diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..6d86dd6
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,12 @@
+FROM git.modelhub.org.cn:9443/enginex-iluvatar-bi150/vllm:0.8.3
+
+WORKDIR /workspace
+
+COPY requirements.txt /workspace
+RUN pip install --no-cache-dir -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+ADD . /workspace
+
+EXPOSE 80
+CMD ["sh","-c","python3 transformers_server.py"]
+
diff --git a/cats_image.jpeg b/cats_image.jpeg
new file mode 100644
index 0000000..e131e8e
Binary files /dev/null and b/cats_image.jpeg differ
diff --git a/docker.sh b/docker.sh
new file mode 100755
index 0000000..8e8c70c
--- /dev/null
+++ b/docker.sh
@@ -0,0 +1,6 @@
+docker stop bi150_ic
+docker rm bi150_ic
+
+docker build . -t bi150_image_classification
+
+docker run -p 17777:8000 -v /mnt/contest_ceph/aiyueqi/image_classification/microsoft/resnet-50/:/model:ro -it --device=/dev/iluvatar0:/dev/iluvatar0  --name bi150_ic -e CONFIG_JSON='{"model_class": "AutoModelForImageClassification", "processer": "AutoImageProcessor", "torch_dtype": "auto"}' bi150_image_classification
diff --git a/logger.py b/logger.py
new file mode 100644
index 0000000..d238e70
--- /dev/null
+++ b/logger.py
@@ -0,0 +1,13 @@
+# -*- coding: utf-8 -*-
+import logging
+import os
+
+logging.basicConfig(
+    format="%(asctime)s %(name)-12s %(levelname)-4s %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO"),
+)
+
+def get_logger(file):
+    return logging.getLogger(file)
+
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..97dc7cd
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,2 @@
+fastapi
+uvicorn
diff --git a/transformers_server.py b/transformers_server.py
new file mode 100644
index 0000000..ebdb7af
--- /dev/null
+++ b/transformers_server.py
@@ -0,0 +1,128 @@
+import base64
+import json
+import os
+from io import BytesIO
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from PIL import Image
+import torch
+import transformers
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import uvicorn
+
+import logger
+logger = logger.get_logger(__file__)
+
+app = FastAPI()
+initialized = False
+processor = None
+model = None
+device = None
+
+class ClassifyRequest(BaseModel):
+    image: str
+    image_name: str | None = None
+
+def load_config():
+    default_config = {
+        "model_class": "AutoModelForImageClassification",
+        "processer_class": "AutoImageProcessor",
+        "torch_dtype": "auto",
+    }
+
+    raw_config = os.getenv("CONFIG_JSON")
+    if not raw_config:
+        return default_config
+
+    try:
+        user_config = json.loads(raw_config)
+    except json.JSONDecodeError as exc:
+        raise ValueError("CONFIG_JSON is not valid JSON") from exc
+
+    if not isinstance(user_config, dict):
+        raise ValueError("CONFIG_JSON must decode to an object")
+
+    default_config.update(user_config)
+    if "processor_class" in user_config and "processer_class" not in user_config:
+        default_config["processer_class"] = user_config["processor_class"]
+
+    return default_config
+
+
+def resolve_transformers_class(class_name):
+    try:
+        return getattr(transformers, class_name)
+    except AttributeError as exc:
+        raise ValueError(f"unsupported transformers class: {class_name}") from exc
+
+
+def resolve_torch_dtype(dtype_name):
+    if dtype_name == "auto":
+        return "auto"
+
+    try:
+        return getattr(torch, dtype_name)
+    except AttributeError as exc:
+        raise ValueError(f"unsupported torch dtype: {dtype_name}") from exc
+
+@app.get("/")
+def read_root():
+    return {"message": "Hello, World!"}
+
+@app.on_event("startup")
+def load_model():
+    logger.info("loading model")
+    global initialized, processor, model, device
+
+    config = load_config()
+    processor_class = resolve_transformers_class(config["processer_class"])
+    model_class = resolve_transformers_class(config["model_class"])
+    torch_dtype = resolve_torch_dtype(config["torch_dtype"])
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    logger.info(
+        f"model config: model_class={config['model_class']}, "
+        f"processer_class={config['processer_class']}, torch_dtype={config['torch_dtype']}, "
+        f"device={device}"
+    )
+
+    processor = processor_class.from_pretrained("/model")
+    model = model_class.from_pretrained("/model", torch_dtype=torch_dtype)
+    model.to(device)
+    model.eval()
+
+    initialized = True
+    logger.info("model loaded successfully")
+
+@app.get("/v1/models")
+async def get_status():
+    logger.info(f"get status, initialized={initialized}")
+    return initialized
+
+@app.post("/v1/classify")
+async def classify(request: ClassifyRequest):
+    if not initialized or processor is None or model is None or device is None:
+        raise HTTPException(status_code=503, detail="model is not initialized")
+
+    try:
+        image_bytes = base64.b64decode(request.image)
+        image = Image.open(BytesIO(image_bytes)).convert("RGB")
+    except Exception as exc:
+        logger.exception("failed to decode input image")
+        raise HTTPException(status_code=400, detail="invalid image payload") from exc
+
+    try:
+        inputs = processor(images=image, return_tensors="pt")
+        inputs = {key: value.to(device) for key, value in inputs.items()}
+        with torch.no_grad():
+            logits = model(**inputs).logits
+        top5 = torch.topk(logits, k=5, dim=-1).indices[0].tolist()
+        logger.info(f"classify image_name={request.image_name}, labels={top5}")
+        return {"labels": top5}
+    except Exception as exc:
+        logger.exception("image classification failed")
+        raise HTTPException(status_code=500, detail="classification failed") from exc
+
+if __name__ == '__main__':
+    uvicorn.run("transformers_server:app", host="0.0.0.0", port=8000, workers=1, access_log=False)