UXI-LLM: A Modular Framework for Symbolic-Ready LLMs 🌟

Welcome to the UXI-LLM repository! This project offers a modular and symbolic-ready LLM framework designed to address key development pain points. With a focus on local fine-tuning, full-language interoperability, and composable reasoning, UXI-LLM aims to enhance AI/UX integration across various ecosystems.

Table of Contents

1. Introduction
2. Features
3. Installation
4. Usage
5. Contributing
6. License
7. Contact
8. Releases

Introduction

The UXI-LLM framework provides a robust solution for developers looking to implement language models in a modular way. Its design allows for easy extensibility and high performance, making it suitable for a wide range of applications. This framework is particularly useful for those who want to integrate AI into their user experience seamlessly.

Features

• Modular Design: Easily customize and extend the framework to fit your needs.
• Symbolic Reasoning: Incorporate symbolic reasoning capabilities into your applications.
• Local Fine-Tuning: Fine-tune models locally for better performance and relevance.
• Full-Language Interoperability: Work with multiple languages without restrictions.
• Composable Reasoning: Combine different reasoning methods to enhance decision-making.
• Open Source: Contribute to and learn from an open-source community.

Installation

To get started with UXI-LLM, follow these steps:

1. Clone the repository:

   git clone https://github.com/mostafaaladham/UXI-LLM.git

2. Navigate to the project directory:

   cd UXI-LLM

3. Install the required packages:

   pip install -r requirements.txt

4. Run the setup script:

   python setup.py install

Usage

Using UXI-LLM is straightforward. Below are some examples to help you get started.

Basic Example

To create a simple LLM instance, use the following code:

from uxi_llm import LLM

model = LLM(model_name='your_model_name')
response = model.generate("Hello, how can I help you?")
print(response)

Fine-Tuning

To fine-tune a model locally, follow these steps:

model.fine_tune(training_data='path/to/your/data')

Composable Reasoning

You can also utilize composable reasoning methods:

from uxi_llm import ComposableReasoning

reasoning = ComposableReasoning()
result = reasoning.combine(methods=['method1', 'method2'], input_data='some_input')
print(result)

Contributing

We welcome contributions to UXI-LLM! To contribute:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your changes to your fork.
5. Submit a pull request.

Please ensure that your code follows the existing style and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or suggestions, please reach out:

• Email: [email protected]
• GitHub: mostafaaladham

Releases

To access the latest releases of UXI-LLM, please visit the Releases section. You can download and execute the latest files to get started with the framework.

To keep up with updates, check the Releases section regularly.

Topics

• Composable AI
• Extensible AI
• Language Agnostic LLM
• LLM Training Toolkit
• Local Fine-Tuning
• Modular LLM
• Multi-Language AI
• Open Source LLM
• Python LLM Framework
• Symbolic Reasoning

Conclusion

The UXI-LLM framework offers a powerful and flexible solution for developers looking to leverage language models in their applications. With its modular design and focus on symbolic reasoning, it stands out as a valuable tool for enhancing AI/UX integration. We invite you to explore the repository, contribute, and help us improve this framework for everyone.

For further information, updates, and community discussions, please refer to the Releases section.