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README.md
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@ -230,6 +230,7 @@ git clone https://github.com/SmartFlowAI/EmoLLM.git
| [SantiagoTOP](https://github.com/santiagoTOP) | 太原理工大学在读硕士 | | | | [SantiagoTOP](https://github.com/santiagoTOP) | 太原理工大学在读硕士 | | |
| [zealot52099](https://github.com/zealot52099) | AI搬用工 | |清洗数据、RAG| | [zealot52099](https://github.com/zealot52099) | AI搬用工 | |清洗数据、RAG|
| [wwwyfff](https://github.com/wwwyfff) | 复旦大学在读硕士 | || | [wwwyfff](https://github.com/wwwyfff) | 复旦大学在读硕士 | ||
| [jkhumor](https://github.com/jkhumor) | 南开大学在读硕士 | |RAG|
### 版权说明 ### 版权说明

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README_EN.md
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@ -246,6 +246,7 @@ This project uses Git for version control. You can see the currently available v
| [SantiagoTOP](https://github.com/santiagoTOP) | Taiyuan University of Technology, Master's student | | | | [SantiagoTOP](https://github.com/santiagoTOP) | Taiyuan University of Technology, Master's student | | |
| [zealot52099](https://github.com/zealot52099) | AI Mover | |Data Processing and RAG| | [zealot52099](https://github.com/zealot52099) | AI Mover | |Data Processing and RAG|
| [wwwyfff](https://github.com/wwwyfff) | FuDan University, Master's student | || | [wwwyfff](https://github.com/wwwyfff) | FuDan University, Master's student | ||
| [jkhumor](https://github.com/jkhumor) | Nankai University, Master's student | |RAG|
### Copyright Notice ### Copyright Notice

3
xtuner_config/ChatGLM3-6b-ft.md
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@ -65,8 +65,7 @@ LLM 的微调一般指指令微调过程。所谓指令微调，是说我们使
def process_func(example): def process_func(example):
MAX_LENGTH = 512 MAX_LENGTH = 512
input_ids, labels = [], [] input_ids, labels = [], []
instruction = tokenizer.encode(text="\n".join(["<|system|>", "现在你是一个心理专家，我有一些心理问题，请你用专业的知识帮我解决。", "<|user|>", instruction = tokenizer.encode(text="\n".join(["<|system|>", example["system"], "<|user|>", example["input"] + "<|assistant|>"]).strip() + "\n",
example["system"] + example["input"] + "<|assistant|>"]).strip() + "\n",
add_special_tokens=True, truncation=True, max_length=MAX_LENGTH) add_special_tokens=True, truncation=True, max_length=MAX_LENGTH)
response = tokenizer.encode(text=example["output"], add_special_tokens=False, truncation=True, response = tokenizer.encode(text=example["output"], add_special_tokens=False, truncation=True,