Commit
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Parent(s):
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initial commit
Browse files- LICENSE +201 -0
- README.MD +1 -0
- README.md +728 -0
- README_en.md +1 -0
- README_zh.MD +1 -0
- assets/imgs/llama_cpp_1.png +0 -0
- assets/imgs/model_cap_en.png +0 -0
- assets/imgs/model_cap_zh.png +0 -0
- assets/imgs/opencompass_en.png +0 -0
- assets/imgs/opencompass_zh.png +0 -0
- assets/imgs/orion_start.PNG +0 -0
- assets/imgs/vllm_1.png +0 -0
- assets/imgs/wechat_group.jpg +0 -0
- config.json +31 -0
- configuration.json +1 -0
- configuration_orion.py +82 -0
- modeling_orion.py +1097 -0
- pytorch_model-00001-of-00002.bin +3 -0
- pytorch_model-00002-of-00002.bin +3 -0
- pytorch_model.bin.index.json +491 -0
- special_tokens_map.json +24 -0
- tokenization_orion.py +269 -0
- tokenizer.model +3 -0
- tokenizer_config.json +46 -0
LICENSE
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README.MD
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|
| 1 |
+
<!-- markdownlint-disable first-line-h1 -->
|
| 2 |
+
<!-- markdownlint-disable html -->
|
| 3 |
+
<div align="center">
|
| 4 |
+
<img src="./assets/imgs/orion_start.PNG" alt="logo" width="30%" />
|
| 5 |
+
</div>
|
| 6 |
+
|
| 7 |
+
<div align="center">
|
| 8 |
+
<h1>
|
| 9 |
+
Orion-14B
|
| 10 |
+
</h1>
|
| 11 |
+
</div>
|
| 12 |
+
|
| 13 |
+
<div align="center">
|
| 14 |
+
|
| 15 |
+
<div align="center">
|
| 16 |
+
<b>🇨🇳中文</b> | <a href="#table-of-contents">🌐English</a>
|
| 17 |
+
</div>
|
| 18 |
+
|
| 19 |
+
<h4 align="center">
|
| 20 |
+
<p>
|
| 21 |
+
🤗 <a href="https://huggingface.co/OrionStarAI" target="_blank">HuggingFace主页</a> | 🤖 <a href="https://modelscope.cn/organization/OrionStarAI" target="_blank">ModelScope主页</a><br>🎬 <a href="https://huggingface.co/spaces/OrionStarAI/Orion-14B-App-Demo" target="_blank">HuggingFace在线试用</a> | 🎫 <a href="https://modelscope.cn/studios/OrionStarAI/Orion-14B-App-Demo/summary" target="_blank">ModelScope在线试用</a><br>😺 <a href="https://github.com/OrionStarAI/Orion" target="_blank">GitHub</a><br>📖 <a href="https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf" target="_blank">技术报告</a>
|
| 22 |
+
<p>
|
| 23 |
+
</h4>
|
| 24 |
+
|
| 25 |
+
</div>
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
# 目录
|
| 30 |
+
|
| 31 |
+
- [📖 模型介绍](#zh_model-introduction)
|
| 32 |
+
- [🔗 下载路径](#zh_model-download)
|
| 33 |
+
- [🔖 评估结果](#zh_model-benchmark)
|
| 34 |
+
- [📊 模型推理](#zh_model-inference) [<img src="./assets/imgs/vllm_1.png" alt="vllm" style="margin: 0;display: initial;" height="20" />](#vllm) [<img src="./assets/imgs/llama_cpp_1.png" alt="llamacpp" style="margin: 0;display: initial;" height="20" />](#llama-cpp)
|
| 35 |
+
- [📜 声明协议](#zh_declarations-license)
|
| 36 |
+
- [🥇 企业介绍](#zh_company-introduction)
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
<a name="zh_model-introduction"></a><br>
|
| 40 |
+
# 1. 模型介绍
|
| 41 |
+
|
| 42 |
+
- Orion-14B-Base是一个具有140亿参数的多语种大模型，该模型在一个包含2.5万亿token的多样化数据集上进行了训练，涵盖了中文、英语、日语、韩语等多种语言。在多语言环境下的一系列任务中展现出卓越的性能。在主流的公开基准评测中，Orion-14B系列模型表现优异，多项指标显著超越同等参数基本的其他模型。具体技术细节请参考[技术报告](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf)。
|
| 43 |
+
|
| 44 |
+
- Orion-14B系列大模型有以下几个特点：
|
| 45 |
+
- 基座20B参数级别大模型综合评测效果表现优异
|
| 46 |
+
- 多语言能力强，在日语、韩语测试集上显著领先
|
| 47 |
+
- 微调模型适应性强，在人类标注盲测中，表现突出
|
| 48 |
+
- 长上下文版本支持超长文本，在200k token长度上效果优异，最长可支持可达320k
|
| 49 |
+
- 量化版本模型大小缩小70%，推理速度提升30%，性能损失小于1%
|
| 50 |
+
|
| 51 |
+
<table style="border-collapse: collapse; width: 100%;">
|
| 52 |
+
<tr>
|
| 53 |
+
<td style="border: none; padding: 10px; box-sizing: border-box;">
|
| 54 |
+
<img src="./assets/imgs/opencompass_zh.png" alt="opencompass" style="width: 100%; height: auto;">
|
| 55 |
+
</td>
|
| 56 |
+
<td style="border: none; padding: 10px; box-sizing: border-box;">
|
| 57 |
+
<img src="./assets/imgs/model_cap_zh.png" alt="modelcap" style="width: 100%; height: auto;">
|
| 58 |
+
</td>
|
| 59 |
+
</tr>
|
| 60 |
+
</table>
|
| 61 |
+
|
| 62 |
+
- 具体而言，Orion-14B系列大语言模型包含:
|
| 63 |
+
- **Orion-14B-Base:** 基于2.5万亿tokens多样化数据集训练处的140亿参数量级的多语言基座模型。
|
| 64 |
+
- **Orion-14B-Chat:** 基于高质量语料库微调的对话类模型，旨在为大模型社区提供更好的用户交互体验。
|
| 65 |
+
- **Orion-14B-LongChat:** 在200k token长度上效果优异，最长可支持可达320k，在长文本评估集上性能比肩专有模型。
|
| 66 |
+
- **Orion-14B-Chat-RAG:** 在一个定制的检索增强生成数据集上进行微调的聊天模型，在检索增强生成任务中取得了卓越的性能。
|
| 67 |
+
- **Orion-14B-Chat-Plugin:** 专门针对插件和函数调用任务定制的聊天模型，非常适用于使用代理的相关场景，其中大语言模型充当插件和函数调用系统。
|
| 68 |
+
- **Orion-14B-Base-Int4:** 一个使用int4进行量化的基座模型。它将模型大小显著减小了70%，同时提高了推理速度30%，仅引入了1%的最小性能损失。
|
| 69 |
+
- **Orion-14B-Chat-Int4:** 一个使用int4进行量化的对话模型。
|
| 70 |
+
|
| 71 |
+
|
| 72 |
+
<a name="zh_model-download"></a><br>
|
| 73 |
+
# 2. 下载路径
|
| 74 |
+
|
| 75 |
+
发布模型和下载链接见下表：
|
| 76 |
+
|
| 77 |
+
| 模型名称 | HuggingFace下载链接 | ModelScope下载链接 |
|
| 78 |
+
|---------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|
|
| 79 |
+
| ⚾ 基座模型 | [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) | [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) |
|
| 80 |
+
| 😛 对话模型 | [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) | [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) |
|
| 81 |
+
| 📃 长上下文模型 | [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) | [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) |
|
| 82 |
+
| 🔎 检索增强模型 | [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) | [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) |
|
| 83 |
+
| 🔌 插件模型 | [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) | [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary)|
|
| 84 |
+
| 💼 基座Int4量化模型 | [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) | [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) |
|
| 85 |
+
| 📦 对话Int4量化模型 | [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) | [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) |
|
| 86 |
+
|
| 87 |
+
|
| 88 |
+
<a name="zh_model-benchmark"></a><br>
|
| 89 |
+
# 3. 评估结果
|
| 90 |
+
|
| 91 |
+
## 3.1. 基座模型Orion-14B-Base评估
|
| 92 |
+
|
| 93 |
+
### 3.1.1. 专业知识与试题评估结果
|
| 94 |
+
| 模型名称 | C-Eval | CMMLU | MMLU | AGIEval | Gaokao | BBH |
|
| 95 |
+
|--------------------|----------|----------|----------|----------|----------|----------|
|
| 96 |
+
| LLaMA2-13B | 41.4 | 38.4 | 55.0 | 30.9 | 18.2 | 45.6 |
|
| 97 |
+
| Skywork-13B | 59.1 | 61.4 | 62.7 | 43.6 | 56.1 | 48.3 |
|
| 98 |
+
| Baichuan2-13B | 59.0 | 61.3 | 59.5 | 37.4 | 45.6 | 49.0 |
|
| 99 |
+
| QWEN-14B | 71.7 | 70.2 | 67.9 | 51.9 | **62.5** | 53.7 |
|
| 100 |
+
| InternLM-20B | 58.8 | 59.0 | 62.1 | 44.6 | 45.5 | 52.5 |
|
| 101 |
+
| **Orion-14B-Base** | **72.9** | **70.6** | **69.9** | **54.7** | 62.1 | **56.5** |
|
| 102 |
+
|
| 103 |
+
### 3.1.2. 理解与通识评估结果
|
| 104 |
+
| 模型名称 |RACE-middle|RACE-high| HellaSwag| PIQA | Lambada | WSC |
|
| 105 |
+
|--------------------|----------|----------|----------|----------|----------|----------|
|
| 106 |
+
| LLaMA 2-13B | 63.0 | 58.9 | 77.5 | 79.8 | 76.5 | 66.3 |
|
| 107 |
+
| Skywork-13B | 87.6 | 84.1 | 73.7 | 78.3 | 71.8 | 66.3 |
|
| 108 |
+
| Baichuan 2-13B | 68.9 | 67.2 | 70.8 | 78.1 | 74.1 | 66.3 |
|
| 109 |
+
| QWEN-14B | 93.0 | 90.3 | **80.2** | 79.8 | 71.4 | 66.3 |
|
| 110 |
+
| InternLM-20B | 86.4 | 83.3 | 78.1 | **80.3** | 71.8 | 68.3 |
|
| 111 |
+
| **Orion-14B-Base** | **93.2** | **91.3** | 78.5 | 79.5 | **78.8** | **70.2** |
|
| 112 |
+
|
| 113 |
+
### 3.1.3. OpenCompass评测集评估结果
|
| 114 |
+
| 模型名称 | Average | Examination | Language | Knowledge | Understanding | Reasoning |
|
| 115 |
+
|------------------|----------|----------|----------|----------|----------|----------|
|
| 116 |
+
| LLaMA 2-13B | 47.3 | 45.2 | 47.0 | 58.3 | 50.9 | 43.6 |
|
| 117 |
+
| Skywork-13B | 53.6 | 61.1 | 51.3 | 52.7 | 64.5 | 45.2 |
|
| 118 |
+
| Baichuan 2-13B | 49.4 | 51.8 | 47.5 | 48.9 | 58.1 | 44.2 |
|
| 119 |
+
| QWEN-14B | 62.4 | 71.3 | 52.67 | 56.1 | 68.8 | 60.1 |
|
| 120 |
+
| InternLM-20B | 59.4 | 62.5 | 55.0 | **60.1** | 67.3 | 54.9 |
|
| 121 |
+
|**Orion-14B-Base**| **64.3** | **71.4** | **55.0** | 60.0 | **71.9** | **61.6** |
|
| 122 |
+
|
| 123 |
+
### 3.1.4. 日语测试集评估结果
|
| 124 |
+
| 模型名称 |**Average**| JCQA | JNLI | MARC | JSQD | JQK | XLS | XWN | MGSM |
|
| 125 |
+
|--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
|
| 126 |
+
| PLaMo-13B | 52.3 | 56.7 | 42.8 | 95.8 | 70.6 | 71.0 | 8.70 | 70.5 | 2.40 |
|
| 127 |
+
| WebLab-10B | 50.7 | 66.6 | 53.7 | 82.1 | 62.9 | 56.2 | 10.0 | 72.0 | 2.40 |
|
| 128 |
+
| ELYZA-jp-7B | 48.8 | 71.7 | 25.3 | 86.6 | 70.8 | 64.1 | 2.50 | 62.1 | 7.20 |
|
| 129 |
+
| StableLM-jp-7B | 51.1 | 33.4 | 43.3 | **96.7** | 70.6 | 78.1 | 10.7 | 72.8 | 2.80 |
|
| 130 |
+
| LLaMA 2-13B | 46.3 | 75.0 | 47.6 | 38.8 | 76.1 | 67.7 | 18.1 | 63.2 | 10.4 |
|
| 131 |
+
| Baichuan 2-13B | 57.1 | 73.7 | 31.3 | 91.6 | 80.5 | 63.3 | 18.6 | 72.2 | 25.2 |
|
| 132 |
+
| QWEN-14B | 65.8 | 85.9 | 60.7 | 97.0 | 83.3 | 71.8 | 18.8 | 70.6 | 38.0 |
|
| 133 |
+
| Yi-34B | 67.1 | 83.8 | 61.2 | 95.2 | **86.1** | 78.5 | **27.2** | 69.2 | 35.2 |
|
| 134 |
+
| **Orion-14B-Base** | **69.1** | **88.2** | **75.8** | 94.1 | 75.7 | **85.1** | 17.3 | **78.8** | **38.0** |
|
| 135 |
+
|
| 136 |
+
### 3.1.5. 韩语测试集n-shot评估结果
|
| 137 |
+
| 模型名称 | **Average**<br>n=0 n=5 | HellaSwag<br>n=0 n=5 | COPA<br> n=0 n=5 | BooIQ<br>n=0 n=5 | SentiNeg<br>n=0 n=5|
|
| 138 |
+
|------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
|
| 139 |
+
| KoGPT | 53.0 70.1 | 55.9 58.3 | 73.5 72.9 | 45.1 59.8 | 37.5 89.4 |
|
| 140 |
+
| Polyglot-ko-13B | 69.6 73.7 |**59.5** **63.1**|**79.4** **81.1**| 48.2 60.4 | 91.2 90.2 |
|
| 141 |
+
| LLaMA 2-13B | 46.7 63.7 | 41.3 44.0 | 59.3 63.8 | 34.9 73.8 | 51.5 73.4 |
|
| 142 |
+
| Baichuan 2-13B | 52.1 58.7 | 39.2 39.6 | 60.6 60.6 | 58.4 61.5 | 50.3 72.9 |
|
| 143 |
+
| QWEN-14B | 53.8 73.7 | 45.3 46.8 | 64.9 68.9 | 33.4 83.5 | 71.5 95.7 |
|
| 144 |
+
| Yi-34B | 54.2 72.1 | 44.6 44.7 | 58.0 60.6 | 65.9 90.2 | 48.3 92.9 |
|
| 145 |
+
|**Orion-14B-Base**|**74.5** **79.6**| 47.0 49.6 | 77.7 79.4 |**81.6** **90.7**|**92.4** **98.7**|
|
| 146 |
+
|
| 147 |
+
### 3.1.6. 多语言评估结果
|
| 148 |
+
| 模型名称 | Train Lang | Japanese | Korean | Chinese | English |
|
| 149 |
+
|--------------------|------------|----------|----------|----------|----------|
|
| 150 |
+
| PLaMo-13B | En,Jp | 52.3 | * | * | * |
|
| 151 |
+
| Weblab-10B | En,Jp | 50.7 | * | * | * |
|
| 152 |
+
| ELYZA-jp-7B | En,Jp | 48.8 | * | * | * |
|
| 153 |
+
| StableLM-jp-7B | En,Jp | 51.1 | * | * | * |
|
| 154 |
+
| KoGPT-6B | En,Ko | * | 70.1 | * | * |
|
| 155 |
+
| Polyglot-ko-13B | En,Ko | * | 70.7 | * | * |
|
| 156 |
+
| Baichuan2-13B | Multi | 57.1 | 58.7 | 50.8 | 57.1 |
