RoboTF LLM Tools 🪙

This application is part of the RobotF AI Suite and is designed to assist users running LocalAI via Docker. It provides a Streamlit-based interface with multiple pages to help manage and configure your LocalAI setup.

Table of Contents

• Description
• Features
• Getting Started
  • Docker Compose
  • Docker from DockerHub
  • Direct Python Development
• Development Setup
• Contact
• Contributing
• License
• Acknowledgements

Description

Welcome to the RoboTF LLM Tools, a Streamlit web application that assists you in managing the RobotF AI Suite project and LocalAI in a Docker based environment.

Features

• Model Configuration Editor: Easily edit and manage your LocalAI model configurations through a user-friendly interface (pages/Model_Config_Editor.py)
• Docker Command Runner: Execute Docker commands directly through the app interface (pages/Docker_Command_Runner.py) REQUIRES you to mount the docker.sock!!
• Docker Dashboard: Show status of all the RoboTF AI Suite containers and interact with containers. REQUIRES you to mount the docker.sock!!
• HuggingFace Download: Download models directly from HuggingFace to your mounted models path (share with LocalAI)
• LLM Token Estimator: Estimate Tokens from different open source models directly in your browser. REQUIRES HF_TOKEN for private reposs
• Streamlit-Based UI: Modern web interface built with Streamlit framework (RoboTF_LLM_Tools.py)

Getting Started 🧹

Choose your path with one of these three options:

Option 1: Docker Compose

Docker Compose:

Clone the Repo:

git clone https://github.com/kkacsh321/robotf-llm-tools.git

cd robotf-llm-tools

Set Up Docker Compose: Ensure you have Docker and Docker Compose installed, Match up mount and env variables needed, then run:

docker-compose up -d --build

Run the App:

After Docker Compose has successfully built and started the containers, navigate to http://localhost:8969 in your web browser.

Option 2: Docker Hub Container

For those who wish to pull the pre-built container from Docker Hub:

Pull the Docker Image using latest tag (example v0.0.5):

docker pull robotf/robotf-llm-tools:latest

Run the Container:

docker run -d robotf/robotf-llm-tools \
  -e HF_TOKEN=<hf token> \ # HuggingFace Token
  -e MODELS_DIR=/models \ # Where to mount models volume in container
  -p 8969:8969 \
  -v /var/run/docker.sock:/var/run/docker.sock \ # important to run docker commands through the applications!
  -v <localai models path>:/models \ # The directory you store your LocalAI models in

Open your web browser to http://localhost:8969 and let the fun begin.

Option 3: Local Development

For those who wish to tinker with the source code:

Clone the Repo:

git clone <git clone https://github.com/kkacsh321/robotf-llm-tools.git>

cd robotf-llm-tools

# Install Dependencies:

pip install -r requirements.txt

Run the App:

streamlit run RoboTF_LLM_Tools.py

or using gotask

task run

Follow the Streamlit link to your web browser, or navigate to the provided local URL http://localhost:8969

Development Setup

This repo uses things such as precommit, task, and brew (for Mac)

Mac: Run the setup script (if on mac with brew already installed):

./scripts/setup.sh

Otherwise install the required Python packages:

pip install -r requirements.txt

This command installs all the necessary packages, including Streamlit, langchain components, etc.

Running the App To run the app, navigate to the app's directory in your terminal and execute the following command:

with task:

task run

with docker:

task docker-load && task docker-run

with just plain streamlit

streamlit run RoboTF_LLM_Tools.py

Environment Variables

If you need to access private Huggingface repos you will need to set the environment variable of HF_TOKEN to your huggingface api key.

This can be done locally by doing:

export HF_TOKEN=<insert token>

In the docker compose file by replacing the HF_TOKEN variable

Using docker by passing it at the docker run statement

docker run -e HF_TOKEN=<insert token> -p 8505:8505 robotf/robotf-llm-token-estimator

Contact

[email protected]

Contributing 👥

Feel free to fork this repository and submit pull requests. For major changes, please open an issue first to discuss what you would like to change.

Create a new branch

git checkout -b feature/your-cool-feature

# Make your changes.

# Commit your changes 
git commit -m 'I added X or fixed Y'

# Push to the branch 
git push origin feature/your-cool-feature

# Open a pull request and prepare to share your nightmare with the world.

License 📜

This project is licensed under the MIT License - see the LICENSE file for details, but be warned: using this software may result in unintended issues.

Acknowledgments 🏆

Thanks to the Hugging Face community for providing a wide range of models and tokenizers.

Special thanks to the developers of the autotiktokenizer library for making it easy to work with different tokenizers.

Go support our friends at LocalAI