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https://github.com/dsacms/mural-ollama

Multimodal LLM Mural Assistant with Ollama
https://github.com/dsacms/mural-ollama

ai llava llm llms multimodal mural ollama open-source pyqt6 python

Last synced: 28 days ago
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Multimodal LLM Mural Assistant with Ollama

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README

        

# MurAI - Multimodal Mural Assistant
Multimodal LLM Mural Assistant with Ollama

![Example screenshot](assets/ExampleScreenshot.png)

## About the Project
MurAI is a multimodal LLM assistant designed to analyze and extract information from Mural.co screenshots. This tool is meant to help users quickly understand and interact with Mural content.

### Project Vision
A general multimodal LLM assistant would greatly help the office navigate large amounts of information quicker and more efficiently. This prototype aims to lay the groundwork for future LLM projects at CMS.

## Installation

1. Clone the repository:
```
git clone https://github.com/DSACMS/mural-ollama
cd mural-ollama
```

2. Install the required dependencies (ideally in a virtual environment):
```
pip install -r requirements.txt
```

3. Ensure Ollama is installed and the required model is available (in this case llava:13b):
```
ollama pull llava:13b
```

## Core Team

A full list of contributors can be found on [https://github.com/DSACMS/mural-ollama/graphs/contributors](https://github.com/DSACMS/mural-ollama/graphs/contributors).

## Contributing
Thank you for considering contributing to an Open Source project of the US Government! For more information about our contribution guidelines, see [CONTRIBUTING.md](CONTRIBUTING.md).

## Policies

### Open Source Policy

We adhere to the [CMS Open Source
Policy](https://github.com/CMSGov/cms-open-source-policy). If you have any
questions, just [shoot us an email](mailto:[email protected]).

### Security and Responsible Disclosure Policy

*Submit a vulnerability:* Unfortunately, we cannot accept secure submissions via
email or via GitHub Issues. Please use our website to submit vulnerabilities at
[https://hhs.responsibledisclosure.com](https://hhs.responsibledisclosure.com).
HHS maintains an acknowledgements page to recognize your efforts on behalf of
the American public, but you are also welcome to submit anonymously.

For more information about our Security, Vulnerability, and Responsible Disclosure Policies, see [SECURITY.md](SECURITY.md).

## Public domain

This project is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the [CC0 1.0 Universal public domain dedication](https://creativecommons.org/publicdomain/zero/1.0/) as indicated in [LICENSE](LICENSE).

All contributions to this project will be released under the CC0 dedication. By submitting a pull request or issue, you are agreeing to comply with this waiver of copyright interest.