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README.md

@@ -65,7 +65,7 @@ For more information about the pipeline parameters, please refer to [here](https
 LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:
 
 ```shell
-lmdeploy serve api_server OpenGVLab/InternVL-Chat-V1-5-AWQ --model-name InternVL-Chat-V1-5-AWQ --backend turbomind --server-port 23333 --model-format awq
+lmdeploy serve api_server OpenGVLab/InternVL-Chat-V1-5-AWQ --backend turbomind --server-port 23333 --model-format awq
 ```
 
 To use the OpenAI-style interface, you need to install OpenAI:
@@ -82,7 +82,7 @@ from openai import OpenAI
 client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
 model_name = client.models.list().data[0].id
 response = client.chat.completions.create(
- model="InternVL-Chat-V1-5-AWQ",
+ model=model_name,
  messages=[{
  'role':
  'user',
@@ -104,7 +104,7 @@ print(response)
 
 ## License
 
-This project is released under the MIT license, while InternLM is licensed under the Apache-2.0 license.
+This project is released under the MIT license, while InternLM2 is licensed under the Apache-2.0 license.
 
 ## Citation
 

config.json

@@ -8,6 +8,7 @@
  "AutoModel": "modeling_internvl_chat.InternVLChatModel",
  "AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"
  },
+ "system_message": "You are an AI assistant whose name is InternLM (书生·浦语).",
  "downsample_ratio": 0.5,
  "dynamic_image_size": true,
  "force_image_size": 448,