Optical network orchestrator based on Large Language Models

Installation

Simply clone this repository and install it as a package using pip:

$ git clone git@github.com:nicoladicicco/llm-orchestrator.git
$ cd llm-orchestrator
$ pip install -e .

Usage

The repository is structured as a data folder and self-contained scripts for running the different components of the pipeline independently. Said code will be encapsulated into the LLMInterface class in the future.

• data/ contains the LLM files, the test set, and the model outputs.
• llm_orchestrator/ contains Python scripts for querying the LLM interface and the output validator.
• planning.py runs the planning phase of the pipeline, and saves the generated tasks in the test set folder.
• execution.py runs the execution phase of the pipeline, and saves the generated data structures in the test set folder.
• baseline.py runs the baseline algorithm (just LLM inference without the planning and execution phases), and saves the generated data structures in the test set folder.

To run the code, clone a Mixtral-Instruct LLM in .gguf format from here and place it in data/models/. Feel free to experiment with other models.

ECOC 2024 paper

You may download the paper associated with this dataset here: [insert link]