diff --git a/docker_sample/Dockerfile b/docker_sample/Dockerfile
index d857bc3..47eb57f 100644
--- a/docker_sample/Dockerfile
+++ b/docker_sample/Dockerfile
@@ -11,7 +11,9 @@ RUN apt-get update && \
     && rm -rf /var/lib/apt/lists/*
 
 # vLLMのインストール
-RUN pip install --no-cache-dir vllm
+RUN pip install --no-cache-dir vllm nvidia-ml-py && \
+    pip uninstall --yes pynvml && \
+    pip install -U --no-cache-dir opencv-python-headless==4.5.5.62
 
 # モデルファイルをイメージに取り込む (FIXME: モデルファイルのパスを変更)
 COPY ./models/llm-jp/llm-jp-3-1.8b-instruct /workspace/model
diff --git a/docker_sample/README.md b/docker_sample/README.md
index 11c8dac..15bcd96 100644
--- a/docker_sample/README.md
+++ b/docker_sample/README.md
@@ -72,7 +72,6 @@ sudo docker load < [チームID].tar
 
 ```bash
 docker run --rm \
-  --runtime=nvidia \
   --gpus all \
   --network none \
   -v "$(pwd)/data:/data" \
diff --git a/docker_sample/src/main.py b/docker_sample/src/main.py
index 78c5d20..effbc40 100644
--- a/docker_sample/src/main.py
+++ b/docker_sample/src/main.py
@@ -3,6 +3,8 @@
 import argparse
 from vllm import LLM
 
+# see more examples at https://github.com/vllm-project/vllm/tree/main/examples
+chat_template = "{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}\n\n{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ message['content']|trim -}}\n{% if not loop.last %}\n\n\n{% endif %}\n{% elif message['role'] == 'assistant' %}\n{{ message['content']|trim -}}\n{% if not loop.last %}\n\n\n{% endif %}\n{% elif message['role'] == 'user_context' %}\n{{ message['content']|trim -}}\n{% if not loop.last %}\n\n\n{% endif %}\n{% endif %}\n{% endfor %}\n"
 
 def main():
     parser = argparse.ArgumentParser()
@@ -26,9 +28,9 @@ def main():
     for d in data:
         messages_list.append({"role": "user", "content": d["text"]})
 
-    outputs = llm.chat(messages_list)
-    for i, output in enumerate(outputs):
-        data[i]["response"] = output.outputs[0].text
+    for i, m in enumerate(messages_list):
+        output = llm.chat([m], chat_template=chat_template)
+        data[i]["response"] = output[0].outputs[0].text.strip()
 
     os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
     with open(args.output_path, "w", encoding="utf-8") as f: