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<div align="center">
# EmoLLM-心理健康大模型
</div>
<p align="center">
<a href="https://github.com/SmartFlowAI/EmoLLM/">
<img src="assets/EmoLLM_transparent.png" alt="Logo" width="50%">
</a>
<div align="center">
<!-- PROJECT SHIELDS -->
[![Contributors][contributors-shield]][contributors-url]
[![Forks][forks-shield]][forks-url]
[![Issues][issues-shield]][issues-url]
[![OpenXLab_App][OpenXLab_App-image]][OpenXLab_App-url]
[![OpenXLab_Model][OpenXLab_Model-image]][OpenXLab_Model-url]
[![MIT License][license-shield]][license-url]
[![Stargazers][stars-shield]][stars-url]
</div>
<h3 align="center">EmoLLM</h3>
<div align="center">
简体中文| <a href="README_EN.md" >English</a>
<br />
<br />
<a href="https://github.com/SmartFlowAI/EmoLLM"><strong>探索本项目的文档 »</strong></a>
<br />
<br />
<a href="https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0">体验EmoLLM 2.0</a>
·
<a href="https://github.com/SmartFlowAI/EmoLLM/issues">报告Bug</a>
·
<a href="https://github.com/SmartFlowAI/EmoLLM/issues">提出新特性</a>
</div>
<!-- 本篇README.md面向开发者 -->
**EmoLLM** 是一系列能够支持 **理解用户-支持用户-帮助用户** 心理健康辅导链路的心理健康大模型，由 `LLM`指令微调而来，欢迎大家star~⭐⭐。目前已经开源的 `LLM` 微调配置如下：
<div align="center">
| 模型 | 类型 | 链接 | 模型链接 |
| :-------------------: | :------: | :------------------------------------------------------------------------------------------------------: |:------: |
| InternLM2_5_7B_chat | QLORA | [internlm2_5_chat_7b_qlora_oasst1_e3.py](./xtuner_config/internlm2_5_chat_7b_qlora_oasst1_e3.py) |[ModelScope](https://www.modelscope.cn/models/z342994309/emollm_interlm2_5/) |
| InternLM2_7B_chat | QLORA | [internlm2_7b_chat_qlora_e3.py](./xtuner_config/internlm2_7b_chat_qlora_e3.py) | |
| InternLM2_7B_chat | 全量微调 | [internlm2_chat_7b_full.py](./xtuner_config/internlm2_chat_7b_full.py) | |
| InternLM2_7B_base | QLORA | [internlm2_7b_base_qlora_e10_M_1e4_32_64.py](./xtuner_config/internlm2_7b_base_qlora_e10_M_1e4_32_64.py) |[OpenXLab](https://openxlab.org.cn/models/detail/chg0901/EmoLLM-InternLM7B-base-10e), [ModelScope](https://www.modelscope.cn/models/chg0901/EmoLLM-InternLM7B-base-10e/summary) |
| InternLM2_1_8B_chat | 全量微调 | [internlm2_1_8b_full_alpaca_e3.py](./xtuner_config/internlm2_1_8b_full_alpaca_e3.py) | |
| InternLM2_20B_chat | LORA |[internlm2_20b_chat_lora_alpaca_e3.py](./xtuner_config/internlm2_20b_chat_lora_alpaca_e3.py)| |
| Qwen_7b_chat | QLORA | [qwen_7b_chat_qlora_e3.py](./xtuner_config/qwen_7b_chat_qlora_e3.py) | |
| Qwen1_5-0_5B-Chat | 全量微调 | [qwen1_5_0_5_B_full.py](./xtuner_config/qwen1_5_0_5_B_full.py) | |
| Baichuan2_13B_chat | QLORA | [baichuan2_13b_chat_qlora_alpaca_e3.py](./xtuner_config/baichuan2_13b_chat_qlora_alpaca_e3.py) | |
