首先可以参考modelScope社区给出的使用文档，已经足够全面
https://modelscope.cn/models/qwen/Qwen-7B-Chat/quickstart
但在按照文档中步骤部署时，还是有些错误问题发生，可以搜索参考的解决方式不多，所以记录下来
个人电脑部署
这里不太建议使用自己的笔记本部署通义千问模型，因为实在是太耗资源，我使用的M2芯片的MacBook Pro即使运行起来了，但模型回答一个问题都需要四五分钟的时间，内存全部占满，其他应用程序也都强制退出了。所以还是使用社区提供的免费资源，或者有更高配置的服务器来部署模型。而且期间还有各种问题，搜了很多github上的问答才解决，耗时耗力，这里就不记录了，很不推荐这种方式。
免费算力服务器
打开modelScope社区后，点击登录注册可以看到免费赠送算力的活动
注册完成后在对应模型里可以看到，随时都能启用的服务器
这里CPU环境的服务器勉强可以跑起来模型，但运行效果感人，而且配置过程中有各种问题需要修改，而GPU环境启动模型可以说是非常流畅，体验效果也很好
CPU环境启动
社区提供的服务器配置已经很高了，8核32G，但因为是纯CPU环境，启动过程中还是有些问题
安装依赖包
第一行命令不需要运行，服务器已经自带了modelscope包
只需要新建一个Terminal窗口来执行第二条命令
启动代码
直接运行文档提供的代码会报错，这里是因为纯CPU环境导致的
错误 1
RuntimeError: "addmm_implcpu" not implemented for 'Half'Hide Error Details
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 |
RuntimeError: "addmm_impl_cpu_" not implemented for 'Half' --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) Cell In[1], line 8 5 model = AutoModelForCausalLM.from_pretrained("qwen/Qwen-7B-Chat", revision = 'v1.0.5',device_map="auto", trust_remote_code=True,fp16 = True).eval() 6 model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat",revision = 'v1.0.5', trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参 ----> 8 response, history = model.chat(tokenizer, "你好", history=None) 9 print(response) 10 response, history = model.chat(tokenizer, "浙江的省会在哪里？", history=history) File ~/.cache/huggingface/modules/transformers_modules/Qwen-7B-Chat/modeling_qwen.py:1010, in QWenLMHeadModel.chat(self, tokenizer, query, history, system, append_history, stream, stop_words_ids, **kwargs) 1006 stop_words_ids.extend(get_stop_words_ids( 1007 self.generation_config.chat_format, tokenizer 1008 )) 1009 input_ids = torch.tensor([context_tokens]).to(self.device) -> 1010 outputs = self.generate( 1011 input_ids, 1012 stop_words_ids = stop_words_ids, 1013 return_dict_in_generate = False, 1014 **kwargs, 1015 ) 1017 response = decode_tokens( 1018 outputs[0], 1019 tokenizer, (...) 1024 errors='replace' 1025 ) 1027 if append_history: |
错误 2
ValueError: The current device_map had weights offloaded to the disk. Please provide an offload_folder for them. Alternatively, make sure you have safetensors installed if the model you are using offers the weights in this format.Hide Error Details
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |
ValueError: The current `device_map` had weights offloaded to the disk. Please provide an `offload_folder` for them. Alternatively, make sure you have `safetensors` installed if the model you are using offers the weights in this format. --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[2], line 5 2 from modelscope import GenerationConfig 4 tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen-7B-Chat", revision = 'v1.0.5',trust_remote_code=True) ----> 5 model = AutoModelForCausalLM.from_pretrained("qwen/Qwen-7B-Chat", revision = 'v1.0.5',device_map="auto", trust_remote_code=True,fp16 = True).eval() 6 model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat",revision = 'v1.0.5', trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参 7 model.float() File /opt/conda/lib/python3.8/site-packages/modelscope/utils/hf_util.py:98, in get_wrapped_class.<locals>.ClassWrapper.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) 95 else: 96 model_dir = pretrained_model_name_or_path ---> 98 model = module_class.from_pretrained(model_dir, *model_args, 99 **kwargs) 100 model.model_dir = model_dir 101 return model |
解决方式
首先确保torch 2.0.1版本，然后在代码中添加这两行，即可运行
model.float()
offload_folder="offload_folder",
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 |
from modelscope import AutoModelForCausalLM, AutoTokenizer from modelscope import GenerationConfig import datetime print("启动时间：" + str(datetime.datetime.now())) tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen-7B-Chat", revision = 'v1.0.5',trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("qwen/Qwen-7B-Chat", revision = 'v1.0.5',device_map="auto",offload_folder="offload_folder", trust_remote_code=True,fp16 = True).eval() model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat",revision = 'v1.0.5', trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参 model.float() print("开始执行：" + str(datetime.datetime.now())) response, history = model.chat(tokenizer, "你好", history=None) print(response) print("第一个问题处理完毕：" + str(datetime.datetime.now())) response, history = model.chat(tokenizer, "浙江的省会在哪里？", history=history) print(response) print("第二个问题处理完毕：" + str(datetime.datetime.now())) response, history = model.chat(tokenizer, "它有什么好玩的景点", history=history) print(response) print("第三个问题处理完毕：" + str(datetime.datetime.now())) |
运行起来之后速度实在感人，没回答一个问题都需要 5 分钟左右，还有一定概率直接启动失败
启动模型过程中会出现这种报错，点击OK重新执行就好了，可能是服务器负载太高
