |
|
- python 3.8 and above |
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- pytorch 2.0 and above |
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- transformers 4.37.2 and above |
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- CUDA 11.4 and above are recommended. |
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|
|
```shell |
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pip install -r requirements.txt |
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``` |
|
我们推荐安装flash-attention（当前已支持flash attention 2）来提高你的运行效率以及降低显存占用。(flash-attention只是可选项，不安装也可正常运行该项目) |
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|
|
>flash-attn >= 2.3.6 |
|
```shell |
|
FLASH_ATTENTION_FORCE_BUILD=TRUE pip install flash-attn==2.3.6 |
|
``` |
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|
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|
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## 🤗 Transformers |
|
### Base模型推理 |
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|
|
此代码演示使用transformers快速使用360Zhinao2-7B-Base模型进行推理 |
|
```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from transformers.generation import GenerationConfig |
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|
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MODEL_NAME_OR_PATH = "qihoo360/360Zhinao2-7B-Base" |
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|
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tokenizer = AutoTokenizer.from_pretrained( |
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MODEL_NAME_OR_PATH, |
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trust_remote_code=True) |
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|
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model = AutoModelForCausalLM.from_pretrained( |
|
MODEL_NAME_OR_PATH, |
|
device_map="auto", |
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trust_remote_code=True) |
|
|
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generation_config = GenerationConfig.from_pretrained( |
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MODEL_NAME_OR_PATH, |
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trust_remote_code=True) |
|
|
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inputs = tokenizer('中国二十四节气\n1. 立春\n2. 雨水\n3. 惊蛰\n4. 春分\n5. 清明\n', return_tensors='pt') |
|
inputs = inputs.to(model.device) |
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|
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pred = model.generate(input_ids=inputs["input_ids"], generation_config=generation_config) |
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print("outputs:\n", tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) |
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``` |
|
|
|
### Chat模型推理 |
|
|
|
此代码演示使用transformers快速使用360Zhinao2-7B-Chat-4K模型进行推理 |
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
from transformers.generation import GenerationConfig |
|
|
|
MODEL_NAME_OR_PATH = "qihoo360/360Zhinao2-7B-Chat-4K" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained( |
|
MODEL_NAME_OR_PATH, |
|
trust_remote_code=True) |
|
|
|
model = AutoModelForCausalLM.from_pretrained( |
|
MODEL_NAME_OR_PATH, |
|
device_map="auto", |
|
trust_remote_code=True) |
|
|
|
generation_config = GenerationConfig.from_pretrained( |
|
MODEL_NAME_OR_PATH, |
|
trust_remote_code=True) |
|
|
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messages = [] |
|
#round-1 |
|
messages.append({"role": "user", "content": "介绍一下刘德华"}) |
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response = model.chat(tokenizer=tokenizer, messages=messages, generation_config=generation_config) |
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messages.append({"role": "assistant", "content": response}) |
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print(messages) |
