Improve model card: Add pipeline tag, paper link, abstract, code link, and usage examples

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  license: mit
 
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- # RouteFinder Checkpoints
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- You may find instructions here: https://github.com/ai4co/routefinder
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: mit
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+ pipeline_tag: reinforcement-learning
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+ # RouteFinder: Towards Foundation Models for Vehicle Routing Problems
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+ This repository contains the checkpoints for **RouteFinder**, a comprehensive foundation model framework designed to tackle different Vehicle Routing Problem (VRP) variants, as presented in the paper [RouteFinder: Towards Foundation Models for Vehicle Routing Problems](https://huggingface.co/papers/2406.15007).
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+
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+ ## Abstract
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+ This paper introduces RouteFinder, a comprehensive foundation model framework to tackle different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model for VRPs should be able to represent variants by treating each as a subset of a generalized problem equipped with different attributes. We propose a unified VRP environment capable of efficiently handling any combination of these attributes. The RouteFinder model leverages a modern transformer-based encoder and global attribute embeddings to improve task representation. Additionally, we introduce two reinforcement learning techniques to enhance multi-task performance: mixed batch training, which enables training on different variants at once, and multi-variant reward normalization to balance different reward scales. Finally, we propose efficient adapter layers that enable fine-tuning for new variants with unseen attributes. Extensive experiments on 48 VRP variants show RouteFinder outperforms recent state-of-the-art learning methods. Our code is publicly available at this https URL .
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+
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+ ## Code
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+ The official code repository is available at: [https://github.com/ai4co/routefinder](https://github.com/ai4co/routefinder)
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+
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+ ## Model Overview
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+ <div align="center">
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+ <img src="https://github.com/ai4co/routefinder/raw/main/assets/overview.png" alt="RouteFinder Overview" style="width: 100%; height: auto;">
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+ </div>
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+
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+ ## ๐Ÿš€ Installation
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+
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+ We use [uv](https://github.com/astral-sh/uv) (Python package manager) to manage the dependencies:
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+
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+ ```bash
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+ uv venv --python 3.12 # create a new virtual environment
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+ source .venv/bin/activate # activate the virtual environment
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+ uv sync --all-extras # for all dependencies
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+ ```
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+
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+ Note that this project is also compatible with normal `pip install -e .` in case you use a different package manager.
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+
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+ ## ๐Ÿ Quickstart
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+
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+ ### Download data and checkpoints
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+
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+ To download the data and checkpoints from HuggingFace automatically, you can use:
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+
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+ ```bash
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+ python scripts/download_hf.py
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+ ```
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+
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+ ### Running
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+
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+ We recommend exploring [this quickstart notebook](https://github.com/ai4co/routefinder/blob/main/examples/1.quickstart.ipynb) to get started with the `RouteFinder` codebase!
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+
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+ The main runner (example here of main baseline) can be called via:
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+
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+ ```bash
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+ python run.py experiment=main/rf/rf-transformer-100
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+ ```
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+ You may change the experiment by using the `experiment=YOUR_EXP`, with the path under [`configs/experiment`](https://github.com/ai4co/routefinder/tree/main/configs/experiment) directory.
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+
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+ ### Testing
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+
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+ You may use the provided test function to test the model:
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+
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+ ```bash
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+ python test.py --checkpoint checkpoints/100/rf-transformer.ckpt
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+ ```
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+
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+ or with additional parameters:
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+
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+ ```
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+ usage: test.py [-h] --checkpoint CHECKPOINT [--problem PROBLEM] [--size SIZE] [--datasets DATASETS] [--batch_size BATCH_SIZE]
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+ [--device DEVICE] [--remove-mixed-backhaul | --no-remove-mixed-backhaul]
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+
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+ options:
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+ -h, --help show this help message and exit
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+ --checkpoint CHECKPOINT
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+ Path to the model checkpoint
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+ --problem PROBLEM Problem name: cvrp, vrptw, etc. or all
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+ --size SIZE Problem size: 50, 100, for automatic loading
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+ --datasets DATASETS Filename of the dataset(s) to evaluate. Defaults to all under data/{problem}/ dir
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+ --batch_size BATCH_SIZE
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+ --device DEVICE
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+ --remove-mixed-backhaul, --no-remove-mixed-backhaul
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+ Remove mixed backhaul instances. Use --no-remove-mixed-backhaul to keep them. (default: True)
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+ ```
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+
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+ We also have a notebook to automatically download and test models on the CVRPLIB [here](https://github.com/ai4co/routefinder/blob/main/examples/2.eval-cvrplib.ipynb)!
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+
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+ ## ๐Ÿšš Available Environments
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+ RouteFinder considers 48 VRP variants, which are detailed in the [GitHub repository](https://github.com/ai4co/routefinder) under the "Available Environments" section. These variants combine different features such as Capacity (C), Open Route (O), Backhaul (B), Mixed (M), Duration Limit (L), Time Windows (TW), and Multi-depot (MD).
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+
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+ ## A tip for you!
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+ Do you want to improve the performance of your model with no effort? Use our Transformer structure, based on recent models such as Llama and DeepSeek ;)
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+
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+ <div align="center">
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+ <img src="https://github.com/ai4co/routefinder/raw/main/assets/rf-te.png" alt="RouteFinder Transformer Structure" style="width: 50%; height: auto;">
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+ </div>
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+
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+ ## Known Bugs
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+ - For some reason, there seem to be bugs when training on M series processors from Apple (but not during inference somehow?). We recommend training with a discrete GPU. We'll keep you posted with updates!
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+
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+ ## ๐Ÿค— Acknowledgements
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+ - https://github.com/FeiLiu36/MTNCO/tree/main
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+ - https://github.com/RoyalSkye/Routing-MVMoE
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+ - https://github.com/yd-kwon/POMO
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+ - https://github.com/ai4co/rl4co
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+
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+ ## ๐Ÿคฉ Citation
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+ If you find RouteFinder valuable for your research or applied projects:
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+
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+ ```bibtex
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+ @article{
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+ berto2025routefinder,
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+ title={{RouteFinder: Towards Foundation Models for Vehicle Routing Problems}},
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+ author={Federico Berto and Chuanbo Hua and Nayeli Gast Zepeda and Andr{\'e} Hottung and Niels Wouda and Leon Lan and Junyoung Park and Kevin Tierney and Jinkyoo Park},
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+ journal={Transactions on Machine Learning Research},
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+ issn={2835-8856},
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+ year={2025},
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+ url={https://openreview.net/forum?id=QzGLoaOPiY},
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+ }
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+ ```
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+
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+ ---
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+
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+ <div align="center">
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+ <a href="https://github.com/ai4co">
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+ <img src="https://raw.githubusercontent.com/ai4co/assets/main/svg/ai4co_animated_full.svg" alt="AI4CO Logo" style="width: 30%; height: auto;">
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+ </a>
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+ </div>