| ChatGLM3_6B | LORA | [chatglm3_6b_lora_alpaca_e3.py](./xtuner_config/chatglm3_6b_lora_alpaca_e3.py) | |
| DeepSeek MoE_16B_chat | QLORA | [deepseek_moe_16b_chat_qlora_oasst1_e3.py](./xtuner_config/deepseek_moe_16b_chat_qlora_oasst1_e3.py) | |
| Mixtral 8x7B_instruct | QLORA | [mixtral_8x7b_instruct_qlora_oasst1_e3.py](./xtuner_config/mixtral_8x7b_instruct_qlora_oasst1_e3.py) | |
| LLaMA3_8b_instruct | QLORA | [aiwei_llama3_8b_instruct_qlora_e3.py](./xtuner_config/aiwei_llama3_8b_instruct_qlora_e3.py) | |
| LLaMA3_8b_instruct | QLORA | [llama3_8b_instruct_qlora_alpaca_e3_M_ruozhi_scM.py](./xtuner_config/llama3_8b_instruct_qlora_alpaca_e3_M_ruozhi_scM.py) |[OpenXLab](https://openxlab.org.cn/models/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0), [ModelScope](https://modelscope.cn/models/chg0901/EmoLLM-Llama3-8B-Instruct3.0/summary) |
| …… | …… | …… | …… |
</div>
欢迎大家为本项目做出贡献~
---
心理健康大模型（Mental Health Grand Model）是一个综合性的概念，它旨在全面理解和促进个体、群体乃至整个社会的心理健康状态。这个模型通常包含以下几个关键组成部分：
- 认知因素：涉及个体的思维模式、信念系统、认知偏差以及解决问题的能力。认知因素对心理健康有重要影响，因为它们影响个体如何解释和应对生活中的事件。
- 情感因素：包括情绪调节、情感表达和情感体验。情感健康是心理健康的重要组成部分，涉及个体如何管理和表达自己的情感，以及如何从负面情绪中恢复。
- 行为因素：涉及个体的行为模式、习惯和应对策略。这包括应对压力的技巧、社交技能以及自我效能感，即个体对自己能力的信心。
- 社会环境：包括家庭、工作、社区和文化背景等外部因素，这些因素对个体的心理健康有着直接和间接的影响。
- 生理健康：身体健康与心理健康紧密相关。良好的身体健康可以促进心理健康，反之亦然。
- 心理韧性：指个体在面对逆境时的恢复力和适应能力。心理韧性强的人更能够从挑战中恢复，并从中学习和成长。
- 预防和干预措施：心理健康大模型还包括预防心理问题和促进心理健康的策略，如心理教育、心理咨询、心理治疗和社会支持系统。
- 评估和诊断工具：为了有效促进心理健康，需要有科学的工具来评估个体的心理状态，以及诊断可能存在的心理问题。
<table>
<tr>
<td align="center" style="background-color: transparent">
<img src="assets\aiwei_demo.gif" alt="占位图">
</td>
<td align="center" style="background-color: transparent">
<img src="assets\aiwei_demo2.gif" alt="占位图">
</td>
</tr>
<tr>
<td align="center" style="background-color: transparent">
<img src="assets\aiwei_demo3.gif" alt="占位图">
</td>
<td align="center" style="background-color: transparent">
<img src="assets\aiwei_demo4.gif" alt="占位图">
</td>
</tr>
</table>
## 🎇最近更新
- 【2024.7】新增基于InternLM2_5_7B_chat[微调配置](./xtuner_config/internlm2_5_chat_7b_qlora_oasst1_e3.py)、模型文件发布在 [ModelScope](https://www.modelscope.cn/models/z342994309/emollm_interlm2_5/)。
- 【2024.6】新增基于[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)[GLM4-9B-chat微调指南](./doc/GLM-4-9B-chat%20Lora%20微调（llama-factory）.md)、新增[基于swift的微调指南](./swift/)、论文[ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models](https://arxiv.org/abs/2406.14952)引用了EmoLLM且EmoLLM取得了较好的效果。
- 【2024.05.28】EmoLLM使用的多轮对话数据集CPsyCounD和专业评测方法已公开，详见2024 ACL findings[《CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling》](https://arxiv.org/abs/2405.16433)!