|
| 157 |
+
| Qwen-14B | Multi | 65.8 | 73.7 | 64.5 | 65.4 |
|
| 158 |
+
| Llama2-13B | Multi | 46.3 | 63.7 | 41.4 | 55.3 |
|
| 159 |
+
| Yi-34B | Multi | 67.1 | 72.2 | 58.7 | **68.8** |
|
| 160 |
+
| **Orion-14B-Base** | Multi | **69.1** | **79.5** | **67.9** | 67.3 |
|
| 161 |
+
|
| 162 |
+
## 3.2. 对话模型Orion-14B-Chat评估
|
| 163 |
+
### 3.2.1. 对话模型MTBench主观评估
|
| 164 |
+
| 模型名称 | 第一轮 | 第二轮 | **平均** |
|
| 165 |
+
|----------------------|----------|----------|----------|
|
| 166 |
+
| Baichuan2-13B-Chat | 7.05 | 6.47 | 6.76 |
|
| 167 |
+
| Qwen-14B-Chat | 7.30 | 6.62 | 6.96 |
|
| 168 |
+
| Llama2-13B-Chat | 7.10 | 6.20 | 6.65 |
|
| 169 |
+
| InternLM-20B-Chat | 7.03 | 5.93 | 6.48 |
|
| 170 |
+
| **Orion-14B-Chat** | **7.68** | **7.07** | **7.37** |
|
| 171 |
+
|
| 172 |
+
\*这里评测使用vllm进行推理
|
| 173 |
+
|
| 174 |
+
### 3.2.2. 对话模型AlignBench主观评估
|
| 175 |
+
| 模型名称 | 数学能力 | 逻辑推理 | 基本能力 | 中文理解 | 综合问答 | 写作能力 | 角色扮演 | 专业知识 | **平均** |
|
| 176 |
+
|--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
|
| 177 |
+
| Baichuan2-13B-Chat | 3.76 | 4.07 | 6.22 | 6.05 | 7.11 | 6.97 | 6.75 | 6.43 | 5.25 |
|
| 178 |
+
| Qwen-14B-Chat | **4.91** | **4.71** | **6.90** | 6.36 | 6.74 | 6.64 | 6.59 | 6.56 | **5.72** |
|
| 179 |
+
| Llama2-13B-Chat | 3.05 | 3.79 | 5.43 | 4.40 | 6.76 | 6.63 | 6.99 | 5.65 | 4.70 |
|
| 180 |
+
| InternLM-20B-Chat | 3.39 | 3.92 | 5.96 | 5.50 | **7.18** | 6.19 | 6.49 | 6.22 | 4.96 |
|
| 181 |
+
| **Orion-14B-Chat** | 4.00 | 4.24 | 6.18 | **6.57** | 7.16 | **7.36** | **7.16** | **6.99** | 5.51 |
|
| 182 |
+
|
| 183 |
+
\*这里评测使用vllm进行推理
|
| 184 |
+
|
| 185 |
+
## 3.3. 长上下文模型Orion-14B-LongChat评估
|
| 186 |
+
### 3.3.1. 长上下文模型LongBench评估
|
| 187 |
+
| 模型名称 | NarrativeQA| MultiFieldQA-en| MultiFieldQA-zh | DuReader | QMSum | VCSUM | TREC | TriviaQA | LSHT | RepoBench-P |
|
| 188 |
+
|--------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
|
| 189 |
+
| GPT-3.5-Turbo-16k | **23.60** | **52.30** | **61.20** | 28.70 | 23.40 | **16.00** | 68.00 | **91.40** | 29.20 | 53.60 |
|
| 190 |
+
| LongChat-v1.5-7B-32k | 16.90 | 41.40 | 29.10 | 19.50 | 22.70 | 9.90 | 63.50 | 82.30 | 23.20 | 55.30 |
|
| 191 |
+
| Vicuna-v1.5-7B-16k | 19.40 | 38.50 | 43.00 | 19.30 | 22.80 | 15.10 | 71.50 | 86.20 | 28.80 | 43.50 |
|
| 192 |
+
| Yi-6B-200K | 14.11 | 36.74 | 22.68 | 14.01 | 20.44 | 8.08 | 72.00 | 86.61 | 38.00 | **63.29** |
|
| 193 |
+
| Orion-14B-LongChat | 19.47 | 48.11 | 55.84 | **37.02** | **24.87** | 15.44 | **77.00** | 89.12 | **45.50** | 54.31 |
|
| 194 |
+
|
| 195 |
+
## 3.4. 检索增强模型Orion-14B-Chat-RAG评估
|
| 196 |
+
### 3.4.1. 自建检索增强测试集评估结果
|
| 197 |
+
|模型名称|回复效果(关键字)|*回复效果(主观打分)|引用能力|兜底能力|*AutoQA|*抽取数据|
|
| 198 |
+
|---------------------|------|------|------|------|------|------|
|
| 199 |
+
| Baichuan2-13B-Chat | 85 | 76 | 1 | 0 | 69 | 51 |
|
| 200 |
+
| Qwen-14B-Chat | 79 | 77 | 75 | 47 | 68 | 72 |
|
| 201 |
+
| Qwen-72B-Chat(Int4) | 87 | 89 | 90 | 32 | 67 | 76 |
|
| 202 |
+
| GPT-4 | 91 | 94 | 96 | 95 | 75 | 86 |
|
| 203 |
+
| Orion-14B-Chat-RAG | 86 | 87 | 91 | 97 | 73 | 71 |
|
| 204 |
+
\* 表示人工评判结果
|
| 205 |
+
|
| 206 |
+
## 3.5. 插件模型Orion-14B-Chat-Plugin评估
|
| 207 |
+
### 3.5.1. 自建插件测试集评估结果
|
| 208 |
+
| 模型名称 | 全参数意图识别 | 缺参数意图识别 | 非插件调用识别 |
|
| 209 |
+
|-----------------------|--------|-----------|--------|
|
| 210 |
+
| Baichuan2-13B-Chat | 25 | 0 | 0 |
|
| 211 |
+
| Qwen-14B-Chat | 55 | 0 | 50 |
|
| 212 |
+
| GPT-4 | **95** | 52.38 | 70 |
|
| 213 |
+
| Orion-14B-Chat-Plugin | 92.5 | **60.32** | **90** |
|
| 214 |
+
|
| 215 |
+
## 3.6. 量化模型Orion-14B-Base-Int4评估
|
| 216 |
+
### 3.6.1. 量化前后整体对比
|
| 217 |
+
|模型名称|模型大小(GB)|推理速度(令牌数/秒)|C-Eval |CMMLU |MMLU |RACE | HellaSwag|
|
| 218 |
+
|-------------------------|------|-----|------|------|------|------|------|
|
| 219 |
+
| OrionStar-14B-Base | 28.0 | 135 | 72.8 | 70.6 | 70.0 | 93.3 | 78.5 |
|
| 220 |
+
| OrionStar-14B-Base-Int4 | 8.3 | 178 | 71.8 | 69.8 | 69.2 | 93.1 | 78.0 |
|
| 221 |
+
|
| 222 |
+
|
| 223 |
+
<a name="zh_model-inference"></a><br>
|
| 224 |
+
# 4. 模型推理
|
| 225 |
+
|
| 226 |
+
推理所需的模型权重、源码、配置已发布在 Hugging Face，下载链接见本文档最开始的表格。我们在此示范多种推理方式。程序会自动从
|
| 227 |
+
Hugging Face 下载所需资源。
|
| 228 |
+
|
| 229 |
+
## 4.1. Python 代码方式
|
| 230 |
+
|
| 231 |
+
```python
|
| 232 |
+
import torch
|
| 233 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 234 |
+
from transformers.generation.utils import GenerationConfig
|
| 235 |
+
|
| 236 |
+
tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True)
|
| 237 |
+
model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto",
|
| 238 |
+
torch_dtype=torch.bfloat16, trust_remote_code=True)
|
| 239 |
+
|
| 240 |
+
model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B")
|
| 241 |
+
messages = [{"role": "user", "content": "你好! 你叫什么名字!"}]
|
| 242 |
+
response = model.chat(tokenizer, messages, streaming=False)
|
| 243 |
+
print(response)
|
| 244 |
+
|
| 245 |
+
```
|
| 246 |
+
|
| 247 |
+
在上述两段代码中，模型加载指定 `device_map='auto'`
|
| 248 |
+
，会使用所有可用显卡。如需指定使用的设备，可以使用类似 `export CUDA_VISIBLE_DEVICES=0,1`（使用了0、1号显卡）的方式控制。
|
| 249 |
+
|
| 250 |
+
## 4.2. 命令行工具方式
|
| 251 |
+
|
| 252 |
+
```shell
|
| 253 |
+
CUDA_VISIBLE_DEVICES=0 python cli_demo.py
|
| 254 |
+
```
|
| 255 |
+
|
| 256 |
+
本命令行工具是为 Chat 场景设计，因此我们不支持使用该工具调用 Base 模型。
|
| 257 |
+
|
| 258 |
+
## 4.3. 脚本直接推理
|
| 259 |
+
|
| 260 |
+
```shell
|
| 261 |
+
# base model
|
| 262 |
+
CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt 你好,你叫什么名字
|
| 263 |
+
|
| 264 |
+
# chat model
|
| 265 |
+
CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt 你好,你叫什么名字
|
| 266 |
+
|
| 267 |
+
```
|
| 268 |
+
|
| 269 |
+
<a name="vllm"></a><br>
|
| 270 |
+
## 4.4. 使用vllm推理
|
| 271 |
+
- 工程地址<br>
|
| 272 |
+
https://github.com/vllm-project/vllm
|
| 273 |
+
|
| 274 |
+
- 拉取请求<br>
|
| 275 |
+
https://github.com/vllm-project/vllm/pull/2539
|
| 276 |
+
|
| 277 |
+
<a name="llama-cpp"></a><br>
|
| 278 |
+
## 4.5. 使用llama.cpp推理
|
| 279 |
+
|
| 280 |
+
- 工程地址<br>
|
| 281 |
+
https://github.com/ggerganov/llama.cpp
|
| 282 |
+
|
| 283 |
+
- 拉取请求<br>
|
| 284 |
+
https://github.com/ggerganov/llama.cpp/pull/5118
|
| 285 |
+
|
| 286 |
+
- 如何转换GGUF格式模型
|
| 287 |
+
|
| 288 |
+
```shell
|
| 289 |
+
python convert-hf-to-gguf.py path/to/Orion-14B-Chat --outfile chat.gguf
|
| 290 |
+
```
|
| 291 |
+
|
| 292 |
+
- 如何运行GGUF格式模型推理
|
| 293 |
+
|
| 294 |
+
```shell
|
| 295 |
+
./main --frequency-penalty 0.5 --frequency-penalty 0.5 --top-k 5 --top-p 0.9 -m chat.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
|
| 296 |
+
```
|
| 297 |
+
|
| 298 |
+
|
| 299 |
+
|
| 300 |
+
|
| 301 |
+
## 4.6. 示例输出
|
| 302 |
+
|
| 303 |
+
### 4.6.1. 闲聊
|
| 304 |
+
|
| 305 |
+
`````
|
| 306 |
+
用户：你好,你叫什么名字
|
| 307 |
+
Orion-14B：你好!我是一个人工智能助手,没有固定的名字。你可以随意称呼我。有什么我可以帮助你的吗?
|
| 308 |
+
用户：讲个故事
|
| 309 |
+
Orion-14B：从前有一个小男孩叫杰克,他住在一个小村庄里。杰克非常喜欢冒险和探索,总是梦想着能够发现一些神奇的事情。
|
| 310 |
+
|
| 311 |
+
有一天,杰克听说在附近的森林深处有一座传说中的魔法城堡,里面住着一个邪恶的巨人。这个巨人的名字叫格里姆,他被困在城堡中,无法离开。
|
| 312 |
+
|
| 313 |
+
杰克决定要去解救这个巨人,因为他相信格里姆一定有自己的原因被困在那里。于是,他带着勇气和决心,踏上了前往魔法城堡的旅程。
|
| 314 |
+
|
| 315 |
+
经过长时间的跋涉,杰克终��来到了城堡的大门前。他小心翼翼地推开大门,进入了一个充满神秘气息的房间。
|
| 316 |
+
|
| 317 |
+
房间里弥漫着黑暗的气息,但杰克并没有被吓倒。他勇敢地向城堡深处走去,最终找到了格里姆。
|
| 318 |
+
|
| 319 |
+
格里姆告诉杰克,他是被一个邪恶的女巫诅咒的,只有通过解开咒语才能获得自由。杰克决定要帮助格里姆,他开始寻找线索和解开咒语的方法。
|
| 320 |
+
|
| 321 |
+
经过一番努力,杰克终于找到了一条通往女巫的秘密实验室的道路。他在那里发现了女巫留下的魔法书,书中记载了解开咒语的方法。
|
| 322 |
+
|
| 323 |
+
杰克按照书上所写的步骤进行操作,成功地解开了格里姆身上的诅咒。巨人感激不已,他答应帮助杰克实现他的愿望。
|
| 324 |
+
|
| 325 |
+
从此以后,杰克和格里姆成为了好朋友,他们一起经历了许多惊险刺激的冒险,并用他们的勇气和智慧战胜了各种困难。
|
| 326 |
+
|
| 327 |
+
这个故事告诉我们,只要有勇气和决心,我们就能克服一切困难,实现自己的梦想。
|
| 328 |
+
`````
|
| 329 |
+
|
| 330 |
+
### 4.6.2. 日韩
|
| 331 |
+
|
| 332 |
+
`````
|
| 333 |
+
用户：自己を紹介してください
|
| 334 |
+
Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。
|
| 335 |
+
用户：자기소개를 해주세요.
|
| 336 |
+
Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다.
|
| 337 |
+
`````
|
| 338 |
+
|
| 339 |
+
|
| 340 |
+
<a name="zh_declarations-license"></a><br>
|
| 341 |
+
# 5. 声明、协议
|
| 342 |
+
|
| 343 |
+
## 5.1. 声明
|
| 344 |
+
|
| 345 |
+
我们强烈呼吁所有使用者，不要利用 Orion-14B 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将
|
| 346 |
+
Orion-14B 模型用于未经适当安全审查和备案的互联网服务。
|
| 347 |
+
|
| 348 |
+
我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。
|
| 349 |
+
我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用
|
| 350 |
+
Orion-14B 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。
|
| 351 |
+
|
| 352 |
+
## 5.2. 协议
|
| 353 |
+
|
| 354 |
+
社区使用Orion-14B系列模型
|
| 355 |
+
- 代码请遵循 [Apache License Version 2.0](./LICENSE)<br>
|
| 356 |
+
- 模型请遵循 [Orion-14B系列模型社区许可协议](./ModelsCommunityLicenseAgreement)
|
| 357 |
+
|
| 358 |
+
|
| 359 |
+
<a name="zh_company-introduction"></a><br>
|
| 360 |
+
# 6. 企业介绍
|
| 361 |
+
|
| 362 |
+
猎户星空（OrionStar）是一家全球领先的服务机器人解决方案公司，成立于2016年9月。猎户星空致力于基于人工智能技术打造下一代革命性机器人，使人们能够摆脱重复的体力劳动，使人类的工作和生活更加智能和有趣，通过技术使社会和世界变得更加美好。
|
| 363 |
+
|
| 364 |
+
猎户星空拥有完全自主开发的全链条人工智能技术，如语音交互和视觉导航。它整合了产品开发能力和技术应用能力。基于Orion机械臂平台，它推出了ORION
|
| 365 |
+
STAR AI Robot Greeting、AI Robot Greeting Mini、Lucki、Coffee
|
| 366 |
+
Master等产品，并建立了Orion机器人的开放平台OrionOS。通过为 **真正有用的机器人而生** 的理念实践，它通过AI技术为更多人赋能。
|
| 367 |
+
|
| 368 |
+
凭借7年AI经验积累，猎户星空已推出的大模型深度应用“聚言”，并陆续面向行业客户提供定制化AI大模型咨询与服务解决方案，真正帮助客户实现企业经营效率领先同行目标。
|
| 369 |
+
|
| 370 |
+
**猎户星空具备全链条大模型应用能力的核心优势**，包括拥有从海量数据处理、大模型预训练、二次预训练、微调(Fine-tune)、Prompt
|
| 371 |
+
Engineering 、Agent开发的全链条能力和经验积累；拥有完整的端到端模型训练能力，包括系统化的数据处理流程和数百张GPU的并行模型训练能力，现已在大政务、云服务、出海电商、快消等多个行业场景落地。
|
| 372 |
+
|
| 373 |
+
***欢迎有大模型应用落地需求的企业联系我们进行商务合作***<br>
|
| 374 |
+
**咨询电话:** 400-898-7779<br>
|
| 375 |
+
**电子邮箱:** ai@orionstar.com<br>
|
| 376 |
+
**Discord社区链接:** https://discord.gg/zumjDWgdAs
|
| 377 |
+
|
| 378 |
+
<div align="center">
|
| 379 |
+
<img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" />
|
| 380 |
+
</div>
|
| 381 |
+
|
| 382 |
+
|
| 383 |
+
|
| 384 |
+
|
| 385 |
+
# Table of Contents
|
| 386 |
+
|
| 387 |
+
- [📖 Model Introduction](#model-introduction)
|
| 388 |
+
- [🔗 Model Download](#model-download)
|
| 389 |
+
- [🔖 Model Benchmark](#model-benchmark)
|
| 390 |
+
- [📊 Model Inference](#model-inference)
|
| 391 |
+
- [📜 Declarations & License](#declarations-license)
|
| 392 |
+
- [🥇 Company Introduction](#company-introduction)
|
| 393 |
+
|
| 394 |
+
<a name="model-introduction"></a><br>
|
| 395 |
+
# 1. Model Introduction
|
| 396 |
+
|
| 397 |
+
- Orion-14B series models are open-source multilingual large language models trained from scratch by OrionStarAI. The base model is trained on 2.5T multilingual corpus, including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages. For details, please refer to [tech report](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf).
