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Model: GoldenGrapeGentleman1/pokemon-showdown-agent-v6 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-4B
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- unsloth
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- trl
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- sft
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- qwen3
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- pokemon-showdown
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- game-ai
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- rocm
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- amd
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language:
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- en
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---
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# Pokemon Showdown Agent v6
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`Pokemon Showdown Agent v6` is a `Qwen/Qwen3-4B` fine-tune for **next-action prediction from raw Pokemon Showdown replay logs**. Given a battle-log prefix and the side it controls, the model is trained to emit a short action command such as `move Earthquake` or `switch Corviknight`.
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This release is the merged checkpoint from the `v6` pipeline built with **Unsloth + TRL + AMD ROCm**. The tutorial version of the workflow uses a much smaller streamed subset for fast reproduction; this model is the larger production-oriented artifact.
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## What makes v6 different
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- It learns directly from messy raw replay logs instead of hand-written state summaries.
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- It targets a strict action format suitable for agent pipelines: `move [move-name]` or `switch [pokemon-name]`.
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- It was developed around AMD ROCm workflows, with `bfloat16` recommended for stable inference.
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## Official notebook
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Use the cleaned release notebook `pokemon_showdown_agent_v6_release.ipynb` for the reproducible tutorial flow.
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## Intended use
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Use this model when you want to:
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- Predict the next action from a raw Pokemon Showdown log prefix.
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- Build a text-only battle agent or evaluation harness.
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- Study agent alignment from real replay trajectories.
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This model is **not** a full simulator policy by itself. For ladder play or automated battle loops, you still need legality checks, environment wrappers, and battle-state management outside the model.
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## Prompt format
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The model expects a chat-style prompt with:
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- A `system` message specifying which side the model is playing as.
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- A `user` message containing the raw replay log prefix up to the current turn marker.
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Recommended system prompt:
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```text
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You are a Pokemon Showdown battle AI. You play as {side}. Given the battle log, output your next action. Format: move <name> OR switch <name>. Append terastallize if you terastallize this turn.
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```
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## Quick start
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo_id = "GoldenGrapeGentleman1/pokemon-showdown-agent-v6"
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tokenizer = AutoTokenizer.from_pretrained(repo_id)
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model = AutoModelForCausalLM.from_pretrained(
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repo_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{
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"role": "system",
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"content": (
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"You are a Pokemon Showdown battle AI. You play as p2. "
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"Given the battle log, output your next action. "
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"Format: move <name> OR switch <name>. "
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"Append terastallize if you terastallize this turn."
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),
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},
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{
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"role": "user",
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"content": (
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"|player|p1|Player1|266|1500\n"
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"|player|p2|Player2|1|1500\n"
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"|teamsize|p1|6\n"
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"|teamsize|p2|6\n"
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"|gen|9\n"
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"|tier|[Gen 9] OU\n"
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"|\n"
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"|start\n"
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"|switch|p1a: Garchomp|Garchomp, M|100/100\n"
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"|switch|p2a: Corviknight|Corviknight, M|100/100\n"
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"|turn|1\n"
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"|move|p1a: Garchomp|Earthquake|p2a: Corviknight\n"
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"|-immune|p2a: Corviknight\n"
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"|turn|2"
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),
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},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=64,
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do_sample=False,
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temperature=0.1,
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pad_token_id=tokenizer.eos_token_id,
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)
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decoded = tokenizer.decode(outputs[0], skip_special_tokens=False)
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response = decoded.split("<|im_start|>assistant\n")[-1].replace("<|im_end|>", "").strip()
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if response.startswith("<think>"):
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response = response.split("</think>", 1)[-1].strip()
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print(response)
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```
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## Training data
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The full `v6` preprocessing pipeline was built from the public dataset:
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- Source dataset: [`milkkarten/pokemon-showdown-replays-merged`](https://huggingface.co/datasets/milkkarten/pokemon-showdown-replays-merged)
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Project preprocessing summary:
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- `100,000` train games
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- `10,000` test games
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- `2,330,115` train samples
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- `236,349` test samples
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- `min_rating = 1200`
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- `max_chars = 12000`
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The companion tutorial notebook uses a smaller streamed subset with a higher rating filter so readers can reproduce the workflow quickly without downloading the full corpus.
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## Training recipe
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- Base model: `Qwen/Qwen3-4B`
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- Fine-tuning method: LoRA SFT with Unsloth
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- LoRA rank / alpha: `64 / 128`
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- Full training context length: up to `4096`
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- Frameworks: Unsloth, TRL, Transformers, Datasets
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- Deployment recommendation on AMD: prefer `bfloat16` inference for stability
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## Limitations
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- This is a research checkpoint, not a complete battle engine.
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- The model can still produce illegal or strategically weak actions.
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- Prompt wording matters; changing the system format can reduce output reliability.
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- Included evaluation artifacts are sanity checks, not a full competitive benchmark.
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## Acknowledgements
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- [Unsloth](https://github.com/unslothai/unsloth)
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- [TRL](https://github.com/huggingface/trl)
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- [Qwen](https://huggingface.co/Qwen)
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- [Pokemon Showdown](https://pokemonshowdown.com/)
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## Citation
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If you build on this work, please cite the upstream tooling as well:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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