**LLaMA.cpp (the C++ library) does not natively support function calling** (e.g., tool calls, structured outputs, or API-style interactions) out of the box. However, **you can simulate 
or extend it to support function calling** through custom code, prompt engineering, or integration with frameworks like `llama_cpp_py` (the Python wrapper). Here's a breakdown:

---

### **1. Core LLaMA.cpp (C++ Library)**
- **No Built-in Function Calling**: The original LLaMA models (and `llama.cpp`) are designed for **text generation** and do not inherently support function calling, API calls, or 
structured outputs.
- **Custom Tokenization**: While you can tokenize and process prompts, the model itself does not "call" functions. You must manually parse the output or use external tools to extract 
structured data.

---

### **2. Using `llama_cpp_py` (Python Wrapper)**
The Python wrapper (`llama_cpp_py`) provides a high-level API for interacting with `llama.cpp` models. While it doesn't include built-in function calling, you can **simulate it** by:
- **Prompt Engineering**: Design prompts to instruct the model to output structured data (e.g., JSON) representing function calls.
- **Post-Processing**: Parse the model's output to extract function names, parameters, and results.

#### Example: Simulating Function Calling
```python
from llama_cpp import Llama

# Load model
model = Llama(model_path="model.gguf", n_gpu_layers=0)

# Prompt to simulate a function call
prompt = "You are a helpful assistant. When the user asks for the current time, call the 'get_time()' function. Here's the user's query: What is the current time?"

# Generate response
response = model(prompt, max_tokens=100, temperature=0.7)
output = response["choices"][0]["text"]

# Parse the output (simulated)
if "get_time()" in output:
    # Simulate calling a function (e.g., via an external API)
    import datetime
    current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    print("Function called: get_time()", "Result:", current_time)
else:
    print("No function call needed:", output)
```

---

### **3. Advanced: Integrating with External Tools**
To enable **true function calling**, you can:
- **Use a Framework like `llama-adapter` or `llama.cpp` plugins**: These tools allow the model to interact with external APIs or databases.
- **Combine with LLM Libraries**: Use libraries like `langchain` or `vllm` to chain the model with tools (e.g., HTTP APIs, databases).

#### Example: Using `llama.cpp` with a Custom Tool
```python
from llama_cpp import Llama
import requests

def get_time():
    response = requests.get("https://worldtimeapi.org/api/timezone/Etc/UTC")
    return response.json()["datetime"]

model = Llama(model_path="model.gguf", n_gpu_layers=0)

prompt = "What is the current time?"
response = model(prompt, max_tokens=100, temperature=0.7)
output = response["choices"][0]["text"]

if "get_time()" in output:
    current_time = get_time()
    print("Function called: get_time()", "Result:", current_time)
else:
    print("No function call needed:", output)
```

---

### **4. Third-Party Extensions**
Some projects extend `llama.cpp` to support function calling:
- **`llama.cpp` Plugins**: Tools like `llama.cpp`'s `llama.cpp` plugin system allow adding custom logic (e.g., for API calls).
- **`llama.cpp` + `langchain`**: Use `langchain` to chain the model with tools, enabling structured function calls.

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### **5. Summary**
| Feature                | Supported? | Notes |
|------------------------|------------|-------|
| Native Function Calling | ❌ No      | Core LLaMA models do not support this. |
| Prompt Engineering     | ✅ Yes     | Use prompts to instruct the model to output structured data. |
| Post-Processing        | ✅ Yes     | Parse the model's output to extract function calls. |
| External Tool Integration | ✅ Yes   | Use external APIs or plugins to simulate function calls. |
| Python Wrapper Support | ✅ Yes     | `llama_cpp_py` allows flexible integration with custom logic. |

---

### **Recommendation**
If you need **true function calling**, use **prompt engineering + post-processing** or integrate `llama.cpp` with a framework like `langchain` or `vllm` to chain the model with external 
tools. For most use cases, **simulating function calls via prompts** is the simplest and most effective approach.