|
| 398 |
+
|
| 399 |
+
- The Orion-14B series models exhibit the following features:
|
| 400 |
+
- Among models with 20B-parameter scale level, Orion-14B-Base model shows outstanding performance in comprehensive evaluations.
|
| 401 |
+
- Strong multilingual capabilities, significantly outperforming in Japanese and Korean testsets.
|
| 402 |
+
- The fine-tuned models demonstrate strong adaptability, excelling in human-annotated blind tests.
|
| 403 |
+
- The long-chat version supports extremely long texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k.
|
| 404 |
+
- The quantized versions reduce model size by 70%, improve inference speed by 30%, with performance loss less than 1%.
|
| 405 |
+
<table style="border-collapse: collapse; width: 100%;">
|
| 406 |
+
<tr>
|
| 407 |
+
<td style="border: none; padding: 10px; box-sizing: border-box;">
|
| 408 |
+
<img src="./assets/imgs/opencompass_en.png" alt="opencompass" style="width: 100%; height: auto;">
|
| 409 |
+
</td>
|
| 410 |
+
<td style="border: none; padding: 10px; box-sizing: border-box;">
|
| 411 |
+
<img src="./assets/imgs/model_cap_en.png" alt="modelcap" style="width: 100%; height: auto;">
|
| 412 |
+
</td>
|
| 413 |
+
</tr>
|
| 414 |
+
</table>
|
| 415 |
+
|
| 416 |
+
- Orion-14B series models including:
|
| 417 |
+
- **Orion-14B-Base:** A multilingual large language foundational model with 14 billion parameters, pretrained on a diverse dataset of 2.5 trillion tokens.
|
| 418 |
+
- **Orion-14B-Chat:** A chat-model fine-tuned on a high-quality corpus aims to provide an excellence interactive experience for users in the large model community.
|
| 419 |
+
- **Orion-14B-LongChat:** The long-context version excels at handling extremely lengthy texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k.
|
| 420 |
+
- **Orion-14B-Chat-RAG:** A chat-model fine-tuned on a custom retrieval augmented generation dataset, achieving superior performance in retrieval augmented generation tasks.
|
| 421 |
+
- **Orion-14B-Chat-Plugin:** A chat-model specifically tailored for plugin and function calling tasks, ideal for agent-related scenarios where the LLM acts as a plugin and function call system.
|
| 422 |
+
- **Orion-14B-Base-Int4:** A quantized base model utilizing 4-bit integer weights. It significantly reduces the model size by 70% and increases the inference speed by 30% while incurring a minimal performance loss of only 1%.
|
| 423 |
+
- **Orion-14B-Chat-Int4:** A quantized chat model utilizing 4-bit integer weights.
|
| 424 |
+
|
| 425 |
+
|
| 426 |
+
<a name="model-download"></a><br>
|
| 427 |
+
# 2. Model Download
|
| 428 |
+
|
| 429 |
+
Model release and download links are provided in the table below:
|
| 430 |
+
|
| 431 |
+
| Model Name | HuggingFace Download Links | ModelScope Download Links |
|
| 432 |
+
|-------------------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
|
| 433 |
+
| ⚾Orion-14B-Base | [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) | [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) |
|
| 434 |
+
| 😛Orion-14B-Chat | [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) | [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) |
|
| 435 |
+
| 📃Orion-14B-LongChat | [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) | [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) |
|
| 436 |
+
| 🔎Orion-14B-Chat-RAG | [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) | [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) |
|
| 437 |
+
| 🔌Orion-14B-Chat-Plugin | [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) | [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary) |
|
| 438 |
+
| 💼Orion-14B-Base-Int4 | [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) | [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) |
|
| 439 |
+
| 📦Orion-14B-Chat-Int4 | [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) | [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) |
|
| 440 |
+
|
| 441 |
+
<a name="model-benchmark"></a><br>
|
| 442 |
+
# 3. Model Benchmarks
|
| 443 |
+
|
| 444 |
+
## 3.1. Base Model Orion-14B-Base Benchmarks
|
| 445 |
+
### 3.1.1. LLM evaluation results on examination and professional knowledge
|
| 446 |
+
| Model | C-Eval | CMMLU | MMLU | AGIEval | Gaokao | BBH |
|
| 447 |
+
|--------------------|----------|----------|----------|----------|----------|----------|
|
| 448 |
+
| LLaMA2-13B | 41.4 | 38.4 | 55.0 | 30.9 | 18.2 | 45.6 |
|
| 449 |
+
| Skywork-13B | 59.1 | 61.4 | 62.7 | 43.6 | 56.1 | 48.3 |
|
| 450 |
+
| Baichuan2-13B | 59.0 | 61.3 | 59.5 | 37.4 | 45.6 | 49.0 |
|
| 451 |
+
| QWEN-14B | 71.7 | 70.2 | 67.9 | 51.9 | **62.5** | 53.7 |
|
| 452 |
+
| InternLM-20B | 58.8 | 59.0 | 62.1 | 44.6 | 45.5 | 52.5 |
|
| 453 |
+
| **Orion-14B-Base** | **72.9** | **70.6** | **69.9** | **54.7** | 62.1 | **56.5** |
|
| 454 |
+
|
| 455 |
+
### 3.1.2. LLM evaluation results on language understanding and common knowledge
|
| 456 |
+
| Model |RACE-middle|RACE-high |HellaSwag | PIQA | Lambada | WSC |
|
| 457 |
+
|--------------------|----------|----------|----------|----------|----------|----------|
|
| 458 |
+
| LLaMA 2-13B | 63.0 | 58.9 | 77.5 | 79.8 | 76.5 | 66.3 |
|
| 459 |
+
| Skywork-13B | 87.6 | 84.1 | 73.7 | 78.3 | 71.8 | 66.3 |
|
| 460 |
+
| Baichuan 2-13B | 68.9 | 67.2 | 70.8 | 78.1 | 74.1 | 66.3 |
|
| 461 |
+
| QWEN-14B | 93.0 | 90.3 | **80.2** | 79.8 | 71.4 | 66.3 |
|
| 462 |
+
| InternLM-20B | 86.4 | 83.3 | 78.1 | **80.3** | 71.8 | 68.3 |
|
| 463 |
+
| **Orion-14B-Base** | **93.2** | **91.3** | 78.5 | 79.5 | **78.8** | **70.2** |
|
| 464 |
+
|
| 465 |
+
### 3.1.3. LLM evaluation results of OpenCompass testsets
|
| 466 |
+
| Model | Average | Examination | Language | Knowledge | Understanding | Reasoning |
|
| 467 |
+
|------------------|----------|----------|----------|----------|----------|----------|
|
| 468 |
+
| LLaMA 2-13B | 47.3 | 45.2 | 47.0 | 58.3 | 50.9 | 43.6 |
|
| 469 |
+
| Skywork-13B | 53.6 | 61.1 | 51.3 | 52.7 | 64.5 | 45.2 |
|
| 470 |
+
| Baichuan 2-13B | 49.4 | 51.8 | 47.5 | 48.9 | 58.1 | 44.2 |
|
| 471 |
+
| QWEN-14B | 62.4 | 71.3 | 52.67 | 56.1 | 68.8 | 60.1 |
|
| 472 |
+
| InternLM-20B | 59.4 | 62.5 | 55.0 | **60.1** | 67.3 | 54.9 |
|
| 473 |
+
|**Orion-14B-Base**| **64.3** | **71.4** | **55.0** | 60.0 | **71.9** | **61.6** |
|
| 474 |
+
|
| 475 |
+
### 3.1.4. Comparison of LLM performances on Japanese testsets
|
| 476 |
+
| Model |**Average**| JCQA | JNLI | MARC | JSQD | JQK | XLS | XWN | MGSM |
|
| 477 |
+
|--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
|
| 478 |
+
| PLaMo-13B | 52.3 | 56.7 | 42.8 | 95.8 | 70.6 | 71.0 | 8.70 | 70.5 | 2.40 |
|
| 479 |
+
| WebLab-10B | 50.7 | 66.6 | 53.7 | 82.1 | 62.9 | 56.2 | 10.0 | 72.0 | 2.40 |
|
| 480 |
+
| ELYZA-jp-7B | 48.8 | 71.7 | 25.3 | 86.6 | 70.8 | 64.1 | 2.50 | 62.1 | 7.20 |
|
| 481 |
+
| StableLM-jp-7B | 51.1 | 33.4 | 43.3 | **96.7** | 70.6 | 78.1 | 10.7 | 72.8 | 2.80 |
|
| 482 |
+
| LLaMA 2-13B | 46.3 | 75.0 | 47.6 | 38.8 | 76.1 | 67.7 | 18.1 | 63.2 | 10.4 |
|
| 483 |
+
| Baichuan 2-13B | 57.1 | 73.7 | 31.3 | 91.6 | 80.5 | 63.3 | 18.6 | 72.2 | 25.2 |
|
| 484 |
+
| QWEN-14B | 65.8 | 85.9 | 60.7 | 97.0 | 83.3 | 71.8 | 18.8 | 70.6 | 38.0 |
|
| 485 |
+
| Yi-34B | 67.1 | 83.8 | 61.2 | 95.2 | **86.1** | 78.5 | **27.2** | 69.2 | 35.2 |
|
| 486 |
+
| **Orion-14B-Base** | **69.1** | **88.2** | **75.8** | 94.1 | 75.7 | **85.1** | 17.3 | **78.8** | **38.0** |
|
| 487 |
+
|
| 488 |
+
### 3.1.5. Comparison of LLM performances on Korean testsets. n = 0 and n = 5 stand for n-shot prompts used in the evaluation
|
| 489 |
+
|Model | **Average**<br>n=0 n=5 | HellaSwag<br>n=0 n=5 | COPA<br> n=0 n=5 | BooIQ<br>n=0 n=5 | SentiNeg<br>n=0 n=5|
|
| 490 |
+
|------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
|
| 491 |
+
| KoGPT | 53.0 70.1 | 55.9 58.3 | 73.5 72.9 | 45.1 59.8 | 37.5 89.4 |
|
| 492 |
+
| Polyglot-ko-13B | 69.6 73.7 |**59.5** **63.1**|**79.4** **81.1**| 48.2 60.4 | 91.2 90.2 |
|
| 493 |
+
| LLaMA 2-13B | 46.7 63.7 | 41.3 44.0 | 59.3 63.8 | 34.9 73.8 | 51.5 73.4 |
|
| 494 |
+
| Baichuan 2-13B | 52.1 58.7 | 39.2 39.6 | 60.6 60.6 | 58.4 61.5 | 50.3 72.9 |
|
| 495 |
+
| QWEN-14B | 53.8 73.7 | 45.3 46.8 | 64.9 68.9 | 33.4 83.5 | 71.5 95.7 |
|
| 496 |
+
| Yi-34B | 54.2 72.1 | 44.6 44.7 | 58.0 60.6 | 65.9 90.2 | 48.3 92.9 |
|
| 497 |
+
|**Orion-14B-Chat**|**74.5** **79.6**| 47.0 49.6 | 77.7 79.4 |**81.6** **90.7**|**92.4** **98.7**|
|
| 498 |
+
|
| 499 |
+
### 3.1.6. Multilingual evaluation
|
| 500 |
+
| Model | Train Lang | Japanese | Korean | Chinese | English |
|
| 501 |
+
|--------------------|------------|----------|----------|----------|----------|
|
| 502 |
+
| PLaMo-13B | En,Jp | 52.3 | * | * | * |
|
| 503 |
+
| Weblab-10B | En,Jp | 50.7 | * | * | * |
|
| 504 |
+
| ELYZA-jp-7B | En,Jp | 48.8 | * | * | * |
|
| 505 |
+
| StableLM-jp-7B | En,Jp | 51.1 | * | * | * |
|
| 506 |
+
| KoGPT-6B | En,Ko | * | 70.1 | * | * |
|
| 507 |
+
| Polyglot-ko-13B | En,Ko | * | 70.7 | * | * |
|
| 508 |
+
| Baichuan2-13B | Multi | 57.1 | 58.7 | 50.8 | 57.1 |
|
| 509 |
+
| Qwen-14B | Multi | 65.8 | 73.7 | 64.5 | 65.4 |
|
| 510 |
+
| Llama2-13B | Multi | 46.3 | 63.7 | 41.4 | 55.3 |
|
| 511 |
+
| Yi-34B | Multi | 67.1 | 72.2 | 58.7 | **68.8** |
|
| 512 |
+
| **Orion-14B-Chat** | Multi | **69.1** | **79.5** | **67.9** | 67.3 |
|
| 513 |
+
|
| 514 |
+
|
| 515 |
+
## 3.2. Chat Model Orion-14B-Chat Benchmarks
|
| 516 |
+
### 3.2.1. Chat model subjective evaluation of MTBench
|
| 517 |
+
| Model | First-Turn | Second-Turn | **Average** |
|
| 518 |
+
|----------------------|----------|----------|----------|
|
| 519 |
+
| Baichuan2-13B-Chat | 7.05 | 6.47 | 6.76 |
|
| 520 |
+
| Qwen-14B-Chat | 7.30 | 6.62 | 6.96 |
|
| 521 |
+
| Llama2-13B-Chat | 7.10 | 6.20 | 6.65 |
|
| 522 |
+
| InternLM-20B-Chat | 7.03 | 5.93 | 6.48 |
|
| 523 |
+
| **Orion-14B-Chat** | **7.68** | **7.07** | **7.37** |
|
| 524 |
+
\* use vllm for inference
|
| 525 |
+
|
| 526 |
+
### 3.2.2. Chat model subjective evaluation of AlignBench
|
| 527 |
+
| Model | Math. | Logi. | Basic. | Chi. | Comp. | Writ. | Role. | Prof. |**Avg.**|
|
| 528 |
+
|--------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|
|
| 529 |
+
| Baichuan2-13B-Chat | 3.76 | 4.07 | 6.22 | 6.05 | 7.11 | 6.97 | 6.75 | 6.43 | 5.25 |
|
| 530 |
+
| Qwen-14B-Chat |**4.91**|**4.71**|**6.90**| 6.36 | 6.74 | 6.64 | 6.59 | 6.56 |**5.72**|
|
| 531 |
+
| Llama2-13B-Chat | 3.05 | 3.79 | 5.43 | 4.40 | 6.76 | 6.63 | 6.99 | 5.65 | 4.70 |
|
| 532 |
+
| InternLM-20B-Chat | 3.39 | 3.92 | 5.96 | 5.50 |**7.18**| 6.19 | 6.49 | 6.22 | 4.96 |
|
| 533 |
+
| **Orion-14B-Chat** | 4.00 | 4.24 | 6.18 |**6.57**| 7.16 |**7.36**|**7.16**|**6.99**| 5.51 |
|
| 534 |
+
\* use vllm for inference
|
| 535 |
+
|
| 536 |
+
## 3.3. LongChat Model Orion-14B-LongChat Benchmarks
|
| 537 |
+
### 3.3.1. LongChat evaluation of LongBench
|
| 538 |
+
| Model | NarrativeQA|MultiFieldQA-en|MultiFieldQA-zh| DuReader | QMSum | VCSUM | TREC | TriviaQA | LSHT |RepoBench-P|
|
| 539 |
+
|--------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
|
| 540 |
+
| GPT-3.5-Turbo-16k | **23.60** | **52.30** | **61.20** | 28.70 | 23.40 | **16.00** | 68.00 | **91.40** | 29.20 | 53.60 |
|
| 541 |
+
| LongChat-v1.5-7B-32k | 16.90 | 41.40 | 29.10 | 19.50 | 22.70 | 9.90 | 63.50 | 82.30 | 23.20 | 55.30 |
|
| 542 |
+
| Vicuna-v1.5-7B-16k | 19.40 | 38.50 | 43.00 | 19.30 | 22.80 | 15.10 | 71.50 | 86.20 | 28.80 | 43.50 |
|
| 543 |
+
| Yi-6B-200K | 14.11 | 36.74 | 22.68 | 14.01 | 20.44 | 8.08 | 72.00 | 86.61 | 38.00 | **63.29** |
|
| 544 |
+
| Orion-14B-LongChat | 19.47 | 48.11 | 55.84 | **37.02** | **24.87** | 15.44 | **77.00** | 89.12 | **45.50** | 54.31 |
|
| 545 |
+
|
| 546 |
+
|
| 547 |
+
## 3.4. Chat RAG Model Benchmarks
|
| 548 |
+
### 3.4.1. LLM evaluation results of self-built RAG testsets
|
| 549 |
+
|Model|Effectiveness of Response(Keyword)|*Effectiveness of Response（subjective evaluation）|Quoting Ability|Fallback Ability|*AutoQA|*Data Extraction|
|
| 550 |
+
|---------------------|------|------|------|------|------|------|
|
| 551 |
+
| Baichuan2-13B-Chat | 85 | 76 | 1 | 0 | 69 | 51 |
|
| 552 |
+
| Qwen-14B-Chat | 79 | 77 | 75 | 47 | 68 | 72 |
|
| 553 |
+
| Qwen-72B-Chat(Int4) | 87 | 89 | 90 | 32 | 67 | 76 |
|
| 554 |
+
| GPT-4 | 91 | 94 | 96 | 95 | 75 | 86 |
|
| 555 |
+
| Orion-14B-Chat-RAG | 86 | 87 | 91 | 97 | 73 | 71 |
|
| 556 |
+
\* means manual assessment
|
| 557 |
+
|
| 558 |
+
## 3.5. Chat Plugin Model Orion-14B-Chat-Plugin Benchmarks
|
| 559 |
+
### 3.5.1. LLM evaluation results of self-built plugin testsets
|
| 560 |
+
|Model |Intent Recognition with Full Params |Intent Recognition with Missing Params |Non-Plugin Invocation Recognition |
|
| 561 |
+
|-----------------------|--------|-----------|--------|
|
| 562 |
+
| Baichuan2-13B-Chat | 25 | 0 | 0 |
|
| 563 |
+
| Qwen-14B-Chat | 55 | 0 | 50 |
|
| 564 |
+
| GPT-4 | **95** | 52.38 | 70 |
|
| 565 |
+
| Orion-14B-Chat-Plugin | 92.5 | **60.32** | **90** |
|
| 566 |
+
|
| 567 |
+
## 3.6. Quantized Model Orion-14B-Base-Int4 Benchmarks
|
| 568 |
+
### 3.6.1. Comparison of before and after quantization
|
| 569 |
+
|Model |Size(GB)|Inference Speed(tokens/s)|C-Eval|CMMLU|MMLU|RACE|HellaSwag|
|
| 570 |
+
|-------------------------|-------|-----|------|------|------|------|------|
|
| 571 |
+
| OrionStar-14B-Base | 28.0 | 135 | 72.8 | 70.6 | 70.0 | 93.3 | 78.5 |
|
| 572 |
+
| OrionStar-14B-Base-Int4 | 8.3 | 178 | 71.8 | 69.8 | 69.2 | 93.1 | 78.0 |
|
| 573 |
+
|
| 574 |
+
|
| 575 |
+
<a name="model-inference"></a><br>
|
| 576 |
+
# 4. Model Inference
|
| 577 |
+
|
| 578 |
+
Model weights, source code, and configuration needed for inference are published on Hugging Face, and the download link
|
| 579 |
+
is available in the table at the beginning of this document. We demonstrate various inference methods here, and the
|
| 580 |
+
program will automatically download the necessary resources from Hugging Face.