- 【2024.05.08】EmoLLM**爹系男友阅览体验版**上线 [1. **百度AppBuilder**](https://appbuilder.baidu.com/s/4cLyw) [2. **OpenXLab**](https://openxlab.org.cn/apps/detail/chg0901/EmoLLM3.0_Gradio_Llama3-8B-Instruct3.0), 欢迎点赞收藏
- 【2024.05.07】[增量预训练指南](xtuner_config/pt/README.md)
- 【2024.05.04】基于LLaMA3_8b_instruct的[EmoLLM3.0 OpenXLab Demo](https://st-app-center-006861-9746-jlroxvg.openxlab.space/)上线（[重启链接](https://openxlab.org.cn/apps/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0)）, [**LLAMA3微调指南**](xtuner_config/README_llama3_8b_instruct_qlora_alpaca_e3_M.md)**更新**，在[**OpenXLab**](https://openxlab.org.cn/models/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0)和[**ModelScope**](https://modelscope.cn/models/chg0901/EmoLLM-Llama3-8B-Instruct3.0/summary)平台发布**LLaMA3_8b_instruct-8B QLoRA微调模型 EmoLLM3.0权重**
- 【2024.04.20】[LLAMA3微调指南](xtuner_config/README_llama3_8b_instruct_qlora_alpaca_e3_M.md)及基于[LLaMA3_8b_instruct的艾薇](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM-LLaMA3_8b_instruct_aiwei)开源
- 【2023.04.14】新增[快速开始](docs/quick_start.md)和保姆级教程[BabyEmoLLM](Baby_EmoLLM.ipynb)
- 【2024.04.02】在 Huggingface 上传[老母亲心理咨询师](https://huggingface.co/brycewang2018/EmoLLM-mother/tree/main)
- 【2024.03.25】在百度飞桨平台发布[爹系男友心理咨询师](https://aistudio.baidu.com/community/app/68787)
- 【2024.03.24】在**OpenXLab**和**ModelScope**平台发布**InternLM2-Base-7B QLoRA微调模型**, 具体请查看[**InternLM2-Base-7B QLoRA**](./xtuner_config/README_internlm2_7b_base_qlora.md)
- 【2024.03.12】在百度飞桨平台发布[艾薇](https://aistudio.baidu.com/community/app/63335)
- 【2024.03.11】 **EmoLLM V2.0 相比 EmoLLM V1.0 全面提升，已超越 Role-playing ChatGPT 在心理咨询任务上的能力！**[点击体验EmoLLM V2.0](https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0)，更新[数据集统计及详细信息](./datasets/)、[路线图](./assets/Roadmap_ZH.png)
- 【2024.03.09】 新增并发功能加速 [QA 对生成](./scripts/qa_generation/)、[RAG pipeline](./rag/)
- 【2024.03.03】 [基于InternLM2-7B-chat全量微调版本EmoLLM V2.0开源](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full)，需要两块A100*80G，更新专业评估，详见[evaluate](./evaluate/)，更新基于PaddleOCR的PDF转txt工具脚本，详见[scripts](./scripts/)
- 【2024.02.29】更新客观评估计算，详见[evaluate](./evaluate/)，更新一系列数据集，详见[datasets](./datasets/)
- 【2024.02.27】更新英文readme和一系列数据集（舔狗和单轮对话）
- 【2024.02.23】推出基于InternLM2_7B_chat_qlora的 `温柔御姐心理医生艾薇`，[点击获取模型权重](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_aiwei)，[配置文件](xtuner_config/aiwei-internlm2_chat_7b_qlora.py)，[在线体验链接](https://openxlab.org.cn/apps/detail/ajupyter/EmoLLM-aiwei)
- 【2024.02.23】更新[若干微调配置](/xtuner_config/)，新增 [data_pro.json](/datasets/data_pro.json)（数量更多、场景更全、更丰富）和 [aiwei.json](/datasets/aiwei.json)（温柔御姐角色扮演专用，带有Emoji表情），即将推出 `温柔御姐心理医生艾薇`
- 【2024.02.18】 [基于Qwen1_5-0_5B-Chat全量微调版本开源](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen1_5-0_5B-Chat_full_sft/summary)，算力有限的道友可以玩起来~
<details>
<summary>查看更多</summary>
- 【2024.02.06】 EmoLLM在[**Openxlab** ](https://openxlab.org.cn/models/detail/jujimeizuo/EmoLLM_Model) 平台下载量高达18.7k，欢迎大家体验！
<p align="center">
<img src="https://github.com/SmartFlowAI/EmoLLM/assets/62385492/7e931682-c54d-4ded-bc67-79130c68d744" alt="模型下载量">
</p>
- 【2024.02.05】 项目荣获公众号**NLP工程化**推文宣传[推文链接](https://mp.weixin.qq.com/s/78lrRl2tlXEKUfElnkVx4A)，为博主推广一波，欢迎大家关注！！🥳🥳
<p align="center">
<img src="https://github.com/SmartFlowAI/EmoLLM/assets/62385492/47868d6a-2e91-4aa9-a630-e594c14295b4" alt="公众号二维码">
</p>
- 【2024.02.03】 [项目宣传视频](https://www.bilibili.com/video/BV1N7421N76X/)完成 😊
- 【2024.01.27】 完善数据构建文档、微调指南、部署指南、Readme等相关文档 👏
