Feature Request: Add `LlamaCppModel` Support to smolagents

### **Motivation Behind This Feature**

As the landscape of language models continues to evolve, integrating diverse model architectures becomes crucial for enhancing the versatility and applicability of libraries like **smolagents**. The `llama.cpp` framework offers an efficient and optimized way to run large language models with reduced resource consumption, making it an attractive option for developers and researchers.

**Current Challenges:**
- **Limited Model Support:** While **smolagents** currently supports models like those from Hugging Face's Transformers library, there's a growing demand for integrating models managed by `llama.cpp`.
- **Performance Optimization:** `llama.cpp` provides optimized performance for running large language models on resource-constrained environments, which is beneficial for users who require high efficiency without compromising on model capabilities.

**Proposed Solution:**
Introduce a new `LlamaCppModel` class that seamlessly integrates `llama.cpp` models into the **smolagents** ecosystem, ensuring proper parameter handling and conditional tool usage.

### **Detailed Description**

The `LlamaCppModel` class is designed to interact with `llama.cpp` models, providing robust parameter management and the ability to utilize tools only when explicitly provided. This integration ensures that users can leverage the efficiency of `llama.cpp` while maintaining the flexibility and functionality that **smolagents** offers.

**Key Features:**
- **Flexible Model Loading:** Supports loading models from a local path or directly from a Hugging Face repository.
- **Parameter Management:** Allows customization of GPU layers, context size, and maximum token generation.
- **Conditional Tool Integration:** Integrates tools seamlessly when they are passed, ensuring optimized performance.