Enhance model card: Add pipeline tag, library name, paper/project links, usage example, and update license

This PR enhances the model card by:

- Adding the `pipeline_tag: reinforcement-learning` to improve discoverability.
- Specifying `library_name: amago` as the primary library used by the model.
- Updating the license to `apache-2.0` as indicated by the project's GitHub.
- Including additional relevant tags like `pokemon`, `game-ai`, `offline-rl`, and `transformers`.
- Linking to the paper: [Human-Level Competitive Pokémon via Scalable Offline Reinforcement Learning with Transformers](https://huggingface.co/papers/2504.04395).
- Adding the project website: https://metamon.tech.
- Providing a concise overview and a practical Python usage example for quick model inference.

These changes will make the model more informative and easier to find and use for researchers and the community.

README.md

@@ -1,10 +1,45 @@
 ---
-license: mit
 datasets:
 - jakegrigsby/metamon-synthetic
 - jakegrigsby/metamon-parsed-replays
+license: apache-2.0
+pipeline_tag: reinforcement-learning
+library_name: amago
+tags:
+- pokemon
+- game-ai
+- offline-rl
+- transformers
 ---
+
 Checkpoints from Metamon (v1) training runs.
 
+**Metamon** enables reinforcement learning (RL) research on [Pokémon Showdown](https://pokemonshowdown.com/) by providing:
+
+1) A standardized suite of teams and opponents for evaluation.
+2) A large dataset of RL trajectories "reconstructed" from real human battles.
+3) Starting points for training imitation learning (IL) and RL policies.
+
+Metamon is the codebase behind ["Human-Level Competitive Pokémon via Scalable Offline RL and Transformers"](https://arxiv.org/abs/2504.04395) (RLC, 2025). Please check out our [project website](https://metamon.tech) for an overview of our results. This README documents the dataset, pretrained models, training, and evaluation details to help you get battling!
+
+**Paper:** [Human-Level Competitive Pokémon via Scalable Offline Reinforcement Learning with Transformers](https://huggingface.co/papers/2504.04395)
+**Project Website:** https://metamon.tech
+**Code:** [GitHub Repository](https://github.com/UT-Austin-RPL/metamon/tree/main)
+
+### Usage
+
+Pretrained models can run without research GPUs, but you will need to install [`amago`](https://github.com/UT-Austin-RPL/amago), which is an RL codebase by the same authors. Follow installation instructions [here](https://ut-austin-rpl.github.io/amago/installation.html).
+
+Load and run pretrained models with `metamon.rl.eval_pretrained`. For example, to run the default checkpoint of the `SyntheticRLV2` model for 100 battles against a set of heuristic baselines:
+
+```bash
+python -m metamon.rl.eval_pretrained --agent SyntheticRLV2 --gens 1 --formats ou --n_challenges 100 --eval_type heuristic
+```
+
+To battle against other models or humans online (via a local Showdown server):
+
+```bash
+python -m metamon.rl.eval_pretrained --agent SyntheticRLV2 --gens 1 --formats ou --n_challenges 50 --eval_type ladder --username <pick unique username> --team_set competitive
+```
 
-Check out the project on [GitHub](https://github.com/UT-Austin-RPL/metamon/tree/main) for more information.
+For more details on models and usage, please refer to the [project's GitHub repository](https://github.com/UT-Austin-RPL/metamon/tree/main).