- 【2024.01.25】 EmoLLM V1.0 已部署上线 https://openxlab.org.cn/apps/detail/jujimeizuo/EmoLLM 😀
</details>
## 🏆荣誉栏
- 项目荣获上海人工智能实验室举办的**2024浦源大模型系列挑战赛春季赛*****创新创意奖***
<p align="center">
<a href="https://github.com/SmartFlowAI/EmoLLM/">
<img src="assets/Shusheng.png" alt="浦语挑战赛创新创意奖">
</p>
- 荣获[AI 赋能大学计划“全国高校行”](https://mp.weixin.qq.com/s/yyaulQ1wBzKq5cXaGl2Wag)一等奖
- 🎉感谢以下媒体及公众号朋友对本项目的报道和支持(以下排名不分先后! 若有遗漏、十分抱歉, 一并感激! 欢迎补充!): [NLP工程化](https://mp.weixin.qq.com/s/78lrRl2tlXEKUfElnkVx4A), [机智流](https://mp.weixin.qq.com/s/_wMCmssRMGd0Oz5OVVkjAA), [爱可可爱生活](https://mp.weixin.qq.com/s/4WaCg4OpkCWXEuWHuV4r3w), [阿郎小哥](https://mp.weixin.qq.com/s/_MSMeL1XHP0v5lDi3YaPVw), [大模型日知路](https://mp.weixin.qq.com/s/FYYibsCXtfU6FFM9TuKILA), [AI Code](https://mp.weixin.qq.com/s/yDWGY3S4CwCi6U_irsFmqA) 等!
- 项目宣传视频 [EmoLLM](https://www.bilibili.com/video/BV1N7421N76X/) 已发布，欢迎大家围观 😀
## 🎯路线图
<p align="center">
<a href="https://github.com/SmartFlowAI/EmoLLM/">
<img src="assets/Roadmap_ZH.png" alt="Roadmap_ZH">
</a>
## 🔗框架图
<p align="center">
<a href="https://github.com/SmartFlowAI/EmoLLM/">
<img src="assets/框架图.png" alt="Framework_ZH">
</a>
## 目录
- [EmoLLM-心理健康大模型](#emollm-心理健康大模型)
- [🎇最近更新](#最近更新)
- [🏆荣誉栏](#荣誉栏)
- [🎯路线图](#路线图)
- [🔗框架图](#框架图)
- [目录](#目录)
- [开发前的配置要求](#开发前的配置要求)
- [使用指南](#使用指南)
- [🍪快速体验](#快速体验)
- [📌数据构建](#数据构建)
- [🎨微调指南](#微调指南)
- [🔧部署指南](#部署指南)
- [⚙RAG(检索增强生成)](#rag检索增强生成)
- [🎓评测指南](#评测指南)
- [使用到的框架](#使用到的框架)
- [如何参与本项目](#如何参与本项目)
- [作者（排名不分先后）](#作者排名不分先后)
- [版权说明](#版权说明)
- [引用](#引用)
- [特别鸣谢](#特别鸣谢)
- [相关项目](#相关项目)
- [人员](#人员)
- [Star History](#star-history)
- [🌟 Contributors](#-contributors)
- [交流群](#交流群)
###### 开发前的配置要求
- 硬件：A100 40G（仅针对InternLM2_7B_chat+qlora微调+deepspeed zero2优化）
###### 使用指南
1. Clone the repo
```sh
git clone https://github.com/SmartFlowAI/EmoLLM.git
```
2. 依次阅读或者选择感兴趣的部分阅读：
- [快速体验](#快速体验)
- [数据构建](#数据构建)
- [微调指南](#微调指南)
- [部署指南](#部署指南)
- [RAG](#rag检索增强生成)
- [评测指南](#评测指南)
- 查看更多详情
### 🍪快速体验
- 请阅读[快速体验](quick_start/quick_start.md)查阅
- 快速上手：[Baby EmoLLM](quick_start/Baby_EmoLLM.ipynb)
### 📌数据构建
- 请阅读[数据构建指南](generate_data/tutorial.md)查阅
- 微调用到的数据集见[datasets](datasets/data.json)
### 🎨微调指南
详见[微调指南](xtuner_config/README.md)
### 🔧部署指南
- Demo部署：详见[部署指南](demo/README.md)
- 基于[LMDeploy](https://github.com/InternLM/lmdeploy/)的量化部署：详见[deploy](./deploy/lmdeploy.md)
### ⚙RAG(检索增强生成)
- 详见[RAG](rag/README.md)
### 🎓评测指南
- 本模型评测分为通用评测和专业评测，请阅读[评测指南](evaluate/README.md)查阅
<details>
<summary>更多详情</summary>
### 使用到的框架
- [Xtuner](https://github.com/InternLM/xtuner)：用于微调
- [Transformers](https://github.com/huggingface/transformers)
- [Pytorch](https://pytorch.org/)
- [LMDeploy](https://github.com/InternLM/lmdeploy/)：用于量化部署
- [Stremlit](https://streamlit.io/)：用于构建Demo
- [DeepSpeed](https://github.com/microsoft/DeepSpeed)：并行训练
- …
#### 如何参与本项目
贡献使开源社区成为一个学习、激励和创造的绝佳场所。你所作的任何贡献都是**非常感谢**的。
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
</details>
### 作者（排名不分先后）
| 用户名 | 学校/组织 | 备注 | 贡献 |
| :----------------------------------------------------------: | :------------------------------------------------: | :----------------------------------------------------------: | :-------------------------------------------: |
| [aJupyter](https://github.com/aJupyter) | 南开大学在读硕士 | DataWhale成员 | 项目发起人 |
| [MING-ZCH](https://github.com/MING-ZCH) | 华中科技大学在读本科生 | LLM x Psychology 研究者 | 项目联合负责人 |
| [jujimeizuo](https://github.com/jujimeizuo) | 江南大学在读硕士 | | |