|
| 581 |
+
|
| 582 |
+
## 4.1. Python Code
|
| 583 |
+
|
| 584 |
+
```python
|
| 585 |
+
import torch
|
| 586 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 587 |
+
from transformers.generation.utils import GenerationConfig
|
| 588 |
+
|
| 589 |
+
tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True)
|
| 590 |
+
model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto",
|
| 591 |
+
torch_dtype=torch.bfloat16, trust_remote_code=True)
|
| 592 |
+
|
| 593 |
+
model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B")
|
| 594 |
+
messages = [{"role": "user", "content": "Hello, what is your name? "}]
|
| 595 |
+
response = model.chat(tokenizer, messages, streaming=False)
|
| 596 |
+
print(response)
|
| 597 |
+
|
| 598 |
+
```
|
| 599 |
+
|
| 600 |
+
In the above Python code, the model is loaded with `device_map='auto'` to utilize all available GPUs. To specify the
|
| 601 |
+
device, you can use something like `export CUDA_VISIBLE_DEVICES=0,1` (using GPUs 0 and 1).
|
| 602 |
+
|
| 603 |
+
## 4.2. Command Line Tool
|
| 604 |
+
|
| 605 |
+
```shell
|
| 606 |
+
CUDA_VISIBLE_DEVICES=0 python cli_demo.py
|
| 607 |
+
```
|
| 608 |
+
|
| 609 |
+
This command-line tool is designed for chat scenarios, and thus, it does not support calling the base model.
|
| 610 |
+
|
| 611 |
+
## 4.3. Direct Script Inference
|
| 612 |
+
|
| 613 |
+
```shell
|
| 614 |
+
|
| 615 |
+
# base model
|
| 616 |
+
CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt hello
|
| 617 |
+
|
| 618 |
+
# chat model
|
| 619 |
+
CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt hi
|
| 620 |
+
|
| 621 |
+
```
|
| 622 |
+
<a name="vllm"></a><br>
|
| 623 |
+
## 4.4. 使用vllm推理
|
| 624 |
+
- 工程地址<br>
|
| 625 |
+
https://github.com/vllm-project/vllm
|
| 626 |
+
|
| 627 |
+
- 拉取请求<br>
|
| 628 |
+
https://github.com/vllm-project/vllm/pull/2539
|
| 629 |
+
|
| 630 |
+
<a name="llama-cpp"></a><br>
|
| 631 |
+
## 4.5. 使用llama.cpp推理
|
| 632 |
+
|
| 633 |
+
- 工程地址<br>
|
| 634 |
+
https://github.com/ggerganov/llama.cpp
|
| 635 |
+
|
| 636 |
+
- 拉取请求<br>
|
| 637 |
+
https://github.com/ggerganov/llama.cpp/pull/5118
|
| 638 |
+
|
| 639 |
+
- 如何转换GGUF格式模型
|
| 640 |
+
|
| 641 |
+
```shell
|
| 642 |
+
python convert-hf-to-gguf.py path/to/Orion-14B-Chat --outfile chat.gguf
|
| 643 |
+
```
|
| 644 |
+
|
| 645 |
+
- 如何运行GGUF格式模型推理
|
| 646 |
+
|
| 647 |
+
```shell
|
| 648 |
+
./main --frequency-penalty 0.5 --frequency-penalty 0.5 --top-k 5 --top-p 0.9 -m chat.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
|
| 649 |
+
```
|
| 650 |
+
|
| 651 |
+
## 4.6 Example Output
|
| 652 |
+
|
| 653 |
+
### 4.6.1. Casual Chat
|
| 654 |
+
|
| 655 |
+
`````
|
| 656 |
+
User: Hello
|
| 657 |
+
Orion-14B: Hello! How can I help you today? If you have any questions or need assistance, feel free to ask.
|
| 658 |
+
User: What functions do you have?
|
| 659 |
+
Orion-14B: As an AI language model, I have a wide range of functions and capabilities, including but not limited to:
|
| 660 |
+
Answering general knowledge questions: I can provide information on various topics such as history, science, geography, and more.
|
| 661 |
+
Providing definitions and explanations: I can explain complex concepts and provide definitions for words and phrases.
|
| 662 |
+
Assisting with tasks: I can help with simple tasks like setting reminders, converting units of measurement, and even ordering food from restaurants.
|
| 663 |
+
Generating text: I can write short stories, essays, articles, and other types of content based on prompts given to me.
|
| 664 |
+
Translating languages: I can translate text from one language to another.
|
| 665 |
+
Chatting: I can engage in conversation with users and respond to their queries in a conversational manner.
|
| 666 |
+
Summarizing long texts: I can summarize longer texts like articles, books, and research papers.
|
| 667 |
+
Generating random numbers and names: I can generate random numbers within a specified range and also suggest names based on certain criteria.
|
| 668 |
+
Playing games: I can play simple games like Hangman, Trivia, and more.
|
| 669 |
+
Providing recommendations: I can recommend movies, music, books, and other forms of entertainment based on user preferences.
|
| 670 |
+
These are just some of the many functions that I am capable of performing. If you have a specific task in mind, feel free to ask me about it.
|
| 671 |
+
User: Tell me a joke.
|
| 672 |
+
Orion-14B: Sure, here's a classic one-liner: Why don't scientists trust atoms? Because they make up everything.
|
| 673 |
+
`````
|
| 674 |
+
|
| 675 |
+
### 4.6.2. Japanese & Korean Chat
|
| 676 |
+
|
| 677 |
+
`````
|
| 678 |
+
User：自己を紹介してください
|
| 679 |
+
Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得���るのを助けることができます。
|
| 680 |
+
User：자기소개를 해주세요.
|
| 681 |
+
Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다.
|
| 682 |
+
`````
|
| 683 |
+
|
| 684 |
+
<a name="declarations-license"></a><br>
|
| 685 |
+
# 5. Declarations, License
|
| 686 |
+
|
| 687 |
+
## 5.1. Declarations
|
| 688 |
+
|
| 689 |
+
We strongly urge all users not to use the Orion-14B model for any activities that may harm national or social security or violate the law.
|
| 690 |
+
Additionally, we request users not to use the Orion-14B model for internet services without proper security review and filing.
|
| 691 |
+
We hope all users abide by this principle to ensure that technological development takes place in a regulated and legal environment.
|
| 692 |
+
We have done our best to ensure the compliance of the data used in the model training process. However, despite our
|
| 693 |
+
significant efforts, unforeseen issues may still arise due to the complexity of the model and data. Therefore, if any
|
| 694 |
+
problems arise due to the use of the Orion-14B open-source model, including but not limited to data security
|
| 695 |
+
issues, public opinion risks, or any risks and issues arising from the model being misled, abused, disseminated, or
|
| 696 |
+
improperly utilized, we will not assume any responsibility.
|
| 697 |
+
|
| 698 |
+
## 5.2. License
|
| 699 |
+
|
| 700 |
+
Community use of the Orion-14B series models
|
| 701 |
+
- For code, please comply with [Apache License Version 2.0](./LICENSE)<br>
|
| 702 |
+
- For model, please comply with [【Orion-14B Series】 Models Community License Agreement](./ModelsCommunityLicenseAgreement)
|
| 703 |
+
|
| 704 |
+
|
| 705 |
+
<a name="company-introduction"></a><br>
|
| 706 |
+
# 6. Company Introduction
|
| 707 |
+
|
| 708 |
+
OrionStar is a leading global service robot solutions company, founded in September 2016. OrionStar is dedicated to
|
| 709 |
+
using artificial intelligence technology to create the next generation of revolutionary robots, allowing people to break
|
| 710 |
+
free from repetitive physical labor and making human work and life more intelligent and enjoyable. Through technology,
|
| 711 |
+
OrionStar aims to make society and the world a better place.
|
| 712 |
+
|
| 713 |
+
OrionStar possesses fully self-developed end-to-end artificial intelligence technologies, such as voice interaction and
|
| 714 |
+
visual navigation. It integrates product development capabilities and technological application capabilities. Based on
|
| 715 |
+
the Orion robotic arm platform, it has launched products such as OrionStar AI Robot Greeting, AI Robot Greeting Mini,
|
| 716 |
+
Lucki, Coffee Master, and established the open platform OrionOS for Orion robots. Following the philosophy of "Born for
|
| 717 |
+
Truly Useful Robots", OrionStar empowers more people through AI technology.
|
| 718 |
+
|
| 719 |
+
**The core strengths of OrionStar lies in possessing end-to-end AI application capabilities,** including big data preprocessing, large model pretraining, fine-tuning, prompt engineering, agent, etc. With comprehensive end-to-end model training capabilities, including systematic data processing workflows and the parallel model training capability of hundreds of GPUs, it has been successfully applied in various industry scenarios such as government affairs, cloud services, international e-commerce, and fast-moving consumer goods.
|
| 720 |
+
|
| 721 |
+
Companies with demands for deploying large-scale model applications are welcome to contact us.<br>
|
| 722 |
+
**Enquiry Hotline: 400-898-7779**<br>
|
| 723 |
+
**E-mail: ai@orionstar.com**<br>
|
| 724 |
+
**Discord Link: https://discord.gg/zumjDWgdAs**
|
| 725 |
+
|
| 726 |
+
<div align="center">
|
| 727 |
+
<img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" />
|
| 728 |
+
</div>
|
README_en.md
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
obsolete
|
README_zh.MD
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
obsolete
|
assets/imgs/llama_cpp_1.png
ADDED
|
assets/imgs/model_cap_en.png
ADDED
|
assets/imgs/model_cap_zh.png
ADDED
|
assets/imgs/opencompass_en.png
ADDED
|
assets/imgs/opencompass_zh.png
ADDED
|
assets/imgs/orion_start.PNG
ADDED
|
assets/imgs/vllm_1.png
ADDED
|
assets/imgs/wechat_group.jpg
ADDED
|
config.json
ADDED
|
@@ -0,0 +1,31 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"OrionForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"auto_map": {
|
| 6 |
+
"AutoConfig": "configuration_orion.OrionConfig",
|
| 7 |
+
"AutoModelForCausalLM": "modeling_orion.OrionForCausalLM"
|
| 8 |
+
},
|
| 9 |
+
"bos_token_id": 1,
|
| 10 |
+
"eos_token_id": 2,
|
| 11 |
+
"hidden_act": "silu",
|
| 12 |
+
"hidden_size": 5120,
|
| 13 |
+
"model_type": "orion",
|
| 14 |
+
"initializer_range": 0.02,
|
| 15 |
+
"intermediate_size": 15360,
|
| 16 |
+
"max_position_embeddings": 4096,
|
| 17 |
+
"max_sequence_length": 4096,
|
| 18 |
+
"num_attention_heads": 40,
|
| 19 |
+
"num_hidden_layers": 40,
|
| 20 |
+
"num_key_value_heads": 40,
|
| 21 |
+
"pad_token_id": 0,
|
| 22 |
+
"pretraining_tp": 1,
|
| 23 |
+
"rms_norm_eps": 1e-05,
|
| 24 |
+
"rope_scaling": null,
|
| 25 |
+
"rope_theta": 10000.0,
|
| 26 |
+
"tie_word_embeddings": false,
|
| 27 |
+
"torch_dtype": "bfloat16",
|
| 28 |
+
"transformers_version": "4.34.0",
|
| 29 |
+
"use_cache": true,
|
| 30 |
+
"vocab_size": 84608
|
| 31 |
+
}
|
configuration.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"framework":"Pytorch","task":"text-generation"}
|
configuration_orion.py
ADDED
|
@@ -0,0 +1,82 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Copyright (c) 2024, OrionStar Inc. All rights reserved.
