Improve model card: Add pipeline tag, library_name, abstract, and usage

This PR significantly improves the model card for **RouteFinder** by:

- Adding the `pipeline_tag: reinforcement-learning`, which makes the model discoverable on the Hub via https://huggingface.co/models?pipeline_tag=reinforcement-learning.
- Including `library_name: rl4co` as the model uses techniques related to Reinforcement Learning for Combinatorial Optimization and explicitly references `rl4co` in its documentation and acknowledgements.
- Updating the main title to reflect the paper's full title: [RouteFinder: Towards Foundation Models for Vehicle Routing Problems](https://huggingface.co/papers/2406.15007).
- Adding the paper's abstract to provide a clear overview of the model.
- Incorporating detailed "Installation" and "Quickstart" sections with code snippets directly from the official GitHub repository, facilitating easier model usage.
- Adding the official citation information.

These changes provide a more comprehensive and user-friendly model card.

README.md

@@ -1,7 +1,91 @@
 ---
 license: mit
+pipeline_tag: reinforcement-learning
+library_name: rl4co
 ---
 
-# RouteFinder Checkpoints
+# RouteFinder: Towards Foundation Models for Vehicle Routing Problems
 
-You may find instructions here: https://github.com/ai4co/routefinder
+This repository contains the checkpoints for **RouteFinder**, a comprehensive foundation model framework designed to tackle various Vehicle Routing Problem (VRP) variants. This model was presented in the paper [RouteFinder: Towards Foundation Models for Vehicle Routing Problems](https://huggingface.co/papers/2406.15007).
+
+The official code and detailed instructions are available in the [GitHub repository](https://github.com/ai4co/routefinder).
+
+## Abstract
+
+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 .
+
+## Installation
+
+We use [uv](https://github.com/astral-sh/uv) (Python package manager) to manage the dependencies:
+
+```bash
+uv venv --python 3.12 # create a new virtual environment
+source .venv/bin/activate # activate the virtual environment
+uv sync --all-extras # for all dependencies
+```
+
+Note that this project is also compatible with normal `pip install -e .` in case you use a different package manager.
+
+## Quickstart
+
+### Download data and checkpoints
+
+To download the data and checkpoints from HuggingFace automatically, you can use:
+
+```bash
+python scripts/download_hf.py
+```
+
+### Running
+
+We recommend exploring [this quickstart notebook](https://github.com/ai4co/routefinder/blob/main/examples/1.quickstart.ipynb) to get started with the `RouteFinder` codebase!
+
+The main runner (example here of main baseline) can be called via:
+
+```bash
+python run.py experiment=main/rf/rf-transformer-100
+```
+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.
+
+### Testing
+
+You may use the provided test function to test the model:
+
+```bash
+python test.py --checkpoint checkpoints/100/rf-transformer.ckpt
+```
+
+or with additional parameters:
+
+```
+usage: test.py [-h] --checkpoint CHECKPOINT [--problem PROBLEM] [--size SIZE] [--datasets DATASETS] [--batch_size BATCH_SIZE]
+               [--device DEVICE] [--remove-mixed-backhaul | --no-remove-mixed-backhaul]
+
+options:
+  -h, --help            show this help message and exit
+  --checkpoint CHECKPOINT
+                        Path to the model checkpoint
+  --problem PROBLEM     Problem name: cvrp, vrptw, etc. or all
+  --size SIZE           Problem size: 50, 100, for automatic loading
+  --datasets DATASETS   Filename of the dataset(s) to evaluate. Defaults to all under data/{problem}/ dir
+  --batch_size BATCH_SIZE
+  --device DEVICE
+  --remove-mixed-backhaul, --no-remove-mixed-backhaul
+                        Remove mixed backhaul instances. Use --no-remove-mixed-backhaul to keep them. (default: True)
+```
+
+## Citation
+
+If you find RouteFinder valuable for your research or applied projects:
+
+```bibtex
+@article{
+berto2025routefinder,
+title={{RouteFinder: Towards Foundation Models for Vehicle Routing Problems}},
+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},
+journal={Transactions on Machine Learning Research},
+issn={2835-8856},
+year={2025},
+url={https://openreview.net/forum?id=QzGLoaOPiY},
+}
+```