| [Smiling-Weeping-zhr](https://github.com/Smiling-Weeping-zhr) | 哈尔滨工业大学（威海）在读本科生 | | |
| [8baby8](https://github.com/8baby8) | 飞桨领航团区域主管 | 文心大模型核心开发者 | |
| [zxazys](https://github.com/zxazys) | 南开大学在读硕士 | | |
| [JasonLLLLLLLLLLL](https://github.com/JasonLLLLLLLLLLL) | swufe | | |
| [MrCatAI](https://github.com/MrCatAI) | AI搬用工 | | |
| [ZeyuBa](https://github.com/ZeyuBa) | 自动化所在读硕士 | | |
| [aiyinyuedejustin](https://github.com/aiyinyuedejustin) | 宾夕法尼亚大学在读硕士 | | |
| [Nobody-ML](https://github.com/Nobody-ML) | 中国石油大学（华东）在读本科生 | | |
| [chg0901](https://github.com/chg0901) | [MiniSora](https://github.com/mini-sora/minisora/) | [MiniSora](https://github.com/mini-sora/minisora/)主要维护者，管理员 | LLM预训练和微调、模型上传、数据清洗、文档翻译 |
| [Mxoder](https://github.com/Mxoder) | 北京航空航天大学在读本科生 | | |
| [Anooyman](https://github.com/Anooyman) | 南京理工大学硕士 | | |
| [Vicky-3021](https://github.com/Vicky-3021) | 西安电子科技大学硕士（研0） | | |
| [SantiagoTOP](https://github.com/santiagoTOP) | 太原理工大学在读硕士 | | 数据清洗，文档管理、Baby EmoLLM维护 |
| [zealot52099](https://github.com/zealot52099) | 个人开发者 | | 清洗数据、LLM微调、RAG |
| [wwwyfff](https://github.com/wwwyfff) | 复旦大学在读硕士 | | |
| [Yicooong](https://github.com/Yicooong) | 南开大学在读硕士 | | |
| [jkhumor](https://github.com/jkhumor) | 南开大学在读硕士 | | RAG |
| [lll997150986](https://github.com/lll997150986) | 南开大学在读硕士 | | 微调 |
| [nln-maker](https://github.com/nln-maker) | 南开大学在读硕士 | | 前后端开发 |
| [dream00001](https://github.com/dream00001) | 南开大学在读硕士 | | 前后端开发 |
| [王几行XING](https://zhihu.com/people/brycewang1898) | 北京大学硕士毕业 | | 清洗数据、LLM微调、前后端开发 |
| [思在] | 北京大学硕士毕业（微软美国） | | LLM微调、前后端开发 |
| [TingWei](https://github.com/wwewwt) | 电子科技大学硕士毕业 | 微信公众号：AI大模型在手 | 微调 |
| [PengYu](https://github.com/hi-pengyu) | 石河子大学在读硕士 | | LLM微调 |
### 版权说明
该项目签署了 MIT 授权许可，详情请参阅 [LICENSE](https://github.com/SmartFlowAI/EmoLLM/blob/main/LICENSE)
### 引用
如果本项目对您的工作有所帮助，请使用以下格式引用：
```bibtex
@misc{EmoLLM,
title={EmoLLM},
author={EmoLLM},
url={https://github.com/SmartFlowAI/EmoLLM/},
year={2024}
}
```
### 特别鸣谢
#### 相关项目
- [CPsyCoun](https://github.com/CAS-SIAT-XinHai/CPsyCoun)
- [Smile](https://github.com/qiuhuachuan/smile)
- [SoulChat](https://github.com/scutcyr/SoulChat)
#### 人员
- [上海人工智能实验室](https://www.shlab.org.cn/)
- [闻星（浦语小助手）](https://github.com/vansin)
- 阿布（北大心理学硕士）
- [Sanbu](https://github.com/sanbuphy)
- [HatBoy](https://github.com/hatboy)
<!-- links -->
<!-- [linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555 -->
<!-- [linkedin-url]: https://linkedin.com/in/aJupyter -->
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=SmartFlowAI/EmoLLM&type=Date)](https://star-history.com/#SmartFlowAI/EmoLLM&Date)
## 🌟 Contributors
[![EmoLLM contributors](https://contrib.rocks/image?repo=SmartFlowAI/EmoLLM&max=50)](https://github.com/SmartFlowAI/EmoLLM/graphs/contributors)
[your-project-path]: SmartflowAI/EmoLLM
[contributors-shield]: https://img.shields.io/github/contributors/SmartflowAI/EmoLLM.svg?style=flat-square
[contributors-url]: https://github.com/SmartflowAI/EmoLLM/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/SmartflowAI/EmoLLM.svg?style=flat-square
[forks-url]: https://github.com/SmartflowAI/EmoLLM/network/members
[stars-shield]: https://img.shields.io/github/stars/SmartflowAI/EmoLLM.svg?style=flat-square
[stars-url]: https://github.com/SmartflowAI/EmoLLM/stargazers
[issues-shield]: https://img.shields.io/github/issues/SmartflowAI/EmoLLM.svg?style=flat-square
