---
license: agpl-3.0
language:
- en
tags:
- pokemon-showdown
- transformer
- game-ai
- monte-carlo-search
library_name: pytorch
pipeline_tag: text-generation
---

# AlphaMon

AlphaMon is a transformer-based battle policy model for Pokemon Showdown doubles formats, trained on large-scale replay logs and used with a Monte Carlo rollout search layer for decision support.

Current repository: https://github.com/davidemodolo/alphamon

## Model summary

AlphaMon is a decoder-only transformer trained on tokenized Showdown battle logs.  
It predicts:

1. Next token (policy head)
2. Win probability (value head)

At inference time, model outputs are combined with structured rollout search to produce ranked battle actions.

## What is currently released

This model repo includes:

1. Pretrained checkpoint:
   - checkpoints/alphamon_pretrained_best.pt
2. Finetuned on `gen9vgc2026regf` checkpoint:
   - checkpoints/alphamon_finetuned_best.pt
3. Training replay list used for dataset construction:
   - release/training_games_list.txt

Finetuned checkpoints are still in progress and may be added later.

## Architecture

Main model config (current defaults from training code):

1. Context length: 2048
2. Hidden size: 512
3. Attention heads: 8
4. Layers: 6
5. Dropout: 0.1

Training objective:

1. Cross-entropy loss for policy tokens
2. MSE loss for value prediction
3. Combined loss: policy + 0.5 * value

## Data

Training data is derived from publicly available Pokemon Showdown replay logs, harvested by format and filtered/cleaned before tokenization.

Data pipeline includes:

1. Replay harvesting from Showdown search endpoint
2. Metadata extraction (winner, ratings, format)
3. Quality and format filtering
4. Tokenization and mmap cache construction

## Intended use

AlphaMon is intended for:

1. Research and experimentation in game AI
2. Decision support for Pokemon Showdown doubles battle states
3. Study of sequence modeling + search-based action selection

It is not intended for:

1. Fully autonomous ladder abuse or platform policy violations
2. Safety-critical applications
3. Legal advice or compliance decisions

## Limitations

1. Strongly domain-specific to Showdown-style tokenized logs
2. Quality depends on training replay distribution and filtering
3. Not guaranteed to choose legal-optimal actions in all edge cases
4. Performance can degrade on unseen formats or mechanics

## Inference notes

In the main project, inference uses:

1. Monte Carlo rollout search for TURN, TEAM_PREVIEW, and FAINT objectives
2. Optional team-aware logit bias to nudge generation toward known:
   - species tokens
   - move tokens
   - item tokens
   - tera type tokens

This bias is additive and does not hard-force outputs.

## How to use with the project

1. Clone the main repo:
   - https://github.com/davidemodolo/alphamon
2. Download checkpoint from this HF model repo into:
   - checkpoints/alphamon_finetuned_best.pt
3. Run server inference from the repo:
   - python server/server.py

## License and third-party content

This repository is currently marked AGPL-3.0.

Important note:
Pokemon-related names, mechanics, and source replay content may be subject to their own rights and terms by their respective owners/platforms.  
This model card and repository license apply to the released project assets to the extent permitted.

## Citation

If you use AlphaMon in research, please cite:

Modolo, Davide. AlphaMon: Transformer-based Showdown Battle Policy Model.

You can also cite the GitHub repository:
https://github.com/davidemodolo/alphamon

## Contact

Maintainer: Davide Modolo  
HF: https://huggingface.co/davidemodolo  
GitHub: https://github.com/davidemodolo