|
|
|
#round-2 |
|
messages.append({"role": "user", "content": "他有什么代表作？"}) |
|
response = model.chat(tokenizer=tokenizer, messages=messages, generation_config=generation_config) |
|
messages.append({"role": "assistant", "content": response}) |
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print(messages) |
|
``` |
|
|
|
## 🤖 ModelScope |
|
### Base模型推理 |
|
|
|
此代码演示使用ModelScope快速使用360Zhinao2-7B-Base模型进行推理 |
|
|
|
|
|
```python |
|
from modelscope import AutoModelForCausalLM, AutoTokenizer |
|
from modelscope import GenerationConfig |
|
|
|
MODEL_NAME_OR_PATH = "qihoo360/360Zhinao2-7B-Base" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained( |
|
MODEL_NAME_OR_PATH, |
|
trust_remote_code=True) |
|
|
|
model = AutoModelForCausalLM.from_pretrained( |
|
MODEL_NAME_OR_PATH, |
|
device_map="auto", |
|
trust_remote_code=True) |
|
|
|
generation_config = GenerationConfig.from_pretrained( |
|
MODEL_NAME_OR_PATH, |
|
trust_remote_code=True) |
|
|
|
inputs = tokenizer('中国二十四节气\n1. 立春\n2. 雨水\n3. 惊蛰\n4. 春分\n5. 清明\n', return_tensors='pt') |
|
inputs = inputs.to(model.device) |
|
|
|
pred = model.generate(input_ids=inputs["input_ids"], generation_config=generation_config) |
|
print("outputs:\n", tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) |
|
``` |
|
|
|
### Chat模型推理 |
|
|
|
此代码演示使用ModelScope快速使用360Zhinao2-7B-Chat-4K模型进行推理 |
|
```python |
|
from modelscope import AutoModelForCausalLM, AutoTokenizer |
|
from modelscope import GenerationConfig |
|
|
|
MODEL_NAME_OR_PATH = "qihoo360/360Zhinao2-7B-Chat-4K" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained( |
|
MODEL_NAME_OR_PATH, |
|
trust_remote_code=True) |
|
|
|
model = AutoModelForCausalLM.from_pretrained( |
|
MODEL_NAME_OR_PATH, |
|
device_map="auto", |
|
trust_remote_code=True) |
|
|
|
generation_config = GenerationConfig.from_pretrained( |
|
MODEL_NAME_OR_PATH, |
|
trust_remote_code=True) |
|
|
|
messages = [] |
|
#round-1 |
|
messages.append({"role": "user", "content": "介绍一下刘德华"}) |
|
response = model.chat(tokenizer=tokenizer, messages=messages, generation_config=generation_config) |
|
messages.append({"role": "assistant", "content": response}) |
|
print(messages) |
|
|
|
#round-2 |
|
messages.append({"role": "user", "content": "他有什么代表作？"}) |
|
response = model.chat(tokenizer=tokenizer, messages=messages, generation_config=generation_config) |
|
messages.append({"role": "assistant", "content": response}) |
|
print(messages) |
|
``` |
|
|
|
## 终端 Demo |
|
可使用终端交互实现快速体验 |
|
```shell |
|
python cli_demo.py |
|
``` |
|
<p align="center"> |
|
<img src="assets/cli_demo.gif" width="600" /> |
|
<p> |
|
|
|
注：我们尚未支持Mac上`device = 'mps'`。 |
|
|
|
## 网页 Demo |
|
也可使用网页交互实现快速体验 |
|
```shell |
|
streamlit run web_demo.py |
|
``` |
|
<p align="center"> |
|
<img src="assets/web_demo.gif" width="600" /> |
|
<p> |
|
|
|
## API Demo |
|
启动命令 |
|
```shell |
|
python openai_api.py |
|
``` |
|
|
|
请求参数 |
|
```shell |
|
curl 'http://localhost:8360/v1/chat/completions' \ |
|
-H 'Content-Type: application/json' \ |
|
-d '{ |
|
"max_new_tokens": 200, |
|
"do_sample": true, |
|
"top_k": 0, |
|
"top_p": 0.8, |
|
"temperature": 1.0, |
|
"repetition_penalty": 1.0, |
|
"messages": [ |
|
{"role": "system", "content": "You are a helpful assistant."}, |
|
{"role": "user", "content": "你好"} |
|
] |
|
}' |
|
``` |
|
|
|
<br> |
|
|
|
# 模型推理 |
|
## 模型量化 |
|
我们提供了基于AutoGPTQ的量化方案，并开源了Int4量化模型。 |
|
|
|
## 模型部署 |
|
### vLLM安装环境 |
|
如希望部署及加速推理，我们建议你使用 `vLLM==0.3.3`。 |
|
|
|
如果你使用**CUDA 12.1和PyTorch 2.1**，可以直接使用以下命令安装vLLM。 |
|
```shell |
|
pip install vllm==0.3.3 |
|
``` |
|
|
|
否则请参考vLLM官方的[安装说明](https://docs.vllm.ai/en/latest/getting_started/installation.html)。 |
|
|
|
>安装完成后，还需要以下操作~ |
|
1. 把vllm/zhinao.py文件复制到env环境对应的vllm/model_executor/models目录下。 |
|
2. 把vllm/serving_chat.py文件复制到env环境对应的vllm/entrypoints/openai目录下。 |
|
3. 然后在vllm/model_executor/models/\_\_init\_\_.py文件增加一行代码 |