[issues-url]: https://img.shields.io/github/issues/SmartflowAI/EmoLLM.svg
[license-shield]: https://img.shields.io/github/license/SmartflowAI/EmoLLM.svg?style=flat-square
[license-url]: https://github.com/SmartFlowAI/EmoLLM/blob/main/LICENSE
[OpenXLab_App-image]: https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg
[OpenXLab_Model-image]: https://cdn-static.openxlab.org.cn/header/openxlab_models.svg
[OpenXLab_App-url]: https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0
[OpenXLab_Model-url]: https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full
## 交流群
- 如果失效，请移步Issue区
<p align="center">
<img width="30%" src="https://private-user-images.githubusercontent.com/8240984/324394775-c8e83dac-9ed9-4a19-bb7f-b6bbedc109d9.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.yfBwgthq3zvmWD2givTJl5w3SMm4O5BeEFwidgG1WpY" alt="EmoLLM官方交流群">
</p>

8
added_tokens.json Normal file
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@@ -0,0 +1,8 @@
{
"[UNUSED_TOKEN_141]": 92544,
"[UNUSED_TOKEN_142]": 92545,
"[UNUSED_TOKEN_143]": 92546,
"[UNUSED_TOKEN_144]": 92547,
"[UNUSED_TOKEN_145]": 92548,
"[UNUSED_TOKEN_146]": 92549
}

37
config.json Normal file
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@@ -0,0 +1,37 @@
{
"_name_or_path": "./internlm2_5-7b-chat/",
"architectures": [
"InternLM2ForCausalLM"
],
"attn_implementation": "eager",
"auto_map": {
"AutoConfig": "configuration_internlm2.InternLM2Config",
"AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
"AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
},
"bias": false,
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "internlm2",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"factor": 2.0,
"type": "dynamic"
},
"rope_theta": 1000000,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.42.3",
"use_cache": true,
"vocab_size": 92544
}

1
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@@ -0,0 +1 @@
{"framework":"Pytorch","task":"text-generation"}

180
configuration_internlm2.py Normal file
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@@ -0,0 +1,180 @@
# coding=utf-8
# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" InternLM2 model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
# Modified from transformers.model.llama.configuration_llama.LlamaConfig
class InternLM2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`InternLM2Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. InternLM2 supports up to 32768 tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism)
to understand more about it. This value is necessary to ensure exact reproducibility
of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
"""
_auto_class = "AutoConfig"
model_type = "internlm2"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__( # pylint: disable=W0102
self,
vocab_size=103168,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
bias=True,
rope_theta=10000,
rope_scaling=None,
attn_implementation=None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.bias = bias
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.attn_implementation = attn_implementation
if self.attn_implementation is None:
self.attn_implementation = "eager"
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if (
rope_scaling_factor is None
or not isinstance(rope_scaling_factor, (float, int))
or rope_scaling_factor < 1.0
):
raise ValueError(