|
| 2 |
+
|
| 3 |
+
from transformers import PretrainedConfig
|
| 4 |
+
|
| 5 |
+
class OrionConfig(PretrainedConfig):
|
| 6 |
+
model_type = "orion"
|
| 7 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 8 |
+
|
| 9 |
+
def __init__(
|
| 10 |
+
self,
|
| 11 |
+
vocab_size=84608,
|
| 12 |
+
hidden_size=4096,
|
| 13 |
+
intermediate_size=15360,
|
| 14 |
+
num_hidden_layers=40,
|
| 15 |
+
num_attention_heads=40,
|
| 16 |
+
num_key_value_heads=40,
|
| 17 |
+
hidden_act="silu",
|
| 18 |
+
max_position_embeddings=4096,
|
| 19 |
+
initializer_range=0.02,
|
| 20 |
+
rms_norm_eps=1e-5,
|
| 21 |
+
use_cache=True,
|
| 22 |
+
pad_token_id=None,
|
| 23 |
+
bos_token_id=1,
|
| 24 |
+
eos_token_id=2,
|
| 25 |
+
pretraining_tp=1,
|
| 26 |
+
tie_word_embeddings=False,
|
| 27 |
+
rope_theta=10000.0,
|
| 28 |
+
rope_scaling=None,
|
| 29 |
+
attention_bias=False,
|
| 30 |
+
**kwargs,
|
| 31 |
+
):
|
| 32 |
+
self.vocab_size = vocab_size
|
| 33 |
+
self.max_position_embeddings = max_position_embeddings
|
| 34 |
+
self.hidden_size = hidden_size
|
| 35 |
+
self.intermediate_size = intermediate_size
|
| 36 |
+
self.num_hidden_layers = num_hidden_layers
|
| 37 |
+
self.num_attention_heads = num_attention_heads
|
| 38 |
+
|
| 39 |
+
# for backward compatibility
|
| 40 |
+
if num_key_value_heads is None:
|
| 41 |
+
num_key_value_heads = num_attention_heads
|
| 42 |
+
|
| 43 |
+
self.num_key_value_heads = num_key_value_heads
|
| 44 |
+
self.hidden_act = hidden_act
|
| 45 |
+
self.initializer_range = initializer_range
|
| 46 |
+
self.rms_norm_eps = rms_norm_eps
|
| 47 |
+
self.pretraining_tp = pretraining_tp
|
| 48 |
+
self.use_cache = use_cache
|
| 49 |
+
self.rope_theta = rope_theta
|
| 50 |
+
self.rope_scaling = rope_scaling
|
| 51 |
+
self._rope_scaling_validation()
|
| 52 |
+
self.attention_bias = attention_bias
|
| 53 |
+
|
| 54 |
+
super().__init__(
|
| 55 |
+
pad_token_id=pad_token_id,
|
| 56 |
+
bos_token_id=bos_token_id,
|
| 57 |
+
eos_token_id=eos_token_id,
|
| 58 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 59 |
+
**kwargs,
|
| 60 |
+
)
|
| 61 |
+
|
| 62 |
+
def _rope_scaling_validation(self):
|
| 63 |
+
"""
|
| 64 |
+
Validate the `rope_scaling` configuration.
|
| 65 |
+
"""
|
| 66 |
+
if self.rope_scaling is None:
|
| 67 |
+
return
|
| 68 |
+
|
| 69 |
+
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
| 70 |
+
raise ValueError(
|
| 71 |
+
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
|
| 72 |
+
f"got {self.rope_scaling}"
|
| 73 |
+
)
|
| 74 |
+
rope_scaling_type = self.rope_scaling.get("type", None)
|
| 75 |
+
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
| 76 |
+
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
|
| 77 |
+
raise ValueError(
|
| 78 |
+
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
| 79 |
+
)
|
| 80 |
+
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
| 81 |
+
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
|
| 82 |
+
|
modeling_orion.py
ADDED
|
@@ -0,0 +1,1097 @@
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|
| 1 |
+
# Copyright 2024 OrionStar Inc. team. All rights reserved.
|
| 2 |
+
# Copied and adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
|
| 3 |
+
|
| 4 |
+
from transformers import AutoConfig, AutoModel
|
| 5 |
+
|
| 6 |
+
from .configuration_orion import OrionConfig
|
| 7 |
+
|
| 8 |
+
import numbers
|
| 9 |
+
import importlib
|
| 10 |
+
import math
|
| 11 |
+
from typing import List, Optional, Tuple, Union
|
| 12 |
+
|
| 13 |
+
import torch
|
| 14 |
+
import torch.nn.functional as F
|
| 15 |
+
from torch.nn.parameter import Parameter
|
| 16 |
+
import torch.utils.checkpoint
|
| 17 |
+
from torch import nn
|
| 18 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
| 19 |
+
from torch.nn import init
|
| 20 |
+
|
| 21 |
+
from transformers.activations import ACT2FN
|
| 22 |
+
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
|
| 23 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 24 |
+
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
|
| 25 |
+
from transformers.utils import (
|
| 26 |
+
add_start_docstrings,
|
| 27 |
+
add_start_docstrings_to_model_forward,
|
| 28 |
+
is_flash_attn_2_available,
|
| 29 |
+
logging,
|
| 30 |
+
replace_return_docstrings,
|
| 31 |
+
)
|
| 32 |
+
|
| 33 |
+
if is_flash_attn_2_available():
|
| 34 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
| 35 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
| 36 |
+
|
| 37 |
+
logger = logging.get_logger(__name__)
|
| 38 |
+
|
| 39 |
+
_CONFIG_FOR_DOC = "OrionConfig"
|
| 40 |
+
|
| 41 |
+
def _get_unpad_data(padding_mask):
|
| 42 |
+
seqlens_in_batch = padding_mask.sum(dim=-1, dtype=torch.int32)
|
| 43 |
+
indices = torch.nonzero(padding_mask.flatten(), as_tuple=False).flatten()
|
| 44 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
| 45 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
|
| 46 |
+
return (
|
| 47 |
+
indices,
|
| 48 |
+
cu_seqlens,
|
| 49 |
+
max_seqlen_in_batch,
|
| 50 |
+
)
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
| 54 |
+
def _make_causal_mask(
|
| 55 |
+
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
| 56 |
+
):
|
| 57 |
+
"""
|
| 58 |
+
Make causal mask used for bi-directional self-attention.
|
| 59 |
+
"""
|
| 60 |
+
bsz, tgt_len = input_ids_shape
|
| 61 |
+
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
|
| 62 |
+
mask_cond = torch.arange(mask.size(-1), device=device)
|
| 63 |
+
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
| 64 |
+
mask = mask.to(dtype)
|
| 65 |
+
|
| 66 |
+
if past_key_values_length > 0:
|
| 67 |
+
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
|
| 68 |
+
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
| 72 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
| 73 |
+
"""
|
| 74 |
+
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
| 75 |
+
"""
|
| 76 |
+
bsz, src_len = mask.size()
|
| 77 |
+
tgt_len = tgt_len if tgt_len is not None else src_len
|
| 78 |
+
|
| 79 |
+
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
| 80 |
+
|
| 81 |
+
inverted_mask = 1.0 - expanded_mask
|
| 82 |
+
|
| 83 |
+
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
| 84 |
+
|
| 85 |
+
class OrionRotaryEmbedding(nn.Module):
|
| 86 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
| 87 |
+
super().__init__()
|
| 88 |
+
|
| 89 |
+
self.dim = dim
|
| 90 |
+
self.max_position_embeddings = max_position_embeddings
|
| 91 |
+
self.base = base
|
| 92 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
| 93 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 94 |
+
|
| 95 |
+
# Build here to make `torch.jit.trace` work.
|
| 96 |
+
self._set_cos_sin_cache(
|
| 97 |
+
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
|
| 98 |
+
)
|
| 99 |
+
|
| 100 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 101 |
+
self.max_seq_len_cached = seq_len
|
| 102 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
| 103 |
+
|
| 104 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
| 105 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 106 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 107 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 108 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 109 |
+
|
| 110 |
+
def forward(self, x, seq_len=None):
|
| 111 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
| 112 |
+
if seq_len > self.max_seq_len_cached:
|
| 113 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
| 114 |
+
|
| 115 |
+
return (
|
| 116 |
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
| 117 |
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
| 118 |
+
)
|
| 119 |
+
|
| 120 |
+
|
| 121 |
+
class OrionLinearScalingRotaryEmbedding(OrionRotaryEmbedding):
|
| 122 |
+
"""OrionRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
| 123 |
+
|
| 124 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
| 125 |
+
self.scaling_factor = scaling_factor
|
| 126 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
| 127 |
+
|
| 128 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 129 |
+
self.max_seq_len_cached = seq_len
|
| 130 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
| 131 |
+
t = t / self.scaling_factor
|
| 132 |
+
|
| 133 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
| 134 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 135 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 136 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 137 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 138 |
+
|
| 139 |
+
|
| 140 |
+
class OrionDynamicNTKScalingRotaryEmbedding(OrionRotaryEmbedding):
|
| 141 |
+
"""OrionRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
| 142 |
+
|
| 143 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
| 144 |
+
self.scaling_factor = scaling_factor
|
| 145 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
| 146 |
+
|
| 147 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 148 |
+
self.max_seq_len_cached = seq_len
|
| 149 |
+
|
| 150 |
+
if seq_len > self.max_position_embeddings:
|
| 151 |
+
base = self.base * (
|
| 152 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
| 153 |
+
) ** (self.dim / (self.dim - 2))
|
| 154 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
| 155 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 156 |
+
|
| 157 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
| 158 |
+
|
| 159 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
| 160 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 161 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 162 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 163 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 164 |
+
|
| 165 |
+
|
| 166 |
+
def rotate_half(x):
|
| 167 |
+
"""Rotates half the hidden dims of the input."""
|
| 168 |
+
x1 = x[..., : x.shape[-1] // 2]
|
| 169 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
| 170 |
+
return torch.cat((-x2, x1), dim=-1)
|
| 171 |
+
|
| 172 |
+
|
| 173 |
+
# Copied from transformers.models.gpt_neox.modeling_gpt_neox.apply_rotary_pos_emb
|
| 174 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
|
| 175 |
+
cos = cos[position_ids].unsqueeze(1) # [seq_len, dim] -> [batch_size, 1, seq_len, head_dim]
|
| 176 |
+
sin = sin[position_ids].unsqueeze(1)
|
| 177 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
| 178 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
| 179 |
+
return q_embed, k_embed
|
| 180 |
+
|
| 181 |
+
|
| 182 |
+
class OrionMLP(nn.Module):
|
| 183 |
+
def __init__(self, config):
|
| 184 |
+
super().__init__()
|
| 185 |
+
self.config = config
|
| 186 |
+
self.hidden_size = config.hidden_size
|
| 187 |
+
self.intermediate_size = config.intermediate_size
|
| 188 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
| 189 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
| 190 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
| 191 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
| 192 |
+
|
| 193 |
+
def forward(self, x):
|
| 194 |
+
if self.config.pretraining_tp > 1:
|
| 195 |
+
slice = self.intermediate_size // self.config.pretraining_tp
|
| 196 |
+
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
|
| 197 |
+
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
|
| 198 |
+
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
|
| 199 |
+
|
| 200 |
+
gate_proj = torch.cat(
|
| 201 |
+
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
|
| 202 |
+
)
|
| 203 |
+
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
|
| 204 |
+
|
| 205 |
+
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
|
| 206 |
+
down_proj = [
|
| 207 |
+
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
|
| 208 |
+
]
|
| 209 |
+
down_proj = sum(down_proj)
|
| 210 |
+
else:
|
| 211 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
| 212 |
+
|
| 213 |
+
return down_proj
|
| 214 |
+
|
| 215 |
+
|
| 216 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
| 217 |
+
"""
|
| 218 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
| 219 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
| 220 |
+
"""
|
| 221 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
| 222 |
+
if n_rep == 1:
|
| 223 |
+
return hidden_states
|
| 224 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
| 225 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
| 226 |
+
|
| 227 |
+
|
| 228 |
+
class OrionAttention(nn.Module):
|
| 229 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
| 230 |
+
|
| 231 |
+
def __init__(self, config: OrionConfig):
|
| 232 |
+
super().__init__()
|
| 233 |
+
self.config = config
|
| 234 |
+
self.hidden_size = config.hidden_size
|
| 235 |
+
self.num_heads = config.num_attention_heads
|
| 236 |
+
self.head_dim = self.hidden_size // self.num_heads
|
| 237 |
+
self.num_key_value_heads = config.num_key_value_heads
|
| 238 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
| 239 |
+
self.max_position_embeddings = config.max_position_embeddings
|
| 240 |
+
self.rope_theta = config.rope_theta
|
| 241 |
+
|
| 242 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
| 243 |
+
raise ValueError(
|
| 244 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
| 245 |
+
f" and `num_heads`: {self.num_heads})."
|
| 246 |
+
)
|
| 247 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
|
| 248 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
| 249 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
| 250 |
+
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
|
| 251 |
+
self._init_rope()
|
| 252 |
+
|
| 253 |
+
def _init_rope(self):
|
| 254 |
+
if self.config.rope_scaling is None:
|
| 255 |
+
self.rotary_emb = OrionRotaryEmbedding(
|
| 256 |
+
self.head_dim,
|
| 257 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 258 |
+
base=self.rope_theta,
|
| 259 |
+
)
|
| 260 |
+
else:
|
| 261 |
+
scaling_type = self.config.rope_scaling["type"]
|
| 262 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
| 263 |
+
if scaling_type == "linear":
|
| 264 |
+
self.rotary_emb = OrionLinearScalingRotaryEmbedding(
|
| 265 |
+
self.head_dim,
|
| 266 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 267 |
+
scaling_factor=scaling_factor,
|
| 268 |
+
base=self.rope_theta,
|
| 269 |
+
)
|
| 270 |
+
elif scaling_type == "dynamic":
|
| 271 |
+
self.rotary_emb = OrionDynamicNTKScalingRotaryEmbedding(
|
| 272 |
+
self.head_dim,
|
| 273 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 274 |
+
scaling_factor=scaling_factor,
|
| 275 |
+
base=self.rope_theta,
|
| 276 |
+
)
|
| 277 |
+
else:
|
| 278 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
| 279 |
+
|
| 280 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
| 281 |
+
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
| 282 |
+
|
| 283 |
+
def forward(
|
| 284 |
+
self,
|
| 285 |
+
hidden_states: torch.Tensor,
|
| 286 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 287 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 288 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
| 289 |
+
output_attentions: bool = False,
|
| 290 |
+
use_cache: bool = False,
|
| 291 |
+
padding_mask: Optional[torch.LongTensor] = None,
|
| 292 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 293 |
+
bsz, q_len, _ = hidden_states.size()
|
| 294 |
+
|
| 295 |
+
if self.config.pretraining_tp > 1:
|
| 296 |
+
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
|
| 297 |
+
query_slices = self.q_proj.weight.split(
|
| 298 |
+
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
| 299 |
+
)
|
| 300 |
+
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
|
| 301 |
+
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
|
| 302 |
+
|
| 303 |
+
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
|
| 304 |
+
query_states = torch.cat(query_states, dim=-1)
|
| 305 |
+
|
| 306 |
+
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
|
| 307 |
+
key_states = torch.cat(key_states, dim=-1)
|
| 308 |
+
|
| 309 |
+
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
|
| 310 |
+
value_states = torch.cat(value_states, dim=-1)
|
| 311 |
+
|
| 312 |
+
else:
|
| 313 |
+
query_states = self.q_proj(hidden_states)
|
| 314 |
+
key_states = self.k_proj(hidden_states)
|
| 315 |
+
value_states = self.v_proj(hidden_states)
|
| 316 |
+
|
| 317 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
| 318 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 319 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 320 |
+
|
| 321 |
+
kv_seq_len = key_states.shape[-2]
|
| 322 |
+
if past_key_value is not None:
|
| 323 |
+
kv_seq_len += past_key_value[0].shape[-2]
|
| 324 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
| 325 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
| 326 |
+
|
| 327 |
+
if past_key_value is not None:
|
| 328 |
+
# reuse k, v, self_attention
|
| 329 |
+
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
| 330 |
+
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
| 331 |
+
|
| 332 |
+
past_key_value = (key_states, value_states) if use_cache else None
|
| 333 |
+
|
| 334 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
| 335 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
| 336 |
+
|
| 337 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
| 338 |
+
|
| 339 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
| 340 |
+
raise ValueError(
|
| 341 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
| 342 |
+
f" {attn_weights.size()}"
|
| 343 |
+
)
|
| 344 |
+
|
| 345 |
+
if attention_mask is not None:
|
| 346 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
| 347 |
+
raise ValueError(
|
| 348 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
| 349 |
+
)
|
| 350 |
+
attn_weights = attn_weights + attention_mask
|
| 351 |
+
|
| 352 |
+
# upcast attention to fp32
|
| 353 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
| 354 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
| 355 |
+
|
| 356 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
| 357 |
+
raise ValueError(
|
| 358 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
| 359 |
+
f" {attn_output.size()}"
|
| 360 |
+
)
|
| 361 |
+
|
| 362 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
| 363 |
+
|
| 364 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
| 365 |
+
|
| 366 |
+
if self.config.pretraining_tp > 1:
|
| 367 |
+
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
| 368 |
+
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
| 369 |
+
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
|
| 370 |
+
else:
|
| 371 |
+
attn_output = self.o_proj(attn_output)
|
| 372 |
+
|
| 373 |
+
if not output_attentions:
|
| 374 |
+
attn_weights = None
|
| 375 |
+
|
| 376 |
+
return attn_output, attn_weights, past_key_value
|
| 377 |
+
|
| 378 |
+
|
| 379 |
+
class OrionFlashAttention2(OrionAttention):
|
| 380 |
+
"""
|
| 381 |
+
Orion flash attention module. This module inherits from `OrionAttention` as the weights of the module stays
|
| 382 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
| 383 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
| 384 |
+
"""
|
| 385 |
+
|
| 386 |
+
def forward(
|
| 387 |
+
self,
|
| 388 |
+
hidden_states: torch.Tensor,
|
| 389 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 390 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 391 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
| 392 |
+
output_attentions: bool = False,
|
| 393 |
+
use_cache: bool = False,
|
| 394 |
+
padding_mask: Optional[torch.LongTensor] = None,
|
| 395 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 396 |
+
# OrionFlashAttention2 attention does not support output_attentions
|
| 397 |
+
output_attentions = False
|
| 398 |
+
|
| 399 |
+
bsz, q_len, _ = hidden_states.size()
|
| 400 |
+
|
| 401 |
+
query_states = self.q_proj(hidden_states)
|
| 402 |
+
key_states = self.k_proj(hidden_states)
|
| 403 |
+
value_states = self.v_proj(hidden_states)
|
| 404 |
+
|
| 405 |
+
# Flash attention requires the input to have the shape
|
| 406 |
+
# batch_size x seq_length x head_dime x hidden_dim
|
| 407 |
+
# therefore we just need to keep the original shape
|
| 408 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
| 409 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 410 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 411 |
+
|
| 412 |
+
kv_seq_len = key_states.shape[-2]
|
| 413 |
+
if past_key_value is not None:
|
| 414 |
+
kv_seq_len += past_key_value[0].shape[-2]
|
| 415 |
+
|
| 416 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
| 417 |
+
|
| 418 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
| 419 |
+
|
| 420 |
+
if past_key_value is not None:
|
| 421 |
+
# reuse k, v, self_attention
|
| 422 |
+
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
| 423 |
+
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
| 424 |
+
|
| 425 |
+
past_key_value = (key_states, value_states) if use_cache else None
|
| 426 |
+
|
| 427 |
+
query_states = query_states.transpose(1, 2)
|
| 428 |
+
key_states = key_states.transpose(1, 2)
|
| 429 |
+
value_states = value_states.transpose(1, 2)
