Upload folder using huggingface_hub

README.md

@@ -576,7 +576,7 @@ To deploy InternVL2 as an API, please configure the chat template config first.
 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/InternVL2-2B --model-name InternVL2-2B --backend turbomind --server-port 23333 --chat-template chat_template.json
+lmdeploy serve api_server OpenGVLab/InternVL2-2B --backend turbomind --server-port 23333 --chat-template chat_template.json
 ```
 
 To use the OpenAI-style interface, you need to install OpenAI:
@@ -593,7 +593,7 @@ 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="InternVL2-2B",
+    model=model_name,
     messages=[{
         'role':
         'user',
@@ -872,7 +872,7 @@ print(sess.response.text)
 LMDeploy 的 `api_server` 使模型能够通过一个命令轻松打包成服务。提供的 RESTful API 与 OpenAI 的接口兼容。以下是服务启动的示例：
 
 ```shell
-lmdeploy serve api_server OpenGVLab/InternVL2-2B --model-name InternVL2-2B --backend turbomind --server-port 23333 --chat-template chat_template.json
+lmdeploy serve api_server OpenGVLab/InternVL2-2B  --backend turbomind --server-port 23333 --chat-template chat_template.json
 ```
 
 为了使用OpenAI风格的API接口，您需要安装OpenAI:
@@ -889,7 +889,7 @@ 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="InternVL2-2B",
+    model=model_name,
     messages=[{
         'role':
         'user',