f"`rope_scaling`'s factor field must be a number >= 1, got {rope_scaling_factor} "
f"of type {type(rope_scaling_factor)}"
)

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{
"bos_token_id": 1,
"eos_token_id": [
2,
92542
],
"pad_token_id": 2,
"transformers_version": "4.42.3"
}

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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|action_start|>",
"<|action_end|>",
"<|interpreter|>",
"<|plugin|>"
],
"bos_token": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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# coding=utf-8
# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for InternLM."""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
PRETRAINED_VOCAB_FILES_MAP = {}
# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
class InternLM2Tokenizer(PreTrainedTokenizer):
"""
Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
model_input_names = ["input_ids", "attention_mask"]
_auto_class = "AutoTokenizer"
def __init__(
self,
vocab_file,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token="</s>",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
add_bos_token=True,
add_eos_token=False,
decode_with_prefix_space=False,
clean_up_tokenization_spaces=False,
**kwargs,
):
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
self.vocab_file = vocab_file
self.add_bos_token = add_bos_token
self.add_eos_token = add_eos_token
self.decode_with_prefix_space = decode_with_prefix_space
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
self._no_prefix_space_tokens = None
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
@property
def no_prefix_space_tokens(self):
if self._no_prefix_space_tokens is None:
vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
return self._no_prefix_space_tokens
@property
def vocab_size(self):
"""Returns vocab size"""
return self.sp_model.get_piece_size()
@property
def bos_token_id(self) -> Optional[int]:
return self.sp_model.bos_id()
@property
def eos_token_id(self) -> Optional[int]:
return self.sp_model.eos_id()
def get_vocab(self):
"""Returns vocab as a dict"""
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text):
"""Returns a tokenized string."""
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
token = self.sp_model.IdToPiece(index)
return token
def _maybe_add_prefix_space(self, tokens, decoded):
if tokens and tokens[0] not in self.no_prefix_space_tokens:
return " " + decoded
else:
return decoded
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
out_string = self.clean_up_tokenization(out_string)
out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
return out_string[1:]
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
Returns:
`Tuple(str)`: Paths to the files saved.
"""
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
if self.add_bos_token:
bos_token_ids = [self.bos_token_id]
else:
bos_token_ids = []
output = bos_token_ids + token_ids_0
if token_ids_1 is not None:
output = output + token_ids_1
if self.add_eos_token:
output = output + [self.eos_token_id]
return output
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
eos = [self.eos_token_id]
if token_ids_1 is None:
return len(token_ids_0 + eos) * [0]
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]

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# coding=utf-8
# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization Fast class for InternLM."""
