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---
license: apache-2.0
language:
- en
datasets:
- Open-Orca/OpenOrca
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: question-answering
tags:
- code
---
# ramgpt 13b Coding Model (LLM PoC) Description
## Overview
This document provides an overview of the ramgpt 13b coding model, which is based on the CodeLlama architecture and is designed to work seamlessly with the ramgpt inferencing platform.
## Model Specifications
### Base Architecture
- **Architecture**: CodeLlama
- **Model Size**: 13 billion parameters
### Integration
- **Platform Compatibility**: Compatible with ramgpt inferencing platform for efficient and scalable deployment.
## Features
- **Advanced Coding Capabilities**: The model excels in understanding and generating complex code structures, making it ideal for a wide range of programming tasks.
- **High Adaptability**: Designed to quickly adapt to new coding patterns and languages, ensuring its utility in diverse development environments.
- **Optimized for Efficiency**: The model's architecture is optimized for high-performance inferencing, offering fast response times even for complex coding queries.
## Use Cases
1. **Automated Code Generation**: Assists in writing code by automatically generating code snippets based on user input.
2. **Code Review and Analysis**: Capable of analyzing existing code for potential improvements or issues.
3. **Language Translation**: Translates code between various programming languages.
## Getting Started
To start using the 13b coding model with the ramgpt inferencing platform, follow these steps:
1. **Setup**: Ensure that the ramgpt inferencing platform is properly set up and running.
2. **Model Deployment**: Deploy the 13b coding model onto the platform.
3. **Integration**: Integrate the model with your development environment or workflow.
## Support and Contribution
For support or to contribute to the development of this model, please visit the [GitHub repository](#) or contact our development team.
---
*Note: This model is continuously updated to incorporate the latest advancements in AI and programming language syntax and semantics.*
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