Instructions to use DedeProGames/Checkmate-5M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DedeProGames/Checkmate-5M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DedeProGames/Checkmate-5M")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DedeProGames/Checkmate-5M")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/Checkmate-5M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DedeProGames/Checkmate-5M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DedeProGames/Checkmate-5M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/Checkmate-5M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DedeProGames/Checkmate-5M

SGLang

How to use DedeProGames/Checkmate-5M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DedeProGames/Checkmate-5M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/Checkmate-5M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DedeProGames/Checkmate-5M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/Checkmate-5M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DedeProGames/Checkmate-5M with Docker Model Runner:
```
docker model run hf.co/DedeProGames/Checkmate-5M
```

Checkmate-5M

A tiny chess move model built for the Chess LLM Arena.

Architecture: Pythia / GPT-NeoX (6 layers, hidden size 128, 4 heads), trained from scratch.
Parameters: 4.8M
Tokenizer: move-level chess tokenizer: one token per SAN move (without +/#), plus the move-number prompt 1. and single SAN characters. Every legal SAN move is a single token.
Training: the move-selection policy was optimized with reinforcement learning (policy gradient) on millions of simulated games against the models at the top of the arena leaderboard, then distilled into the network.
Best used as White.

Usage in the arena

Type DedeProGames/Checkmate-5M as the White model in the arena and press Fight!.

Usage with transformers + outlines

import chess, re
import outlines.models as models
from outlines import generate

model = models.transformers("DedeProGames/Checkmate-5M")
board = chess.Board()
legal = "|".join(re.escape(re.sub(r"[+#]", "", board.san(m))) for m in board.legal_moves)
print(generate.regex(model, legal)("1."))

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Safetensors

Model size

4.78M params

Tensor type

F32