import os
from shutil import copyfile
from typing import Any, Dict, Optional, Tuple
from tokenizers import processors, decoders, Tokenizer, normalizers
from tokenizers.models import BPE
from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
from transformers.utils import logging
from transformers.convert_slow_tokenizer import (
SLOW_TO_FAST_CONVERTERS,
SpmConverter,
SentencePieceExtractor,
)
from .tokenization_internlm2 import InternLM2Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
# Modified from transformers.convert_slow_tokenizer.LlamaConverter
class InternLM2Converter(SpmConverter):
handle_byte_fallback = True
def vocab(self, proto):
vocab = [
("<unk>", 0.0),
("<s>", 0.0),
("</s>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
return vocab
def unk_id(self, proto):
unk_id = 0
return unk_id
def decoder(self, replacement, add_prefix_space):
decoders_sequence = [
decoders.Replace("▁", " "),
decoders.ByteFallback(),
decoders.Fuse(),
]
if self.proto.normalizer_spec.add_dummy_prefix:
decoders_sequence.append(decoders.Strip(content=" ", left=1))
return decoders.Sequence(decoders_sequence)
def tokenizer(self, proto):
model_type = proto.trainer_spec.model_type
vocab_scores = self.vocab(proto)
# special tokens
added_tokens = self.original_tokenizer.added_tokens_decoder
for i in range(len(vocab_scores)):
piece, score = vocab_scores[i]
if i in added_tokens:
vocab_scores[i] = (added_tokens[i].content, score)
if model_type == 1:
raise RuntimeError("InternLM2 is supposed to be a BPE model!")
elif model_type == 2:
_, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
tokenizer = Tokenizer(
BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True)
)
tokenizer.add_special_tokens(
[ added_token for index, added_token in added_tokens.items()]
)
else:
raise Exception(
"You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
)
return tokenizer
def normalizer(self, proto):
normalizers_list = []
if proto.normalizer_spec.add_dummy_prefix:
normalizers_list.append(normalizers.Prepend(prepend="▁"))
normalizers_list.append(normalizers.Replace(pattern=" ", content="▁"))
return normalizers.Sequence(normalizers_list)
def pre_tokenizer(self, replacement, add_prefix_space):
return None
SLOW_TO_FAST_CONVERTERS["InternLM2Tokenizer"] = InternLM2Converter
# Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
class InternLM2TokenizerFast(PreTrainedTokenizerFast):
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = InternLM2Tokenizer
padding_side = "left"
model_input_names = ["input_ids", "attention_mask"]
_auto_class = "AutoTokenizer"
def __init__(
self,
vocab_file,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token="</s>",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
add_bos_token=True,
add_eos_token=False,
decode_with_prefix_space=False,
clean_up_tokenization_spaces=False,
**kwargs,
):
super().__init__(
vocab_file=vocab_file,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
sp_model_kwargs=sp_model_kwargs,
add_bos_token=add_bos_token,
add_eos_token=add_eos_token,
decode_with_prefix_space=decode_with_prefix_space,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
self._add_bos_token = add_bos_token
self._add_eos_token = add_eos_token
self.update_post_processor()
self.vocab_file = vocab_file
@property
def can_save_slow_tokenizer(self) -> bool:
return os.path.isfile(self.vocab_file) if self.vocab_file else False
def update_post_processor(self):
"""
Updates the underlying post processor with the current `bos_token` and `eos_token`.
"""
bos = self.bos_token
bos_token_id = self.bos_token_id
if bos is None and self.add_bos_token:
raise ValueError("add_bos_token = True but bos_token = None")
eos = self.eos_token
eos_token_id = self.eos_token_id
if eos is None and self.add_eos_token:
raise ValueError("add_eos_token = True but eos_token = None")
single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
special_tokens = []
if self.add_bos_token:
special_tokens.append((bos, bos_token_id))
if self.add_eos_token:
special_tokens.append((eos, eos_token_id))
self._tokenizer.post_processor = processors.TemplateProcessing(
single=single, pair=pair, special_tokens=special_tokens
)
@property
def add_eos_token(self):
return self._add_eos_token
@property
def add_bos_token(self):
return self._add_bos_token
@add_eos_token.setter
def add_eos_token(self, value):
self._add_eos_token = value
self.update_post_processor()
@add_bos_token.setter
def add_bos_token(self, value):
self._add_bos_token = value
self.update_post_processor()
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer."
)
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)

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version https://git-lfs.github.com/spec/v1
oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
size 1477754

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