Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- Open-Orca/OpenOrca
+language:
+- en
+metrics:
+- accuracy
+library_name: adapter-transformers
+pipeline_tag: question-answering
+tags:
+- code
 ---
+# ramgpt-13b-awq-gemm Model Description
+
+## Overview
+This document details the "ramgpt-13b-awq-gemm" model, an innovative implementation that leverages Arbitrary Width Quantization (AWQ) in General Matrix Multiply (GEMM) operations. This model is part of the ramgpt series and is designed for high efficiency in large-scale AI tasks.
+
+## Model Specifications
+
+### Core Technology
+- **Architecture**: Based on the ramgpt-13b framework.
+- **Quantization Technique**: Utilizes Arbitrary Width Quantization (AWQ) for optimized matrix operations.
+
+### Scale and Performance
+- **Model Size**: 13 billion parameters, finely tuned for a balance between performance and computational efficiency.
+- **Matrix Operations**: Enhanced GEMM operations for faster and more efficient calculations.
+
+## Features
+
+- **Enhanced Computational Efficiency**: The AWQ approach significantly improves the speed of matrix operations, vital for large-scale AI tasks.
+- **Precision and Performance**: Despite the quantization, the model maintains a high level of precision, ensuring reliable and accurate outputs.
+- **Resource Optimization**: Optimally uses computational resources, making it suitable for environments with limited processing capabilities.
+
+## Use Cases
+
+1. **Advanced AI Computations**: Ideal for complex AI tasks requiring large-scale data processing and analysis.
+2. **Efficient Machine Learning Operations**: Perfectly suited for machine learning environments where efficiency and speed are paramount.
+3. **Data-Intensive Applications**: Capable of handling data-intensive applications, such as big data analysis and complex simulations.
+
+## Integration and Deployment
+
+- **Easy Integration**: Designed for easy integration with existing AI platforms and workflows.
+- **Scalable Deployment**: The model's architecture allows for scalable deployment across various environments, from cloud-based systems to edge devices.
+
+## Getting Started
+
+Follow these steps to integrate the ramgpt-13b-awq-gemm model into your system:
+
+1. **Initial Setup**: Ensure compatibility with your existing AI infrastructure.
+2. **Model Deployment**: Deploy the ramgpt-13b-awq-gemm model within your preferred environment.
+3. **Configuration and Testing**: Configure the model parameters to suit your specific needs and perform thorough testing for optimal results.
+
+## Support and Contributions
+
+For support, further information, or to contribute to the model's development, please visit our [GitHub repository](#) or contact our technical team.
+
+---
+
+*Disclaimer: The ramgpt-13b-awq-gemm model is continuously evolving, incorporating cutting-edge advancements in AI and quantization techniques for enhanced performance.*