|
| 430 |
+
|
| 431 |
+
# TODO: llama does not have dropout in the config??
|
| 432 |
+
# It is recommended to use dropout with FA according to the docs
|
| 433 |
+
# when training.
|
| 434 |
+
dropout_rate = 0.0 # if not self.training else self.attn_dropout
|
| 435 |
+
|
| 436 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
| 437 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
| 438 |
+
# cast them back in float16 just to be sure everything works as expected.
|
| 439 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
| 440 |
+
# in fp32. (LlamaRMSNorm handles it correctly)
|
| 441 |
+
input_dtype = query_states.dtype
|
| 442 |
+
if input_dtype == torch.float32:
|
| 443 |
+
logger.warning_once(
|
| 444 |
+
"The input hidden states seems to be silently casted in float32, this might be related to"
|
| 445 |
+
" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
| 446 |
+
" float16."
|
| 447 |
+
)
|
| 448 |
+
|
| 449 |
+
query_states = query_states.to(torch.float16)
|
| 450 |
+
key_states = key_states.to(torch.float16)
|
| 451 |
+
value_states = value_states.to(torch.float16)
|
| 452 |
+
|
| 453 |
+
attn_output = self._flash_attention_forward(
|
| 454 |
+
query_states, key_states, value_states, padding_mask, q_len, dropout=dropout_rate
|
| 455 |
+
)
|
| 456 |
+
|
| 457 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
| 458 |
+
attn_output = self.o_proj(attn_output)
|
| 459 |
+
|
| 460 |
+
if not output_attentions:
|
| 461 |
+
attn_weights = None
|
| 462 |
+
|
| 463 |
+
return attn_output, attn_weights, past_key_value
|
| 464 |
+
|
| 465 |
+
def _flash_attention_forward(
|
| 466 |
+
self, query_states, key_states, value_states, padding_mask, query_length, dropout=0.0, softmax_scale=None
|
| 467 |
+
):
|
| 468 |
+
"""
|
| 469 |
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
| 470 |
+
first unpad the input, then computes the attention scores and pad the final attention scores.
|
| 471 |
+
|
| 472 |
+
Args:
|
| 473 |
+
query_states (`torch.Tensor`):
|
| 474 |
+
Input query states to be passed to Flash Attention API
|
| 475 |
+
key_states (`torch.Tensor`):
|
| 476 |
+
Input key states to be passed to Flash Attention API
|
| 477 |
+
value_states (`torch.Tensor`):
|
| 478 |
+
Input value states to be passed to Flash Attention API
|
| 479 |
+
padding_mask (`torch.Tensor`):
|
| 480 |
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
| 481 |
+
position of padding tokens and 1 for the position of non-padding tokens.
|
| 482 |
+
dropout (`int`, *optional*):
|
| 483 |
+
Attention dropout
|
| 484 |
+
softmax_scale (`float`, *optional*):
|
| 485 |
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
| 486 |
+
"""
|
| 487 |
+
# Contains at least one padding token in the sequence
|
| 488 |
+
if padding_mask is not None:
|
| 489 |
+
batch_size = query_states.shape[0]
|
| 490 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
| 491 |
+
query_states, key_states, value_states, padding_mask, query_length
|
| 492 |
+
)
|
| 493 |
+
|
| 494 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
| 495 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
| 496 |
+
|
| 497 |
+
attn_output_unpad = flash_attn_varlen_func(
|
| 498 |
+
query_states,
|
| 499 |
+
key_states,
|
| 500 |
+
value_states,
|
| 501 |
+
cu_seqlens_q=cu_seqlens_q,
|
| 502 |
+
cu_seqlens_k=cu_seqlens_k,
|
| 503 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
| 504 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
| 505 |
+
dropout_p=dropout,
|
| 506 |
+
softmax_scale=softmax_scale,
|
| 507 |
+
causal=True,
|
| 508 |
+
)
|
| 509 |
+
|
| 510 |
+
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
| 511 |
+
else:
|
| 512 |
+
attn_output = flash_attn_func(
|
| 513 |
+
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=True
|
| 514 |
+
)
|
| 515 |
+
|
| 516 |
+
return attn_output
|
| 517 |
+
|
| 518 |
+
def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_length):
|
| 519 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(padding_mask)
|
| 520 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
| 521 |
+
|
| 522 |
+
key_layer = index_first_axis(
|
| 523 |
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
| 524 |
+
)
|
| 525 |
+
value_layer = index_first_axis(
|
| 526 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
| 527 |
+
)
|
| 528 |
+
if query_length == kv_seq_len:
|
| 529 |
+
query_layer = index_first_axis(
|
| 530 |
+
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
| 531 |
+
)
|
| 532 |
+
cu_seqlens_q = cu_seqlens_k
|
| 533 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
| 534 |
+
indices_q = indices_k
|
| 535 |
+
elif query_length == 1:
|
| 536 |
+
max_seqlen_in_batch_q = 1
|
| 537 |
+
cu_seqlens_q = torch.arange(
|
| 538 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
| 539 |
+
) # There is a memcpy here, that is very bad.
|
| 540 |
+
indices_q = cu_seqlens_q[:-1]
|
| 541 |
+
query_layer = query_layer.squeeze(1)
|
| 542 |
+
else:
|
| 543 |
+
# The -q_len: slice assumes left padding.
|
| 544 |
+
padding_mask = padding_mask[:, -query_length:]
|
| 545 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, padding_mask)
|
| 546 |
+
|
| 547 |
+
return (
|
| 548 |
+
query_layer,
|
| 549 |
+
key_layer,
|
| 550 |
+
value_layer,
|
| 551 |
+
indices_q,
|
| 552 |
+
(cu_seqlens_q, cu_seqlens_k),
|
| 553 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
| 554 |
+
)
|
| 555 |
+
|
| 556 |
+
|
| 557 |
+
class OrionDecoderLayer(nn.Module):
|
| 558 |
+
def __init__(self, config: OrionConfig):
|
| 559 |
+
super().__init__()
|
| 560 |
+
self.hidden_size = config.hidden_size
|
| 561 |
+
self.self_attn = (
|
| 562 |
+
OrionAttention(config=config)
|
| 563 |
+
if not getattr(config, "_flash_attn_2_enabled", False)
|
| 564 |
+
else OrionFlashAttention2(config=config)
|
| 565 |
+
)
|
| 566 |
+
self.mlp = OrionMLP(config)
|
| 567 |
+
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 568 |
+
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 569 |
+
|
| 570 |
+
def forward(
|
| 571 |
+
self,
|
| 572 |
+
hidden_states: torch.Tensor,
|
| 573 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 574 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 575 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
| 576 |
+
output_attentions: Optional[bool] = False,
|
| 577 |
+
use_cache: Optional[bool] = False,
|
| 578 |
+
padding_mask: Optional[torch.LongTensor] = None,
|
| 579 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
| 580 |
+
"""
|
| 581 |
+
Args:
|
| 582 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
| 583 |
+
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
| 584 |
+
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
| 585 |
+
output_attentions (`bool`, *optional*):
|
| 586 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
| 587 |
+
returned tensors for more detail.
|
| 588 |
+
use_cache (`bool`, *optional*):
|
| 589 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
| 590 |
+
(see `past_key_values`).
|
| 591 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
| 592 |
+
"""
|
| 593 |
+
|
| 594 |
+
residual = hidden_states
|
| 595 |
+
|
| 596 |
+
hidden_states = self.input_layernorm(hidden_states)
|
| 597 |
+
|
| 598 |
+
# Self Attention
|
| 599 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
| 600 |
+
hidden_states=hidden_states,
|
| 601 |
+
attention_mask=attention_mask,
|
| 602 |
+
position_ids=position_ids,
|
| 603 |
+
past_key_value=past_key_value,
|
| 604 |
+
output_attentions=output_attentions,
|
| 605 |
+
use_cache=use_cache,
|
| 606 |
+
padding_mask=padding_mask,
|
| 607 |
+
)
|
| 608 |
+
hidden_states = residual + hidden_states
|
| 609 |
+
|
| 610 |
+
# Fully Connected
|
| 611 |
+
residual = hidden_states
|
| 612 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
| 613 |
+
hidden_states = self.mlp(hidden_states)
|
| 614 |
+
hidden_states = residual + hidden_states
|
| 615 |
+
|
| 616 |
+
outputs = (hidden_states,)
|
| 617 |
+
|
| 618 |
+
if output_attentions:
|
| 619 |
+
outputs += (self_attn_weights,)
|
| 620 |
+
|
| 621 |
+
if use_cache:
|
| 622 |
+
outputs += (present_key_value,)
|
| 623 |
+
|
| 624 |
+
return outputs
|
| 625 |
+
|
| 626 |
+
class OrionPreTrainedModel(PreTrainedModel):
|
| 627 |
+
config_class = OrionConfig
|
| 628 |
+
base_model_prefix = "model"
|
| 629 |
+
supports_gradient_checkpointing = True
|
| 630 |
+
_no_split_modules = ["OrionDecoderLayer"]
|
| 631 |
+
_skip_keys_device_placement = "past_key_values"
|
| 632 |
+
_supports_flash_attn_2 = True
|
| 633 |
+
|
| 634 |
+
def _init_weights(self, module):
|
| 635 |
+
std = self.config.initializer_range
|
| 636 |
+
if isinstance(module, nn.Linear):
|
| 637 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
| 638 |
+
if module.bias is not None:
|
| 639 |
+
module.bias.data.zero_()
|
| 640 |
+
elif isinstance(module, nn.Embedding):
|
| 641 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
| 642 |
+
if module.padding_idx is not None:
|
| 643 |
+
module.weight.data[module.padding_idx].zero_()
|
| 644 |
+
|
| 645 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
| 646 |
+
if isinstance(module, OrionModel):
|
| 647 |
+
module.gradient_checkpointing = value
|
| 648 |
+
|
| 649 |
+
class OrionModel(OrionPreTrainedModel):
|
| 650 |
+
"""
|
| 651 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OrionDecoderLayer`]
|
| 652 |
+
|
| 653 |
+
Args:
|
| 654 |
+
config: OrionConfig
|
| 655 |
+
"""
|
| 656 |
+
|
| 657 |
+
def __init__(self, config: OrionConfig):
|
| 658 |
+
super().__init__(config)
|
| 659 |
+
self.padding_idx = config.pad_token_id
|
| 660 |
+
self.vocab_size = config.vocab_size
|
| 661 |
+
|
| 662 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
| 663 |
+
self.layers = nn.ModuleList([OrionDecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
| 664 |
+
self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 665 |
+
|
| 666 |
+
self.gradient_checkpointing = False
|
| 667 |
+
# Initialize weights and apply final processing
|
| 668 |
+
self.post_init()
|
| 669 |
+
|
| 670 |
+
def get_input_embeddings(self):
|
| 671 |
+
return self.embed_tokens
|
| 672 |
+
|
| 673 |
+
def set_input_embeddings(self, value):
|
| 674 |
+
self.embed_tokens = value
|
| 675 |
+
|
| 676 |
+
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
| 677 |
+
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
| 678 |
+
# create causal mask
|
| 679 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
| 680 |
+
combined_attention_mask = None
|
| 681 |
+
if input_shape[-1] > 1:
|
| 682 |
+
combined_attention_mask = _make_causal_mask(
|
| 683 |
+
input_shape,
|
| 684 |
+
inputs_embeds.dtype,
|
| 685 |
+
device=inputs_embeds.device,
|
| 686 |
+
past_key_values_length=past_key_values_length,
|
| 687 |
+
)
|
| 688 |
+
|
| 689 |
+
if attention_mask is not None:
|
| 690 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
| 691 |
+
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
| 692 |
+
inputs_embeds.device
|
| 693 |
+
)
|
| 694 |
+
combined_attention_mask = (
|
| 695 |
+
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
| 696 |
+
)
|
| 697 |
+
|
| 698 |
+
return combined_attention_mask
|
| 699 |
+
|
| 700 |
+
def forward(
|
| 701 |
+
self,
|
| 702 |
+
input_ids: torch.LongTensor = None,
|
| 703 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 704 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 705 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 706 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 707 |
+
use_cache: Optional[bool] = None,
|
| 708 |
+
output_attentions: Optional[bool] = None,
|
| 709 |
+
output_hidden_states: Optional[bool] = None,
|
| 710 |
+
return_dict: Optional[bool] = None,
|
| 711 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
| 712 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 713 |
+
output_hidden_states = (
|
| 714 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 715 |
+
)
|
| 716 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
| 717 |
+
|
| 718 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 719 |
+
|
| 720 |
+
# retrieve input_ids and inputs_embeds
|
| 721 |
+
if input_ids is not None and inputs_embeds is not None:
|
| 722 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
| 723 |
+
elif input_ids is not None:
|
| 724 |
+
batch_size, seq_length = input_ids.shape
|
| 725 |
+
elif inputs_embeds is not None:
|
| 726 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
| 727 |
+
else:
|
| 728 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
| 729 |
+
|
| 730 |
+
seq_length_with_past = seq_length
|
| 731 |
+
past_key_values_length = 0
|
| 732 |
+
|
| 733 |
+
if past_key_values is not None:
|
| 734 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
| 735 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
| 736 |
+
|
| 737 |
+
if position_ids is None:
|
| 738 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
| 739 |
+
position_ids = torch.arange(
|
| 740 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
| 741 |
+
)
|
| 742 |
+
position_ids = position_ids.unsqueeze(0)
|
| 743 |
+
|
| 744 |
+
if inputs_embeds is None:
|
| 745 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
| 746 |
+
# embed positions
|
| 747 |
+
if attention_mask is None:
|
| 748 |
+
attention_mask = torch.ones(
|
| 749 |
+
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
| 750 |
+
)
|
| 751 |
+
padding_mask = None
|
| 752 |
+
else:
|
| 753 |
+
if 0 in attention_mask:
|
| 754 |
+
padding_mask = attention_mask
|
| 755 |
+
else:
|
| 756 |
+
padding_mask = None
|
| 757 |
+
|
| 758 |
+
attention_mask = self._prepare_decoder_attention_mask(
|
| 759 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
| 760 |
+
)
|
| 761 |
+
|
| 762 |
+
hidden_states = inputs_embeds
|
| 763 |
+
|
| 764 |
+
if self.gradient_checkpointing and self.training:
|
| 765 |
+
if use_cache:
|
| 766 |
+
logger.warning_once(
|
| 767 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
| 768 |
+
)
|
| 769 |
+
use_cache = False
|
| 770 |
+
|
| 771 |
+
# decoder layers
|
| 772 |
+
all_hidden_states = () if output_hidden_states else None
|
| 773 |
+
all_self_attns = () if output_attentions else None
|
| 774 |
+
next_decoder_cache = () if use_cache else None
|
| 775 |
+
|
| 776 |
+
for idx, decoder_layer in enumerate(self.layers):
|
| 777 |
+
if output_hidden_states:
|
| 778 |
+
all_hidden_states += (hidden_states,)
|
| 779 |
+
|
| 780 |
+
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
| 781 |
+
|
| 782 |
+
if self.gradient_checkpointing and self.training:
|
| 783 |
+
|
| 784 |
+
def create_custom_forward(module):
|
| 785 |
+
def custom_forward(*inputs):
|
| 786 |
+
# None for past_key_value
|
| 787 |
+
return module(*inputs, past_key_value, output_attentions, padding_mask=padding_mask)
|
| 788 |
+
|
| 789 |
+
return custom_forward
|
| 790 |
+
|
| 791 |
+
layer_outputs = torch.utils.checkpoint.checkpoint(
|
| 792 |
+
create_custom_forward(decoder_layer), hidden_states, attention_mask, position_ids
|
| 793 |
+
)
|
| 794 |
+
else:
|
| 795 |
+
layer_outputs = decoder_layer(
|
| 796 |
+
hidden_states,
|
| 797 |
+
attention_mask=attention_mask,
|
| 798 |
+
position_ids=position_ids,
|
| 799 |
+
past_key_value=past_key_value,
|
| 800 |
+
output_attentions=output_attentions,
|
| 801 |
+
use_cache=use_cache,
|
| 802 |
+
padding_mask=padding_mask,
|
| 803 |
+
)
|
| 804 |
+
|
| 805 |
+
hidden_states = layer_outputs[0]
|
| 806 |
+
|
| 807 |
+
if use_cache:
|
| 808 |
+
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
| 809 |
