SPARSA-LM (Sparse Attention Lumina Language Model) is a Small Language Model (SLM) designed to push the boundaries of artificial intelligence by integrating cutting-edge deep learning techniques, multimodal capabilities, and domain-specific expertise. SPARSA-LM is crafted to address complex challenges in science, medicine, technology, and research.

To develop a state-of-the-art LLM that empowers researchers, professionals, and organizations to solve real-world problems, advance scientific discovery, and democratize access to AI-powered insights.

To shape a future where AI serves as a driving force for innovation, collaboration, and understanding, enabling breakthroughs across diverse fields and communities.

• Built on the foundation of multimodal understanding, combining text, image, and structured data for versatile applications.
• Leverages state-of-the-art deep learning architectures, including unified Transformer models and sparse attention mechanisms.

• Specially tailored for scientific, medical, and technical domains, providing accurate and domain-specific insights.
• Optimized for processing large datasets, including biomedical literature, scientific papers, and open-access repositories.

• Supports multiple languages, fostering global collaboration and catering to diverse communities.

• Engineered for high performance, adaptable to both local systems and large-scale distributed environments.
• Designed with sparse attention for computational efficiency and scalability.

• Python 3.11 or later
• GPU-enabled system for optimal performance
• Libraries specified in requirements.txt

1. Clone the repository:

   git clone https://github.com/Archit03/SPARSA-LM-Base-0.1
   cd SPARSA-LM

2. Install required dependencies:

   pip install -r requirements.txt

Run the training script to train SPARSA-LM:

python src/training.py

Generate text using the trained model:

python src/inference.py

We welcome contributions! Feel free to open issues, submit pull requests, or suggest improvements to make SPARSA-LM better.

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

For questions or collaboration, reach out to:

• Email: email
• Website: Website

Thank you for being part of the SPARSA-LM journey! 🚀