diff --git a/basic_demo/fastapi_call_4v_9b.py b/basic_demo/fastapi_call_4v_9b.py
new file mode 100644
index 0000000..d18d281
--- /dev/null
+++ b/basic_demo/fastapi_call_4v_9b.py
@@ -0,0 +1,112 @@
+import os
+import torch
+from threading import Thread
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+from fastapi.responses import JSONResponse
+from transformers import (
+    AutoTokenizer,
+    StoppingCriteria,
+    StoppingCriteriaList,
+    TextIteratorStreamer, AutoModel
+)
+from pydantic import BaseModel
+from PIL import Image
+import base64
+import io
+
+app = FastAPI()
+
+MODEL_PATH = os.environ.get('MODEL_PATH', '/root/.cache/modelscope/hub/ZhipuAI/glm-4v-9b')
+
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_PATH,
+    trust_remote_code=True,
+    encode_special_tokens=True
+)
+model = AutoModel.from_pretrained(
+    MODEL_PATH,
+    trust_remote_code=True,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+).eval()
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = model.config.eos_token_id
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+
+class Message(BaseModel):
+    role: str
+    content: str
+
+class ChatRequest(BaseModel):
+    model: str
+    messages: list[Message]
+    temperature: float = 0.6
+    top_p: float = 0.8
+    max_tokens: int = 1024
+    image: str = None
+
+@app.post("/v1/chat/completions")
+async def chat(chat_request: ChatRequest):
+    messages = chat_request.messages
+    temperature = chat_request.temperature
+    top_p = chat_request.top_p
+    max_length = chat_request.max_tokens
+    image_data = chat_request.image
+
+    inputs = []
+    for message in messages:
+        inputs.append({"role": message.role, "content": message.content})
+
+    if image_data != "-1":
+        try:
+            image = Image.open(io.BytesIO(base64.b64decode(image_data))).convert("RGB")
+        except:
+            raise HTTPException(status_code=400, detail="Invalid image data")
+        inputs[-1].update({"image": image})
+
+    model_inputs = tokenizer.apply_chat_template(
+        inputs,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_tensors="pt",
+        return_dict=True
+    ).to(next(model.parameters()).device)
+
+    streamer = TextIteratorStreamer(
+        tokenizer=tokenizer,
+        timeout=60,
+        skip_prompt=True,
+        skip_special_tokens=True
+    )
+
+    stop = StopOnTokens()
+    generate_kwargs = {
+        **model_inputs,
+        "streamer": streamer,
+        "max_new_tokens": max_length,
+        "do_sample": True,
+        "top_p": top_p,
+        "temperature": temperature,
+        "stopping_criteria": StoppingCriteriaList([stop]),
+        "repetition_penalty": 1.2,
+        "eos_token_id": [151329, 151336, 151338],
+    }
+
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    response = ""
+    for new_token in streamer:
+        if new_token:
+            response += new_token
+
+    return JSONResponse(content={"choices": [{"message": {"role": "assistant", "content": response.strip()}}]})
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)