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.

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.

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.

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.

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.

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.