diff --git a/Dockerfile b/Dockerfile
index e29e4d17..8107ebcb 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -37,7 +37,7 @@ RUN pip install flash-linear-attention -U --no-cache-dir
 RUN pip install numpy==2.2 --no-cache-dir
 
 # Install tinker, ray, and other deps
-RUN pip install --no-cache-dir tinker==0.16.1 "ray[serve]" transformers peft accelerate -U
+RUN pip install --no-cache-dir tinker==0.16.1 "ray[serve]" transformers peft<=0.18 accelerate -U
 
 # Clone and install twinkle, checkout to latest v-tag
 RUN git clone https://github.com/modelscope/twinkle.git
diff --git a/cookbook/sample/sample.py b/cookbook/sample/sample.py
new file mode 100644
index 00000000..b56460ea
--- /dev/null
+++ b/cookbook/sample/sample.py
@@ -0,0 +1,109 @@
+"""
+Standalone inference example using Ray + vLLMSampler with LoRA adapter.
+
+This script demonstrates how to:
+1. Initialize Twinkle with Ray for distributed inference
+2. Create a vLLMSampler with LoRA enabled on dedicated GPUs
+3. Load a LoRA adapter from a local checkpoint path
+4. Send prompts (Trajectory format) and collect generated responses
+
+Usage:
+    # Single GPU inference
+    SAMPLER_GPUS=1 python sample.py
+
+    # Multi-GPU inference (tensor parallel)
+    SAMPLER_GPUS=2 python sample.py
+
+    # Use a different model / adapter
+    MODEL_ID=/path/to/model LORA_PATH=/path/to/adapter SAMPLER_GPUS=1 python sample.py
+"""
+
+import os
+from typing import List, Dict, Any
+
+import twinkle
+from twinkle import DeviceMesh, DeviceGroup, get_device_placement, get_logger
+from twinkle.data_format import SamplingParams
+from twinkle.sampler import vLLMSampler
+
+logger = get_logger()
+
+MODEL_ID = os.environ.get('MODEL_ID', 'Qwen/Qwen3.5-4B')
+LORA_PATH = os.environ.get('LORA_PATH', '/path/to/lora')
+SAMPLER_GPUS = int(os.environ.get('SAMPLER_GPUS', 1))
+
+
+def build_prompts() -> List[Dict[str, Any]]:
+    """Build a list of Trajectory dicts (messages format) as prompts."""
+    prompts = [
+        {
+            'messages': [
+                {'role': 'system', 'content': 'You are a helpful assistant.'},
+                {'role': 'user', 'content': 'What is the capital of France?'},
+            ]
+        },
+        {
+            'messages': [
+                {'role': 'system', 'content': 'You are a helpful assistant.'},
+                {'role': 'user', 'content': 'Write a short poem about the moon.'},
+            ]
+        },
+        {
+            'messages': [
+                {'role': 'user', 'content': 'Solve: 2x + 3 = 11. What is x?'},
+            ]
+        },
+    ]
+    return prompts
+
+
+def main():
+    # ── 1. Initialize Twinkle with Ray ──────────────────────────────────
+    device_groups = [
+        DeviceGroup(name='sampler', ranks=list(range(SAMPLER_GPUS)), device_type='GPU', gpus_per_worker=SAMPLER_GPUS),
+    ]
+    sampler_mesh = DeviceMesh.from_sizes(world_size=SAMPLER_GPUS, tp_size=SAMPLER_GPUS)
+    twinkle.initialize(mode='ray', nproc_per_node=SAMPLER_GPUS, groups=device_groups)
+
+    # ── 2. Create vLLMSampler with LoRA enabled ────────────────────────
+    sampler = vLLMSampler(
+        model_id=MODEL_ID,
+        engine_args={
+            'gpu_memory_utilization': 0.7,
+            'max_model_len': 4096,
+            'enable_lora': True,
+            'max_loras': 1,
+            'max_lora_rank': 32,
+            'enable_tower_connector_lora': True,
+        },
+        device_mesh=sampler_mesh,
+        remote_group='sampler',
+    )
+    sampler.set_template('Qwen3_5Template', model_id=MODEL_ID)
+    logger.info(get_device_placement())
+
+    # ── 3. Configure sampling parameters ────────────────────────────────
+    sampling_params = SamplingParams(
+        max_tokens=2018,
+        temperature=0.7,
+        top_p=0.9,
+        num_samples=1,
+    )
+
+    # ── 4. Run inference ────────────────────────────────────────────────
+    prompts = build_prompts()
+    logger.info(f'Sampling {len(prompts)} prompts with model {MODEL_ID} ...')
+
+    responses = sampler.sample(prompts, sampling_params, adapter_path=LORA_PATH)
+
+    # ── 5. Print results ────────────────────────────────────────────────
+    for i, response in enumerate(responses):
+        for seq in response.sequences:
+            text = sampler.template.tokenizer.decode(seq.tokens, skip_special_tokens=True)
+            logger.info(f'\n{"="*60}\nPrompt {i}: {prompts[i]["messages"][-1]["content"]}\n{"─"*60}\n{text}\n')
+
+    logger.info('Done.')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/src/twinkle/model/megatron/strategy/megatron.py b/src/twinkle/model/megatron/strategy/megatron.py
index b9e66505..9d03bb87 100644
--- a/src/twinkle/model/megatron/strategy/megatron.py
+++ b/src/twinkle/model/megatron/strategy/megatron.py
@@ -187,6 +187,7 @@ def _wrap_with_megatron_ddp(
 
         wrapped_models = []
         for _model in model:
+            _model = MegatronStrategy._move_model_to_gpu(_model)
             config: TransformerConfig = _model.config  # noqa
 
             if not isinstance(model, Float16Module) and (config.fp16 or config.bf16):