LLM- 结合三元组 SPO 和提示工程来试用 Baichuan2-7B-Chat-4bits 模型

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom transformers.generation.utils import GenerationConfigimport os
# 获取当前文件所在的目录路径current_dir = os.path.dirname(os.path.abspath(__file__))# 将当前目录和'model'连接起来，获得'model'文件夹的完整路径model_path = os.path.join(current_dir, 'model/Baichuan2-7B-Chat-4bits')print('model_path=', model_path)
if __name__ == '__main__':tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)model.generation_config = GenerationConfig.from_pretrained(model_path)messages = []messages.append({"role": "user", "content": "解释一下“温故而知新”"})response = model.chat(tokenizer, messages)print(response)

git clone https://github.com/baichuan-inc/Baichuan2.git
cd Baichuan2
pip install -r requirements.txt
## 处理安装包的兼容问题，Baichuan2-7B-Chat-4bitspip install bitsandbytes==0.41.1pip install accelerate-0.25.0
## 同样的可以将如上的python脚本放到 ${Baichuan2} 文件夹下。## 匹配如上的python脚本，需要下载模型文件到 ${Baichuan2}/model/Baichuan2-7B-Chat-4bits 文件夹下。