# How to Create A Custom Language Model

When working with advanced language models, there might come a time when you need a custom solution tailored to your specific needs. Inheriting from an `BaseLLM` in a Python framework allows developers to create custom language model classes with ease. This developer guide will take you through the process step by step.

### Prerequisites

Before you begin, ensure that you have:

- A working knowledge of Python programming.
- Basic understanding of object-oriented programming (OOP) in Python.
- Familiarity with language models and natural language processing (NLP).
- The appropriate Python framework installed, with access to `BaseLLM`.

### Step-by-Step Guide

#### Step 1: Understand `BaseLLM`

The `BaseLLM` is an abstract base class that defines a set of methods and properties which your custom language model (LLM) should implement. Abstract classes in Python are not designed to be instantiated directly but are meant to be subclasses.

#### Step 2: Create a New Class

Start by defining a new class that inherits from `BaseLLM`. This class will implement the required methods defined in the abstract base class.

```python
from swarms import BaseLLM

class vLLMLM(BaseLLM):
    pass
```

#### Step 3: Initialize Your Class

Implement the `__init__` method to initialize your custom LLM. You'll want to initialize the base class as well and define any additional parameters for your model.

```python
class vLLMLM(BaseLLM):
    def __init__(self, model_name='default_model', tensor_parallel_size=1, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_name = model_name
        self.tensor_parallel_size = tensor_parallel_size
        # Add any additional initialization here
```

#### Step 4: Implement Required Methods

Implement the `run` method or any other abstract methods required by `BaseLLM`. This is where you define how your model processes input and returns output.

```python
class vLLMLM(BaseLLM):
    # ... existing code ...

    def run(self, task, *args, **kwargs):
        # Logic for running your model goes here
        return "Processed output"
```

#### Step 5: Test Your Model

Instantiate your custom LLM and test it to ensure that it works as expected.

```python
model = vLLMLM(model_name='my_custom_model', tensor_parallel_size=2)
output = model.run("What are the symptoms of COVID-19?")
print(output)  # Outputs: "Processed output"
```

#### Step 6: Integrate Additional Components

Depending on the requirements, you might need to integrate additional components such as database connections, parallel computing resources, or custom processing pipelines.

#### Step 7: Documentation

Write comprehensive docstrings for your class and its methods. Good documentation is crucial for maintaining the code and for other developers who might use your model.

```python
class vLLMLM(BaseLLM):
    """
    A custom language model class that extends BaseLLM.

    ... more detailed docstring ...
    """
    # ... existing code ...
```

#### Step 8: Best Practices

Follow best practices such as error handling, input validation, and resource management to ensure your model is robust and reliable.

#### Step 9: Packaging Your Model

Package your custom LLM class into a module or package that can be easily distributed and imported into other projects.

#### Step 10: Version Control and Collaboration

Use a version control system like Git to track changes to your model. This makes collaboration easier and helps you keep a history of your work.

### Conclusion

By following this guide, you should now have a custom model that extends the `BaseLLM`. Remember that the key to a successful custom LLM is understanding the base functionalities, implementing necessary changes, and testing thoroughly. Keep iterating and improving based on feedback and performance metrics.

### Further Reading

- Official Python documentation on abstract base classes.
- In-depth tutorials on object-oriented programming in Python.
- Advanced NLP techniques and optimization strategies for language models.

This guide provides the fundamental steps to create custom models using `BaseLLM`. For detailed implementation and advanced customization, it's essential to dive deeper into the specific functionalities and capabilities of the language model framework you are using.
