Upload folder using huggingface_hub

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,