Add a sample script (#97)

Author: tastelikefeet

cookbook/client/tinker/custom_service/sample.py

@@ -39,7 +39,7 @@
 trajectory = Trajectory(
     messages=[
         Message(role='system', content='You are a helpful assistant'),
-        Message(role='user', content='你是谁？'),
+        Message(role='user', content='Who are you?'),
     ]
 )
 
@@ -56,7 +56,7 @@
 )
 
 # Step 6: Send the sampling request to the server.
-# num_samples=8 generates 8 independent completions for the same prompt.
+# num_samples=1 generates 1 independent completions for the same prompt.
 print('Sampling...')
 future = sampling_client.sample(prompt=prompt, sampling_params=params, num_samples=1)
 result = future.result()

cookbook/client/tinker/modelscope_service/sample.py

@@ -0,0 +1,69 @@
+# Tinker-Compatible Client - Sampling / Inference Example
+#
+# This script demonstrates how to use a previously trained LoRA checkpoint
+# for text generation (sampling) via the Tinker-compatible client API.
+# The server must be running first (see server.py and server_config.yaml).
+
+import os
+from tinker import types
+
+from twinkle.data_format import Message, Trajectory
+from twinkle.template import Template
+from twinkle import init_tinker_client
+
+# Step 1: Initialize Tinker client
+init_tinker_client()
+
+from tinker import ServiceClient
+
+base_model = 'Qwen/Qwen3-30B-A3B-Instruct-2507'
+base_url = 'http://www.modelscope.cn/twinkle'
+
+# Step 2: Define the base model and connect to the server
+service_client = ServiceClient(
+    base_url=base_url,
+    api_key=os.environ.get('MODELSCOPE_TOKEN')
+)
+
+# Step 3: 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.
+# The server will load the base model and apply the LoRA adapter weights.
+sampling_client = service_client.create_sampling_client(
+    model_path='twinkle://xxx-Qwen_Qwen3-30B-A3B-Instruct-2507-xxx/weights/twinkle-lora-1',
+    base_model=base_model
+)
+
+# Step 4: Load the tokenizer locally to encode the prompt and decode the results
+print(f'Using model {base_model}')
+
+template = Template(model_id=f'ms://{base_model}')
+
+trajectory = Trajectory(
+    messages=[
+        Message(role='system', content='You are a helpful assistant'),
+        Message(role='user', content='Who are you?'),
+    ]
+)
+
+input_feature = template.encode(trajectory, add_generation_prompt=True)
+
+input_ids = input_feature['input_ids'].tolist()
+
+# Step 5: Prepare the prompt and sampling parameters
+prompt = types.ModelInput.from_ints(input_ids)
+params = types.SamplingParams(
+    max_tokens=128,       # Maximum number of tokens to generate
+    temperature=0.7,
+    stop=['\n']          # Stop generation when a newline character is produced
+)
+
+# Step 6: Send the sampling request to the server.
+# num_samples=1 generates 1 independent completions for the same prompt.
+print('Sampling...')
+future = sampling_client.sample(prompt=prompt, sampling_params=params, num_samples=1)
+result = future.result()
+
+# Step 7: Decode and print the generated responses
+print('Responses:')
+for i, seq in enumerate(result.sequences):
+    print(f'{i}: {repr(template.decode(seq.tokens))}')