搭建步骤：

1. 拷贝本地模型，把下载好的Baichuan2-7B-Chat拷贝到models目录下

2. 修改modules\models\base_model.py文件，class ModelType增加Baichuan

   Baichuan = 16 elif "baichuan" in model_name_lower:    model_type = ModelType.Baichuan

3. 修改modules\models\models.py文件，get_model方法增加ModelType.Baichuan

   elif model_type == ModelType.Baichuan:     from .Baichuan import Baichuan_Client     model = Baichuan_Client(model_name, user_name=user_name)

4. 增加modules\models\Baichuan.py文件

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   from modelscope import snapshot_download, AutoModelForCausalLM, AutoTokenizer,GenerationConfig from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation import GenerationConfig import logging import colorama from ..index_func import * from ..presets import * from ..utils import * from .base_model import BaseLLMModel from ..presets import MODEL_METADATA from datetime import datetime class Baichuan_Client(BaseLLMModel):     def __init__(self, model_name, user_name="") -> None:         super().__init__(model_name=model_name, user=user_name)         import torch         from transformers import AutoModel, AutoTokenizer         global CHATGLM_TOKENIZER, CHATGLM_MODEL         print("__init__ Baichuan_Client")         if CHATGLM_TOKENIZER is None or CHATGLM_MODEL is None:             model_path = None             if os.path.exists("models"):                 model_dirs = os.listdir("models")                 if model_name in model_dirs:                     model_path = f"models/{model_name}"             if model_path is not None:                 model_source = model_path             else:                 model_source = snapshot_download(f"baichuan-inc/{model_name}", revision='v1.0.4')             CHATGLM_TOKENIZER = AutoTokenizer.from_pretrained(                 model_source, device_map="auto", trust_remote_code=True, torch_dtype=torch.float16             )             quantified = False             if "int4" in model_name:                 quantified = True             model = AutoModelForCausalLM.from_pretrained(                 model_source, device_map="auto", trust_remote_code=True, torch_dtype=torch.float16             )             model.generation_config = GenerationConfig.from_pretrained(model_source)             model = model.eval()             CHATGLM_MODEL = model     def _get_glm_style_input(self):         print("_get_glm_style_input")         print(f"the history is: {self.history}")         history = [x["content"] for x in self.history]         query = history.pop()         print(f"the message is: {query}")         return history, query     def get_answer_at_once(self):         print("get_answer_at_once")         history,query = self._get_glm_style_input()         messages = []         messages.append({'role': 'user', 'content': query})         now = datetime.now()         print("get_answer_at_once start"+"++++++++"+ now.strftime("%Y-%m-%d %H:%M:%S"))         response = CHATGLM_MODEL.chat(             CHATGLM_TOKENIZER, messages)         now = datetime.now()         print("get_answer_at_once end"+"++++++++"+ now.strftime("%Y-%m-%d %H:%M:%S"))         print(f"the response is: {response}")         return response, len(response)     def get_answer_stream_iter(self):         history,query = self._get_glm_style_input()         messages = []         messages.append({'role': 'user', 'content': query})         result = ""         now = datetime.now()         print("get_answer_stream_iter start"+"++++++++"+ now.strftime("%Y-%m-%d %H:%M:%S"))         for response in CHATGLM_MODEL.chat(             CHATGLM_TOKENIZER,             messages         ):             print(f"the response is: {response}")             result += response             yield result         now = datetime.now()         print("get_answer_stream_iter end"+"++++++++"+ now.strftime("%Y-%m-%d %H:%M:%S"))

5. 答案回调开关控制get_answer_at_once、get_answer_stream_iter方法调用选择
   
6. 执行效果