To integrate **llama.cpp** with Python, you can use the **`llama_cpp_py`** Python wrapper, which provides a high-level API for interacting with the `llama.cpp` C++ library. Below is a
step-by-step example of how to set up a WebSocket server (using `websockets` or `asyncio`) and integrate it with `llama.cpp` for real-time model inference.
---
### **1. Prerequisites**
- **Install `llama_cpp_py`** (Python wrapper for `llama.cpp`):
```bash
pip install llama_cpp_py
```
- **Download a model** (e.g., `llama-3` or `qwen3`) in the required format (e.g., `.gguf` or `.bin`). You can convert models using tools like `llama.cpp`'s `convert` utility or download
pre-converted models.
- **Ensure the model file** (e.g., `model.gguf`) is in the same directory as your script or specify the path.
---
### **2. Basic Python Example with WebSocket Server**
This example uses `websockets` to create a WebSocket server that accepts prompts, runs inference via `llama.cpp`, and sends responses back.
```python
import asyncio
import websockets
import json
from llama_cpp import Llama
# Load the model (replace with your model path)
model_path = "model.gguf" # Path to your .gguf or .bin model file
llm = Llama(model_path=model_path, n_gpu_layers=0) # n_gpu_layers=0 for CPU
async def handle_websocket(websocket, path):
async for message in websocket:
data = json.loads(message)
prompt = data.get("prompt", "")
# Generate response using llama.cpp
response = llm(prompt, max_tokens=100, temperature=0.7, top_p=0.9)
reply = response["choices"][0]["text"]
# Send response back to client
await websocket.send(json.dumps({"response": reply}))
# Start WebSocket server
start_server = websockets.serve(
handle_websocket,
"0.0.0.0", # Host
8765 # Port
)
asyncio.get_event_loop().run_until_complete(start_server)
asyncio.get_event_loop().run_forever()
```
---
### **3. Key Notes**
- **Model Format**: Ensure your model is in a compatible format (e.g., `.gguf`). If you're using a different format (e.g., `.bin`), you may need to convert it using `llama.cpp` tools.
- **GPU Acceleration**: Set `n_gpu_layers` to a value greater than 0 if you have a GPU (e.g., `n_gpu_layers=30` for a modern GPU).
- **WebSocket Integration**: This example uses `websockets` for simplicity. For production, consider using a framework like **FastAPI** or **Sanic** for better performance and async
handling.
---
### **4. Example WebSocket Client**
To test the server, use a simple WebSocket client (e.g., in a browser or Python):
```python
import asyncio
import websockets
import json
async def client():
async with websockets.connect("ws://localhost:8765"):
await asyncio.sleep(1)
await websocket.send(json.dumps({"prompt": "Hello, world!"}))
response = await websocket.recv()
print("Response:", response)
asyncio.get_event_loop().run_until_complete(client())
```
---
### **5. Advanced Customization**
- **Streaming**: Use `llm(prompt, stream=True)` to stream responses in real-time.
- **Custom Parameters**: Adjust `temperature`, `top_p`, `max_tokens`, etc., for fine-tuning.
- **Model Switching**: Load different models dynamically based on user input.
---
### **6. Optional: Use `llama.cpp` Directly (C++ Binding)**
If you need lower-level control (e.g., GPU acceleration), you can use the C++ API directly. However, this requires compiling `llama.cpp` and linking it with your Python code, which is
more complex.
---
### **Conclusion**
Using `llama_cpp_py` is the simplest way to integrate `llama.cpp` with Python for WebSocket-based applications. This approach balances performance, ease of use, and flexibility for
real-time model inference. For production, consider adding error handling, rate limiting, and model versioning.