+
|
| 810 |
+
if output_attentions:
|
| 811 |
+
all_self_attns += (layer_outputs[1],)
|
| 812 |
+
|
| 813 |
+
hidden_states = self.norm(hidden_states)
|
| 814 |
+
|
| 815 |
+
# add hidden states from the last decoder layer
|
| 816 |
+
if output_hidden_states:
|
| 817 |
+
all_hidden_states += (hidden_states,)
|
| 818 |
+
|
| 819 |
+
next_cache = next_decoder_cache if use_cache else None
|
| 820 |
+
if not return_dict:
|
| 821 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
| 822 |
+
return BaseModelOutputWithPast(
|
| 823 |
+
last_hidden_state=hidden_states,
|
| 824 |
+
past_key_values=next_cache,
|
| 825 |
+
hidden_states=all_hidden_states,
|
| 826 |
+
attentions=all_self_attns,
|
| 827 |
+
)
|
| 828 |
+
|
| 829 |
+
|
| 830 |
+
class OrionForCausalLM(OrionPreTrainedModel):
|
| 831 |
+
model_type = "orion"
|
| 832 |
+
_tied_weights_keys = ["lm_head.weight"]
|
| 833 |
+
|
| 834 |
+
def __init__(self, config):
|
| 835 |
+
super().__init__(config)
|
| 836 |
+
self.model = OrionModel(config)
|
| 837 |
+
self.vocab_size = config.vocab_size
|
| 838 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| 839 |
+
|
| 840 |
+
# Initialize weights and apply final processing
|
| 841 |
+
self.post_init()
|
| 842 |
+
|
| 843 |
+
def get_input_embeddings(self):
|
| 844 |
+
return self.model.embed_tokens
|
| 845 |
+
|
| 846 |
+
def set_input_embeddings(self, value):
|
| 847 |
+
self.model.embed_tokens = value
|
| 848 |
+
|
| 849 |
+
def get_output_embeddings(self):
|
| 850 |
+
return self.lm_head
|
| 851 |
+
|
| 852 |
+
def set_output_embeddings(self, new_embeddings):
|
| 853 |
+
self.lm_head = new_embeddings
|
| 854 |
+
|
| 855 |
+
def set_decoder(self, decoder):
|
| 856 |
+
self.model = decoder
|
| 857 |
+
|
| 858 |
+
def get_decoder(self):
|
| 859 |
+
return self.model
|
| 860 |
+
|
| 861 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
| 862 |
+
def forward(
|
| 863 |
+
self,
|
| 864 |
+
input_ids: torch.LongTensor = None,
|
| 865 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 866 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 867 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 868 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 869 |
+
labels: Optional[torch.LongTensor] = None,
|
| 870 |
+
use_cache: Optional[bool] = None,
|
| 871 |
+
output_attentions: Optional[bool] = None,
|
| 872 |
+
output_hidden_states: Optional[bool] = None,
|
| 873 |
+
return_dict: Optional[bool] = None,
|
| 874 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
| 875 |
+
r"""
|
| 876 |
+
Args:
|
| 877 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 878 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
| 879 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
| 880 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
| 881 |
+
|
| 882 |
+
Returns:
|
| 883 |
+
|
| 884 |
+
Example:
|
| 885 |
+
|
| 886 |
+
```python
|
| 887 |
+
>>> from transformers import AutoTokenizer, OrionForCausalLM
|
| 888 |
+
|
| 889 |
+
>>> model = OrionForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
| 890 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
| 891 |
+
|
| 892 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
| 893 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
| 894 |
+
|
| 895 |
+
>>> # Generate
|
| 896 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
| 897 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
| 898 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
| 899 |
+
```"""
|
| 900 |
+
|
| 901 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 902 |
+
output_hidden_states = (
|
| 903 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 904 |
+
)
|
| 905 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 906 |
+
|
| 907 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
| 908 |
+
outputs = self.model(
|
| 909 |
+
input_ids=input_ids,
|
| 910 |
+
attention_mask=attention_mask,
|
| 911 |
+
position_ids=position_ids,
|
| 912 |
+
past_key_values=past_key_values,
|
| 913 |
+
inputs_embeds=inputs_embeds,
|
| 914 |
+
use_cache=use_cache,
|
| 915 |
+
output_attentions=output_attentions,
|
| 916 |
+
output_hidden_states=output_hidden_states,
|
| 917 |
+
return_dict=return_dict,
|
| 918 |
+
)
|
| 919 |
+
|
| 920 |
+
hidden_states = outputs[0]
|
| 921 |
+
if self.config.pretraining_tp > 1:
|
| 922 |
+
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
| 923 |
+
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
| 924 |
+
logits = torch.cat(logits, dim=-1)
|
| 925 |
+
else:
|
| 926 |
+
logits = self.lm_head(hidden_states)
|
| 927 |
+
logits = logits.float()
|
| 928 |
+
|
| 929 |
+
loss = None
|
| 930 |
+
if labels is not None:
|
| 931 |
+
# Shift so that tokens < n predict n
|
| 932 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
| 933 |
+
shift_labels = labels[..., 1:].contiguous()
|
| 934 |
+
# Flatten the tokens
|
| 935 |
+
loss_fct = CrossEntropyLoss()
|
| 936 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
| 937 |
+
shift_labels = shift_labels.view(-1)
|
| 938 |
+
# Enable model parallelism
|
| 939 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
| 940 |
+
loss = loss_fct(shift_logits, shift_labels)
|
| 941 |
+
|
| 942 |
+
if not return_dict:
|
| 943 |
+
output = (logits,) + outputs[1:]
|
| 944 |
+
return (loss,) + output if loss is not None else output
|
| 945 |
+
|
| 946 |
+
return CausalLMOutputWithPast(
|
| 947 |
+
loss=loss,
|
| 948 |
+
logits=logits,
|
| 949 |
+
past_key_values=outputs.past_key_values,
|
| 950 |
+
hidden_states=outputs.hidden_states,
|
| 951 |
+
attentions=outputs.attentions,
|
| 952 |
+
)
|
| 953 |
+
|
| 954 |
+
def prepare_inputs_for_generation(
|
| 955 |
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
| 956 |
+
):
|
| 957 |
+
if past_key_values:
|
| 958 |
+
input_ids = input_ids[:, -1:]
|
| 959 |
+
|
| 960 |
+
position_ids = kwargs.get("position_ids", None)
|
| 961 |
+
if attention_mask is not None and position_ids is None:
|
| 962 |
+
# create position_ids on the fly for batch generation
|
| 963 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
| 964 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
| 965 |
+
if past_key_values:
|
| 966 |
+
position_ids = position_ids[:, -1].unsqueeze(-1)
|
| 967 |
+
|
| 968 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
| 969 |
+
if inputs_embeds is not None and past_key_values is None:
|
| 970 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
| 971 |
+
else:
|
| 972 |
+
model_inputs = {"input_ids": input_ids}
|
| 973 |
+
|
| 974 |
+
model_inputs.update(
|
| 975 |
+
{
|
| 976 |
+
"position_ids": position_ids,
|
| 977 |
+
"past_key_values": past_key_values,
|
| 978 |
+
"use_cache": kwargs.get("use_cache"),
|
| 979 |
+
"attention_mask": attention_mask,
|
| 980 |
+
}
|
| 981 |
+
)
|
| 982 |
+
return model_inputs
|
| 983 |
+
|
| 984 |
+
@staticmethod
|
| 985 |
+
def _reorder_cache(past_key_values, beam_idx):
|
| 986 |
+
reordered_past = ()
|
| 987 |
+
for layer_past in past_key_values:
|
| 988 |
+
reordered_past += (
|
| 989 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
| 990 |
+
)
|
| 991 |
+
return reordered_past
|
| 992 |
+
|
| 993 |
+
class OrionForSequenceClassification(OrionPreTrainedModel):
|
| 994 |
+
def __init__(self, config):
|
| 995 |
+
super().__init__(config)
|
| 996 |
+
self.num_labels = config.num_labels
|
| 997 |
+
self.model = OrionModel(config)
|
| 998 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
| 999 |
+
|
| 1000 |
+
# Initialize weights and apply final processing
|
| 1001 |
+
self.post_init()
|
| 1002 |
+
|
| 1003 |
+
def get_input_embeddings(self):
|
| 1004 |
+
return self.model.embed_tokens
|
| 1005 |
+
|
| 1006 |
+
def set_input_embeddings(self, value):
|
| 1007 |
+
self.model.embed_tokens = value
|
| 1008 |
+
|
| 1009 |
+
def forward(
|
| 1010 |
+
self,
|
| 1011 |
+
input_ids: torch.LongTensor = None,
|
| 1012 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1013 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1014 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 1015 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1016 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1017 |
+
use_cache: Optional[bool] = None,
|
| 1018 |
+
output_attentions: Optional[bool] = None,
|
| 1019 |
+
output_hidden_states: Optional[bool] = None,
|
| 1020 |
+
return_dict: Optional[bool] = None,
|
| 1021 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
| 1022 |
+
r"""
|
| 1023 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1024 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
| 1025 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
| 1026 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
| 1027 |
+
"""
|
| 1028 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1029 |
+
|
| 1030 |
+
transformer_outputs = self.model(
|
| 1031 |
+
input_ids,
|
| 1032 |
+
attention_mask=attention_mask,
|
| 1033 |
+
position_ids=position_ids,
|
| 1034 |
+
past_key_values=past_key_values,
|
| 1035 |
+
inputs_embeds=inputs_embeds,
|
| 1036 |
+
use_cache=use_cache,
|
| 1037 |
+
output_attentions=output_attentions,
|
| 1038 |
+
output_hidden_states=output_hidden_states,
|
| 1039 |
+
return_dict=return_dict,
|
| 1040 |
+
)
|
| 1041 |
+
hidden_states = transformer_outputs[0]
|
| 1042 |
+
logits = self.score(hidden_states)
|
| 1043 |
+
|
| 1044 |
+
if input_ids is not None:
|
| 1045 |
+
batch_size = input_ids.shape[0]
|
| 1046 |
+
else:
|
| 1047 |
+
batch_size = inputs_embeds.shape[0]
|
| 1048 |
+
|
| 1049 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
| 1050 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
| 1051 |
+
if self.config.pad_token_id is None:
|
| 1052 |
+
sequence_lengths = -1
|
| 1053 |
+
else:
|
| 1054 |
+
if input_ids is not None:
|
| 1055 |
+
sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
|
| 1056 |
+
logits.device
|
| 1057 |
+
)
|
| 1058 |
+
else:
|
| 1059 |
+
sequence_lengths = -1
|
| 1060 |
+
|
| 1061 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
| 1062 |
+
|
| 1063 |
+
loss = None
|
| 1064 |
+
if labels is not None:
|
| 1065 |
+
labels = labels.to(logits.device)
|
| 1066 |
+
if self.config.problem_type is None:
|
| 1067 |
+
if self.num_labels == 1:
|
| 1068 |
+
self.config.problem_type = "regression"
|
| 1069 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
| 1070 |
+
self.config.problem_type = "single_label_classification"
|
| 1071 |
+
else:
|
| 1072 |
+
self.config.problem_type = "multi_label_classification"
|
| 1073 |
+
|
| 1074 |
+
if self.config.problem_type == "regression":
|
| 1075 |
+
loss_fct = MSELoss()
|
| 1076 |
+
if self.num_labels == 1:
|
| 1077 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
| 1078 |
+
else:
|
| 1079 |
+
loss = loss_fct(pooled_logits, labels)
|
| 1080 |
+
elif self.config.problem_type == "single_label_classification":
|
| 1081 |
+
loss_fct = CrossEntropyLoss()
|
| 1082 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
| 1083 |
+
elif self.config.problem_type == "multi_label_classification":
|
| 1084 |
+
loss_fct = BCEWithLogitsLoss()
|
| 1085 |
+
loss = loss_fct(pooled_logits, labels)
|
| 1086 |
+
if not return_dict:
|
| 1087 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
| 1088 |
+
return ((loss,) + output) if loss is not None else output
|
| 1089 |
+
|
| 1090 |
+
return SequenceClassifierOutputWithPast(
|
| 1091 |
+
loss=loss,
|
| 1092 |
+
logits=pooled_logits,
|
| 1093 |
+
past_key_values=transformer_outputs.past_key_values,
|
| 1094 |
+
hidden_states=transformer_outputs.hidden_states,
|
| 1095 |
+
attentions=transformer_outputs.attentions,
|
| 1096 |
+
)
|
| 1097 |
+
|
pytorch_model-00001-of-00002.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d8155f53c91f0e9591a69a5e49ac5deee0e0f32c671f5a869ed6dd85853e16a2
|
| 3 |
+
size 15704709713
|
pytorch_model-00002-of-00002.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e05566cec048435bc8fda67cc481e46f42df62f4707168d3420cb60bbda13acf
|
| 3 |
+
size 13292827298
|
pytorch_model.bin.index.json
ADDED
|
@@ -0,0 +1,491 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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"model.layers.7.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 456 |
+
"model.layers.7.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 457 |
+
"model.layers.7.post_attention_layernorm.bias": "pytorch_model-00001-of-00002.bin",
|
| 458 |
+
"model.layers.7.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
| 459 |
+
"model.layers.7.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 460 |
+
"model.layers.7.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 461 |
+
"model.layers.7.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 462 |
+
"model.layers.7.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
|
| 463 |
+
"model.layers.7.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 464 |
+
"model.layers.8.input_layernorm.bias": "pytorch_model-00001-of-00002.bin",
|
| 465 |
+
"model.layers.8.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
| 466 |
+
"model.layers.8.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 467 |
+
"model.layers.8.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 468 |
+
"model.layers.8.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 469 |
+
"model.layers.8.post_attention_layernorm.bias": "pytorch_model-00001-of-00002.bin",
|
| 470 |
+
"model.layers.8.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
| 471 |
+
"model.layers.8.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 472 |
+
"model.layers.8.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 473 |
+
"model.layers.8.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 474 |
+
"model.layers.8.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
|
| 475 |
+
"model.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 476 |
+
"model.layers.9.input_layernorm.bias": "pytorch_model-00001-of-00002.bin",
|
| 477 |
+
"model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
| 478 |
+
"model.layers.9.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 479 |
+
"model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 480 |
+
"model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 481 |
+
"model.layers.9.post_attention_layernorm.bias": "pytorch_model-00001-of-00002.bin",
|
| 482 |
+
"model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
| 483 |
+
"model.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 484 |
+
"model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 485 |
+
"model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 486 |
+
"model.layers.9.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
|
| 487 |
+
"model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
| 488 |
+
"model.norm.bias": "pytorch_model-00002-of-00002.bin",
|
| 489 |
+
"model.norm.weight": "pytorch_model-00002-of-00002.bin"
|
| 490 |
+
}
|
| 491 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<s>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": true,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "</s>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": true,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": "<unk>",
|
| 17 |
+
"unk_token": {
|
| 18 |
+
"content": "<unk>",
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"normalized": true,
|
| 21 |
+
"rstrip": false,
|
| 22 |
+
"single_word": false
|
| 23 |
+
}
|
| 24 |
+
}
|
tokenization_orion.py
ADDED
|
@@ -0,0 +1,269 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Copyright (c) 2024, OrionStar Inc. All rights reserved.
