chat_template.md

Customized chat template

The effect of the applied chat template can be observed by setting log level INFO.

LMDeploy supports two methods of adding chat templates:

• One approach is to utilize an existing conversation template by directly configuring a JSON file like the following.

  {
      "model_name": "your awesome chat template name",
      "system": "<|im_start|>system\n",
      "meta_instruction": "You are a robot developed by LMDeploy.",
      "eosys": "<|im_end|>\n",
      "user": "<|im_start|>user\n",
      "eoh": "<|im_end|>\n",
      "assistant": "<|im_start|>assistant\n",
      "eoa": "<|im_end|>",
      "separator": "\n",
      "capability": "chat",
      "stop_words": ["<|im_end|>"]
  }

  model_name is a required field and can be either the name of an LMDeploy built-in chat template (which can be viewed through lmdeploy list), or a new name. Other fields are optional.

  1. When model_name is the name of a built-in chat template, the non-null fields in the JSON file will override the corresponding attributes of the original chat template.
  2. However, when model_name is a new name, it will register BaseChatTemplate directly as a new chat template. The specific definition can be referred to BaseChatTemplate.

  The new chat template would be like this:

  {system}{meta_instruction}{eosys}{user}{user_content}{eoh}{assistant}{assistant_content}{eoa}{separator}{user}...
  

  When using the CLI tool, you can pass in a custom chat template with --chat-template, for example.

  lmdeploy serve api_server internlm/internlm2_5-7b-chat --chat-template ${JSON_FILE}

  You can also pass it in through the interface function, for example.

  from lmdeploy import ChatTemplateConfig, serve
  serve('internlm/internlm2_5-7b-chat',
        chat_template_config=ChatTemplateConfig.from_json('${JSON_FILE}'))

• Another approach is to customize a Python chat template class like the existing LMDeploy chat templates. It can be used directly after successful registration. The advantages are a high degree of customization and strong controllability. Below is an example of registering an LMDeploy chat template.

  from lmdeploy.model import MODELS, BaseChatTemplate
  
  
  @MODELS.register_module(name='customized_model')
  class CustomizedModel(BaseChatTemplate):
      """A customized chat template."""
  
      def __init__(self,
                   system='<|im_start|>system\n',
                   meta_instruction='You are a robot developed by LMDeploy.',
                   user='<|im_start|>user\n',
                   assistant='<|im_start|>assistant\n',
                   eosys='<|im_end|>\n',
                   eoh='<|im_end|>\n',
                   eoa='<|im_end|>',
                   separator='\n',
                   stop_words=['<|im_end|>', '<|action_end|>']):
          super().__init__(system=system,
                           meta_instruction=meta_instruction,
                           eosys=eosys,
                           user=user,
                           eoh=eoh,
                           assistant=assistant,
                           eoa=eoa,
                           separator=separator,
                           stop_words=stop_words)
  
  
  from lmdeploy import ChatTemplateConfig, pipeline
  
  messages = [{'role': 'user', 'content': 'who are you?'}]
  pipe = pipeline('internlm/internlm2_5-7b-chat',
                  chat_template_config=ChatTemplateConfig('customized_model'))
  for response in pipe.stream_infer(messages):
      print(response.text, end='')

  In this example, we register a LMDeploy chat template that sets the model to be created by LMDeploy, so when the user asks who the model is, the model will answer that it was created by LMDeploy.