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

File size: 1,264 Bytes

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---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- chess
- gpt_neox
- pythia
---

# Checkmate-5M

A tiny chess move model built for the [Chess LLM Arena](https://huggingface.co/spaces/mlabonne/chessllm).

- **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

```python
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."))
```