|
| 2 |
+
|
| 3 |
+
import os
|
| 4 |
+
from shutil import copyfile
|
| 5 |
+
from typing import Any, Dict, List, Optional, Tuple
|
| 6 |
+
import re
|
| 7 |
+
|
| 8 |
+
import sentencepiece as spm
|
| 9 |
+
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
|
| 13 |
+
|
| 14 |
+
PRETRAINED_VOCAB_FILES_MAP = {
|
| 15 |
+
"vocab_file": {},
|
| 16 |
+
"tokenizer_file": {},
|
| 17 |
+
}
|
| 18 |
+
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
class OrionTokenizer(PreTrainedTokenizer):
|
| 22 |
+
"""
|
| 23 |
+
Construct a Orion tokenizer. Based on byte-level Byte-Pair-Encoding.
|
| 24 |
+
|
| 25 |
+
Args:
|
| 26 |
+
vocab_file (`str`):
|
| 27 |
+
Path to the vocabulary file.
|
| 28 |
+
"""
|
| 29 |
+
|
| 30 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
| 31 |
+
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
| 32 |
+
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
| 33 |
+
model_input_names = ["input_ids", "attention_mask"]
|
| 34 |
+
|
| 35 |
+
def __init__(
|
| 36 |
+
self,
|
| 37 |
+
vocab_file,
|
| 38 |
+
unk_token="<unk>",
|
| 39 |
+
bos_token="<s>",
|
| 40 |
+
eos_token="</s>",
|
| 41 |
+
pad_token=None,
|
| 42 |
+
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
| 43 |
+
add_bos_token=True,
|
| 44 |
+
add_eos_token=False,
|
| 45 |
+
clean_up_tokenization_spaces=False,
|
| 46 |
+
**kwargs,
|
| 47 |
+
):
|
| 48 |
+
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
| 49 |
+
bos_token = (
|
| 50 |
+
AddedToken(bos_token, lstrip=False, rstrip=False)
|
| 51 |
+
if isinstance(bos_token, str)
|
| 52 |
+
else bos_token
|
| 53 |
+
)
|
| 54 |
+
eos_token = (
|
| 55 |
+
AddedToken(eos_token, lstrip=False, rstrip=False)
|
| 56 |
+
if isinstance(eos_token, str)
|
| 57 |
+
else eos_token
|
| 58 |
+
)
|
| 59 |
+
unk_token = (
|
| 60 |
+
AddedToken(unk_token, lstrip=False, rstrip=False)
|
| 61 |
+
if isinstance(unk_token, str)
|
| 62 |
+
else unk_token
|
| 63 |
+
)
|
| 64 |
+
pad_token = (
|
| 65 |
+
AddedToken(pad_token, lstrip=False, rstrip=False)
|
| 66 |
+
if isinstance(pad_token, str)
|
| 67 |
+
else pad_token
|
| 68 |
+
)
|
| 69 |
+
self.vocab_file = vocab_file
|
| 70 |
+
self.add_bos_token = add_bos_token
|
| 71 |
+
self.add_eos_token = add_eos_token
|
| 72 |
+
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
| 73 |
+
self.sp_model.Load(vocab_file)
|
| 74 |
+
|
| 75 |
+
super().__init__(
|
| 76 |
+
bos_token=bos_token,
|
| 77 |
+
eos_token=eos_token,
|
| 78 |
+
unk_token=unk_token,
|
| 79 |
+
pad_token=pad_token,
|
| 80 |
+
add_bos_token=add_bos_token,
|
| 81 |
+
add_eos_token=add_eos_token,
|
| 82 |
+
sp_model_kwargs=self.sp_model_kwargs,
|
| 83 |
+
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
| 84 |
+
**kwargs,
|
| 85 |
+
)
|
| 86 |
+
|
| 87 |
+
def __getstate__(self):
|
| 88 |
+
state = self.__dict__.copy()
|
| 89 |
+
state["sp_model"] = None
|
| 90 |
+
return state
|
| 91 |
+
|
| 92 |
+
def __setstate__(self, d):
|
| 93 |
+
self.__dict__ = d
|
| 94 |
+
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
| 95 |
+
self.sp_model.Load(self.vocab_file)
|
| 96 |
+
|
| 97 |
+
@property
|
| 98 |
+
def vocab_size(self):
|
| 99 |
+
"""Returns vocab size"""
|
| 100 |
+
return self.sp_model.get_piece_size()
|
| 101 |
+
|
| 102 |
+
def get_vocab(self):
|
| 103 |
+
"""Returns vocab as a dict"""
|
| 104 |
+
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
| 105 |
+
vocab.update(self.added_tokens_encoder)
|
| 106 |
+
return vocab
|
| 107 |
+
|
| 108 |
+
def _tokenize(self, text):
|
| 109 |
+
"""Returns a tokenized string."""
|
| 110 |
+
return self.sp_model.encode(text, out_type=str)
|
| 111 |
+
|
| 112 |
+
def _convert_token_to_id(self, token):
|
| 113 |
+
"""Converts a token (str) in an id using the vocab."""
|
| 114 |
+
return self.sp_model.piece_to_id(token)
|
| 115 |
+
|
| 116 |
+
def _convert_id_to_token(self, index):
|
| 117 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
| 118 |
+
token = self.sp_model.IdToPiece(index)
|
| 119 |
+
return token
|
| 120 |
+
|
| 121 |
+
def convert_tokens_to_string(self, tokens):
|
| 122 |
+
"""Converts a sequence of tokens (string) in a single string."""
|
| 123 |
+
zhPattern = re.compile(u'[\u4e00-\u9fa5]+')
|
| 124 |
+
need_convert_punctuation=(",",";","!","?",":","(",")")
|
| 125 |
+
current_sub_tokens = []
|
| 126 |
+
out_string = ""
|
| 127 |
+
prev_is_special = False
|
| 128 |
+
for i, token in enumerate(tokens):
|
| 129 |
+
# make sure that special tokens are not decoded using sentencepiece model
|
| 130 |
+
if token in self.all_special_tokens:
|
| 131 |
+
if not prev_is_special and i != 0:
|
| 132 |
+
out_string += " "
|
| 133 |
+
out_string += self.sp_model.decode(current_sub_tokens) + token
|
| 134 |
+
prev_is_special = True
|
| 135 |
+
current_sub_tokens = []
|
| 136 |
+
if any([True if punctuation in token else False for punctuation in need_convert_punctuation]):
|
| 137 |
+
out_string += self.sp_model.decode(current_sub_tokens)
|
| 138 |
+
token=self.sp_model.decode(token)
|
| 139 |
+
if zhPattern.search(out_string[-20:]):
|
| 140 |
+
token = self.to_zh_punctuation(token)
|
| 141 |
+
out_string += token
|
| 142 |
+
current_sub_tokens = []
|
| 143 |
+
else:
|
| 144 |
+
current_sub_tokens.append(token)
|
| 145 |
+
prev_is_special = False
|
| 146 |
+
out_string += self.sp_model.decode(current_sub_tokens)
|
| 147 |
+
return out_string
|
| 148 |
+
|
| 149 |
+
def to_zh_punctuation(self, token):
|
| 150 |
+
return token.replace(",","，").replace(";","；").replace("!","！").replace("?","？").replace(":","：").replace("(","（").replace(")","）")
|
| 151 |
+
|
| 152 |
+
def save_vocabulary(
|
| 153 |
+
self, save_directory, filename_prefix: Optional[str] = None
|
| 154 |
+
) -> Tuple[str]:
|
| 155 |
+
"""
|
| 156 |
+
Save the vocabulary and special tokens file to a directory.
|
| 157 |
+
|
| 158 |
+
Args:
|
| 159 |
+
save_directory (`str`):
|
| 160 |
+
The directory in which to save the vocabulary.
|
| 161 |
+
|
| 162 |
+
Returns:
|
| 163 |
+
`Tuple(str)`: Paths to the files saved.
|
| 164 |
+
"""
|
| 165 |
+
if not os.path.isdir(save_directory):
|
| 166 |
+
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
| 167 |
+
return
|
| 168 |
+
out_vocab_file = os.path.join(
|
| 169 |
+
save_directory,
|
| 170 |
+
(filename_prefix + "-" if filename_prefix else "")
|
| 171 |
+
+ VOCAB_FILES_NAMES["vocab_file"],
|
| 172 |
+
)
|
| 173 |
+
|
| 174 |
+
if os.path.abspath(self.vocab_file) != os.path.abspath(
|
| 175 |
+
out_vocab_file
|
| 176 |
+
) and os.path.isfile(self.vocab_file):
|
| 177 |
+
copyfile(self.vocab_file, out_vocab_file)
|
| 178 |
+
elif not os.path.isfile(self.vocab_file):
|
| 179 |
+
with open(out_vocab_file, "wb") as fi:
|
| 180 |
+
content_spiece_model = self.sp_model.serialized_model_proto()
|
| 181 |
+
fi.write(content_spiece_model)
|
| 182 |
+
|
| 183 |
+
return (out_vocab_file,)
|
| 184 |
+
|
| 185 |
+
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
| 186 |
+
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
| 187 |
+
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
| 188 |
+
|
| 189 |
+
output = bos_token_id + token_ids_0 + eos_token_id
|
| 190 |
+
|
| 191 |
+
if token_ids_1 is not None:
|
| 192 |
+
output = output + bos_token_id + token_ids_1 + eos_token_id
|
| 193 |
+
|
| 194 |
+
return output
|
| 195 |
+
|
| 196 |
+
def get_special_tokens_mask(
|
| 197 |
+
self,
|
| 198 |
+
token_ids_0: List[int],
|
| 199 |
+
token_ids_1: Optional[List[int]] = None,
|
| 200 |
+
already_has_special_tokens: bool = False,
|
| 201 |
+
) -> List[int]:
|
| 202 |
+
"""
|
| 203 |
+
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
| 204 |
+
special tokens using the tokenizer `prepare_for_model` method.
|
| 205 |
+
|
| 206 |
+
Args:
|
| 207 |
+
token_ids_0 (`List[int]`):
|
| 208 |
+
List of IDs.
|
| 209 |
+
token_ids_1 (`List[int]`, *optional*):
|
| 210 |
+
Optional second list of IDs for sequence pairs.
|
| 211 |
+
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
| 212 |
+
Whether or not the token list is already formatted with special tokens for the model.
|
| 213 |
+
|
| 214 |
+
Returns:
|
| 215 |
+
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
| 216 |
+
"""
|
| 217 |
+
if already_has_special_tokens:
|
| 218 |
+
return super().get_special_tokens_mask(
|
| 219 |
+
token_ids_0=token_ids_0,
|
| 220 |
+
token_ids_1=token_ids_1,
|
| 221 |
+
already_has_special_tokens=True,
|
| 222 |
+
)
|
| 223 |
+
|
| 224 |
+
bos_token_id = [1] if self.add_bos_token else []
|
| 225 |
+
eos_token_id = [1] if self.add_eos_token else []
|
| 226 |
+
|
| 227 |
+
if token_ids_1 is None:
|
| 228 |
+
return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
|
| 229 |
+
return (
|
| 230 |
+
bos_token_id
|
| 231 |
+
+ ([0] * len(token_ids_0))
|
| 232 |
+
+ eos_token_id
|
| 233 |
+
+ bos_token_id
|
| 234 |
+
+ ([0] * len(token_ids_1))
|
| 235 |
+
+ eos_token_id
|
| 236 |
+
)
|
| 237 |
+
|
| 238 |
+
def create_token_type_ids_from_sequences(
|
| 239 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
| 240 |
+
) -> List[int]:
|
| 241 |
+
"""
|
| 242 |
+
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
|
| 243 |
+
sequence pair mask has the following format:
|
| 244 |
+
|
| 245 |
+
```
|
| 246 |
+
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
|
| 247 |
+
| first sequence | second sequence |
|
| 248 |
+
```
|
| 249 |
+
|
| 250 |
+
if token_ids_1 is None, only returns the first portion of the mask (0s).
|
| 251 |
+
|
| 252 |
+
Args:
|
| 253 |
+
token_ids_0 (`List[int]`):
|
| 254 |
+
List of ids.
|
| 255 |
+
token_ids_1 (`List[int]`, *optional*):
|
| 256 |
+
Optional second list of IDs for sequence pairs.
|
| 257 |
+
|
| 258 |
+
Returns:
|
| 259 |
+
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
| 260 |
+
"""
|
| 261 |
+
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
| 262 |
+
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
| 263 |
+
|
| 264 |
+
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
|
| 265 |
+
|
| 266 |
+
if token_ids_1 is not None:
|
| 267 |
+
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
|
| 268 |
+
|
| 269 |
+
return output
|
tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ded43118b7418f56db97a4eed08a5c265c03120158229ddd4fbcc9658241d5f0
|
| 3 |
+
size 1520600
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": false,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"auto_map": {
|
| 5 |
+
"AutoTokenizer": [
|
| 6 |
+
"tokenization_orion.OrionTokenizer",
|
| 7 |
+
null
|
| 8 |
+
]
|
| 9 |
+
},
|
| 10 |
+
"bos_token": {
|
| 11 |
+
"__type": "AddedToken",
|
| 12 |
+
"content": "<s>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": true,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": true
|
| 17 |
+
},
|
| 18 |
+
"clean_up_tokenization_spaces": false,
|
| 19 |
+
"eos_token": {
|
| 20 |
+
"__type": "AddedToken",
|
| 21 |
+
"content": "</s>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": true,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": true
|
| 26 |
+
},
|
| 27 |
+
"model_max_length": 4096,
|
| 28 |
+
"pad_token": {
|
| 29 |
+
"__type": "AddedToken",
|
| 30 |
+
"content": "<unk>",
|
| 31 |
+
"lstrip": false,
|
| 32 |
+
"normalized": true,
|
| 33 |
+
"rstrip": false,
|
| 34 |
+
"single_word": true
|
| 35 |
+
},
|
| 36 |
+
"sp_model_kwargs": {},
|
| 37 |
+
"tokenizer_class": "OrionTokenizer",
|
| 38 |
+
"unk_token": {
|
| 39 |
+
"__type": "AddedToken",
|
| 40 |
+
"content": "<unk>",
|
| 41 |
+
"lstrip": false,
|
| 42 |
+
"normalized": true,
|
| 43 |
+
"rstrip": false,
|
| 44 |
+
"single_word": true
|
| 45 |
+
}
|
| 46 |
+
}
|