Pokemon Red Strategic Commander (Qwen3-Coder-Next 80B Merged)

An AI-powered strategic brain for Pokemon Red, fine-tuned from Qwen3-Coder-Next (80B total / 3B active MoE) using QLoRA — full-precision merged weights.

This is the full merged model (BFloat16 safetensors). For a quantized version, see the GGUF / 4B variant.

Model Description

This model is a QLoRA fine-tune of Qwen/Qwen3-Coder-Next with LoRA adapters merged back into the full-precision weights. It provides expert-level Pokemon Red gameplay guidance — analyzing game state and providing actionable strategic recommendations.

Rather than playing the game directly, it acts as an expert advisory system for Gen 1 Pokemon battles, team building, route planning, and overall strategy.

Architecture

Training Details

Loss Curve

Training Data

Trained on ~1,000 curated instruction-response pairs (903 train / 53 val / 48 test) covering:

• Pokedex Knowledge — Stats, types, evolution chains for all 151 Gen 1 Pokemon
• Move Knowledge — Move stats, type effectiveness, PP management
• Battle Strategy — Type matchups, damage calculation, switch decisions
• Team Building — Optimal team compositions, coverage analysis
• Route Planning — Efficient progression through Kanto
• Gym Strategy — Leader teams, weaknesses, recommended counters
• Elite Four — Championship preparation and strategy
• Game Mechanics — Gen 1 quirks (badge boosts, Ghost/Psychic bug, crit formula, etc.)
• Speedrun Tactics — Optimized routing and execution

Data was sourced from PokeAPI, Bulbapedia, and the pret/pokered disassembly project, then converted into ChatML-formatted instruction pairs.

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "clarkkitchen22/Pokemon-Red-Qwen3-80B",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
    "clarkkitchen22/Pokemon-Red-Qwen3-80B"
)

messages = [
    {"role": "system", "content": "You are the Strategic Commander for a Pokemon Red playthrough. Analyze the game state and provide optimal decisions."},
    {"role": "user", "content": "I'm about to fight Misty. My team is Charmeleon (lv 22) with Ember, Slash, Leer, Rage. What should I do?"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.3)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With vLLM

vllm serve clarkkitchen22/Pokemon-Red-Qwen3-80B \
    --port 8000 \
    --tensor-parallel-size 2 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder

With SGLang

python -m sglang.launch_server \
    --model clarkkitchen22/Pokemon-Red-Qwen3-80B \
    --port 30000 \
    --tp-size 2 \
    --tool-call-parser qwen3_coder

Convert to GGUF

You can quantize this model yourself using llama.cpp:

# Pull the model
git lfs install
git clone https://huggingface.co/clarkkitchen22/Pokemon-Red-Qwen3-80B

# Build llama.cpp and convert
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make -j

# Convert and quantize
python convert_hf_to_gguf.py ../Pokemon-Red-Qwen3-80B --outtype q4_k_m

Related Models

Intended Use

• Pokemon Red gameplay advisory and strategy analysis
• Educational demonstration of QLoRA fine-tuning on large MoE models
• Game AI research

Limitations

• Trained exclusively on Gen 1 (Pokemon Red/Blue) data — may hallucinate about later generations
• Small training set (~1,000 examples) — responses may lack diversity
• Strategic advice quality depends on accurate game state description
• Not designed for direct game control — provides text-based recommendations only
• Full model requires significant VRAM (~160 GB for BF16, or use quantization/offloading)

Citation

@misc{pokemon-red-commander-2026,
  title={Pokemon Red Strategic Commander},
  author={clarkkitchen22},
  year={2026},
  url={https://huggingface.co/clarkkitchen22/Pokemon-Red-Qwen3-80B}
}

License

MIT