|
|
|
```shell |
|
"ZhinaoForCausalLM": ("zhinao", "ZhinaoForCausalLM"), |
|
``` |
|
|
|
### vLLM服务启动 |
|
|
|
启动服务 |
|
```shell |
|
python -m vllm.entrypoints.openai.api_server \ |
|
--served-model-name 360Zhinao2-7B-Chat-4K \ |
|
--model qihoo360/360Zhinao2-7B-Chat-4K \ |
|
--trust-remote-code \ |
|
--tensor-parallel-size 1 \ |
|
--max-model-len 4096 \ |
|
--host 0.0.0.0 \ |
|
--port 8360 |
|
``` |
|
|
|
使用curl请求服务 |
|
```shell |
|
curl http://localhost:8360/v1/chat/completions \ |
|
-H "Content-Type: application/json" \ |
|
-d '{ |
|
"model": "360Zhinao2-7B-Chat-4K", |
|
"max_tokens": 200, |
|
"top_k": -1, |
|
"top_p": 0.8, |
|
"temperature": 1.0, |
|
"presence_penalty": 0.0, |
|
"frequency_penalty": 0.0, |
|
"messages": [ |
|
{"role": "system", "content": "You are a helpful assistant."}, |
|
{"role": "user", "content": "你好"} |
|
], |
|
"stop": [ |
|
"<eod>", |
|
"<|im_end|>", |
|
"<|im_start|>" |
|
] |
|
}' |
|
``` |
|
使用python请求服务 |
|
```python |
|
from openai import OpenAI |
|
# Set OpenAI's API key and API base to use vLLM's API server. |
|
openai_api_key = "EMPTY" |
|
openai_api_base = "http://localhost:8360/v1" |
|
|
|
client = OpenAI( |
|
api_key=openai_api_key, |
|
base_url=openai_api_base, |
|
) |
|
|
|
chat_response = client.chat.completions.create( |
|
model="360Zhinao2-7B-Chat-4K", |
|
messages=[ |
|
{"role": "system", "content": "You are a helpful assistant."}, |
|
{"role": "user", "content": "你好"}, |
|
], |
|
stop=[ |
|
"<eod>", |
|
"<|im_end|>", |
|
"<|im_start|>" |
|
], |
|
presence_penalty=0.0, |
|
frequency_penalty=0.0 |
|
) |
|
print("Chat response:", chat_response) |
|
``` |
|
|
|
> 注意：如需要开启重复惩罚，建议使用 *presence_penalty* 和 *frequency_penalty* 参数。 |
|
|
|
<br> |
|
|
|
# 模型微调 |
|
## 训练数据 |
|
|
|
我们提供了微调训练样例数据 data/test.json，该样例数据是从 [multiturn_chat_0.8M](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) 采样出 1 万条，并且做了格式转换。 |
|
|
|
数据格式: |
|
```json |
|
[ |
|
{ |
|
"id": 1, |
|
"conversations": [ |
|
{ |
|
"from": "system", |
|
"value": "You are a helpful assistant." |
|
}, |
|
{ |
|
"from": "user", |
|
"value": "您好啊" |
|
}, |
|
{ |
|
"from": "assistant", |
|
"value": "你好！我今天能为您做些什么？有什么问题或需要帮助吗? 我在这里为您提供服务。" |
|
} |
|
] |
|
} |
|
] |
|
``` |
|
|
|
## 微调训练 |
|
训练脚本如下： |
|
```shell |
|
set -x |
|
|
|
HOSTFILE=hostfile |
|
DS_CONFIG=./finetune/ds_config_zero2.json |
|
|
|
# PARAMS |
|
LR=5e-6 |
|
EPOCHS=3 |
|
MAX_LEN=4096 |
|
BATCH_SIZE=4 |
|
NUM_NODES=1 |
|
NUM_GPUS=8 |
|
MASTER_PORT=29500 |
|
|
|
IS_CONCAT=False # 是否数据拼接到最大长度（MAX_LEN） |
|
|
|
DATA_PATH="./data/training_data_sample.json" |
|
MODEL_PATH="qihoo360/360Zhinao2-7B-Base" |
|
OUTPUT_DIR="./outputs/" |
|
|
|
deepspeed --hostfile ${HOSTFILE} \ |
|
--master_port ${MASTER_PORT} \ |
|
--num_nodes ${NUM_NODES} \ |
|
--num_gpus ${NUM_GPUS} \ |
|
finetune.py \ |
|
--report_to "tensorboard" \ |
|
--data_path ${DATA_PATH} \ |
|
--model_name_or_path ${MODEL_PATH} \ |
|
--output_dir ${OUTPUT_DIR} \ |
|
--model_max_length ${MAX_LEN} \ |
|
--num_train_epochs ${EPOCHS} \ |
|
--per_device_train_batch_size ${BATCH_SIZE} \ |
|
--gradient_accumulation_steps 1 \ |
|
--save_strategy steps \ |
|
--save_steps 200 \ |
|
--learning_rate ${LR} \ |
|
--lr_scheduler_type cosine \ |
|
--adam_beta1 0.9 \ |
|
--adam_beta2 0.95 \ |
|
--adam_epsilon 1e-8 \ |
|
--max_grad_norm 1.0 \ |
|
--weight_decay 0.1 \ |
|
--warmup_ratio 0.01 \ |
|
--gradient_checkpointing True \ |
|
--bf16 True \ |
|
--tf32 True \ |
|
--deepspeed ${DS_CONFIG} \ |
|
--is_concat ${IS_CONCAT} \ |
|
--logging_steps 1 \ |
|
--log_on_each_node False |
|
``` |
|
```shell |
|
bash finetune/ds_finetune.sh |
|
``` |
|
- 可通过配置hostfile，实现单机、多机训练。 |
|
- 可通过配置ds_config，实现zero2、zero3。 |
|
- 可通过配置fp16、bf16实现混合精度训练，建议使用bf16，与预训练模型保持一致。 |
|
- 可通过配置is_concat参数，控制训练数据是否拼接，当训练数据量级较大时，可通过拼接提升训练效率。 |
|
|
|
<br> |
|
|
|
# 许可证 |
|
|
|
本仓库源码遵循开源许可证Apache 2.0。 |
|
|
|
360智脑开源模型支持免费商用，无需向我们进行特殊申请。 |
