change token name

Author: Yingda Chen

cookbook/client/tinker/lora.py

@@ -19,7 +19,7 @@
 # - base_url: the address of the running server
 # - api_key: authentication token (loaded from environment variable)
 service_client = init_tinker_compat_client(
-    base_url='http://www.modelscope.cn/twinkle', api_key=os.environ.get('MODELSCOPE_SDK_TOKEN'))
+    base_url='http://www.modelscope.cn/twinkle', api_key=os.environ.get('MODELSCOPE_TOKEN'))
 
 # Step 3: List models available on the server to verify the connection
 print('Available models:')

cookbook/client/tinker/sample.py

@@ -14,7 +14,7 @@
 base_model = 'Qwen/Qwen3-30B-A3B-Instruct-2507'
 service_client = init_tinker_compat_client(
     base_url='http://www.modelscope.cn/twinkle',
-    api_key=os.environ.get('MODELSCOPE_SDK_TOKEN')
+    api_key=os.environ.get('MODELSCOPE_TOKEN')
 )
 # Step 2: Create a sampling client by loading weights from a saved checkpoint.
 # The model_path is a twinkle:// URI pointing to a previously saved LoRA checkpoint.

cookbook/client/tinker/self_congnition.py

@@ -44,7 +44,7 @@ def train():
 
     # Connect to the Twinkle server running locally
     service_client = init_tinker_compat_client(
-        base_url='http://www.modelscope.cn/twinkle', api_key=os.environ.get('MODELSCOPE_SDK_TOKEN'))
+        base_url='http://www.modelscope.cn/twinkle', api_key=os.environ.get('MODELSCOPE_TOKEN'))
 
     # Create a LoRA training client for the base model (rank=16 for the LoRA adapter)
     training_client = service_client.create_lora_training_client(base_model=base_model, rank=16)

cookbook/client/tinker/short_math_grpo.py

@@ -216,7 +216,7 @@ def main():
     # Step 2: Initialize the Tinker-compatible client
     logger.info('Connecting to Tinker server...')
     service_client = init_tinker_compat_client(
-        base_url='http://www.modelscope.cn/twinkle', api_key=os.environ.get('MODELSCOPE_SDK_TOKEN'))
+        base_url='http://www.modelscope.cn/twinkle', api_key=os.environ.get('MODELSCOPE_TOKEN'))
 
     logger.info('Creating LoRA training client...')
     # Create a LoRA training client for GRPO

cookbook/client/twinkle/grpo.py

@@ -75,7 +75,7 @@ def train():
     # Step 1: Initialize the Twinkle client
     client = init_twinkle_client(
         base_url='http://127.0.0.1:8000',
-        api_key=os.environ.get('MODELSCOPE_SDK_TOKEN'),
+        api_key=os.environ.get('MODELSCOPE_TOKEN'),
     )
 
     # Step 2: Prepare dataset and dataloader

cookbook/client/twinkle/sample.py

@@ -36,7 +36,7 @@ def sample():
     # Step 2: Initialize the Twinkle client to communicate with the remote server.
     client = init_twinkle_client(
         base_url='http://127.0.0.1:8000',
-        api_key=os.environ.get('MODELSCOPE_SDK_TOKEN'),
+        api_key=os.environ.get('MODELSCOPE_TOKEN'),
     )
 
     # Step 3: Create the sampler client pointing to the model on the server

cookbook/client/twinkle/self_congnition.py

@@ -26,7 +26,7 @@
 # Step 2: Initialize the Twinkle client to communicate with the remote server.
 # - base_url: the address of the running Twinkle server
 # - api_key: authentication token (loaded from environment variable)
-client = init_twinkle_client(base_url='http://127.0.0.1:8000', api_key=os.environ.get('MODELSCOPE_SDK_TOKEN'))
+client = init_twinkle_client(base_url='http://127.0.0.1:8000', api_key=os.environ.get('MODELSCOPE_TOKEN'))
 
 # Step 3: Query the server for existing training runs and their checkpoints.
 # This is useful for resuming a previous training session.

docs/source_en/Usage Guide/Server and Client/Tinker-Compatible-Client.md

@@ -45,7 +45,7 @@ from twinkle_client import init_tinker_compat_client
 # Step 1: Initialize client (automatically patches Tinker SDK)
 service_client = init_tinker_compat_client(
     base_url='http://localhost:8000',
-    api_key=os.environ.get('MODELSCOPE_SDK_TOKEN')
+    api_key=os.environ.get('MODELSCOPE_TOKEN')
 )
 
 # Step 2: Query existing training runs (optional)

docs/source_en/Usage Guide/Server and Client/Twinkle-Client.md

@@ -76,7 +76,7 @@ logger = get_logger()
 # Step 1: Initialize client
 client = init_twinkle_client(
     base_url='http://127.0.0.1:8000',
-    api_key=os.environ.get('MODELSCOPE_SDK_TOKEN')
+    api_key=os.environ.get('MODELSCOPE_TOKEN')
 )
 
 # Step 2: Query existing training runs (optional, for resuming training)

docs/source_zh/使用指引/服务端和客户端/Tinker兼容客户端.md

@@ -45,7 +45,7 @@ from twinkle_client import init_tinker_compat_client
 # Step 1: 初始化客户端（会自动 patch Tinker SDK）
 service_client = init_tinker_compat_client(
     base_url='http://localhost:8000',
-    api_key=os.environ.get('MODELSCOPE_SDK_TOKEN')
+    api_key=os.environ.get('MODELSCOPE_TOKEN')
 )
 
 # Step 2: 查询已有训练运行（可选）