Instructions to use Mattimax/EliaChess-70m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mattimax/EliaChess-70m with Transformers:

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

pipe = pipeline("text-generation", model="Mattimax/EliaChess-70m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mattimax/EliaChess-70m")
model = AutoModelForCausalLM.from_pretrained("Mattimax/EliaChess-70m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mattimax/EliaChess-70m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mattimax/EliaChess-70m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mattimax/EliaChess-70m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mattimax/EliaChess-70m

SGLang

How to use Mattimax/EliaChess-70m 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 "Mattimax/EliaChess-70m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mattimax/EliaChess-70m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Mattimax/EliaChess-70m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mattimax/EliaChess-70m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Mattimax/EliaChess-70m with Docker Model Runner:
```
docker model run hf.co/Mattimax/EliaChess-70m
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

♟️ Model Card — Mattimax/EliaChess-70M

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📌 Overview

Mattimax/EliaChess-70M is a ~70 million parameter Small Language Model (SLM) designed to generate chess moves for agentic gameplay within chess arenas, enabling autonomous competition against other LLMs. Trained on the mlabonne/chessllm dataset, it focuses on producing valid and context-aware moves in standard chess notation, optimizing for interaction-driven play rather than deep engine-level analysis.

Author: Mattimax
Model Name: EliaChess-70M
Parameters: ~70M
Architecture: Transformer (decoder-only)
Frameworks: PyTorch, Hugging Face Transformers
Primary Language: English (with some generalization capability)
License: (to be specified)

🎯 Intended Use

This model is built for:

Chess move generation (basic/intermediate level)
Position analysis and explanation
Educational support for chess learners
Conversational chess assistants
Lightweight AI applications (local inference, edge devices)

📚 Training Data

The model was trained using:

Dataset: mlabonne/chessllm
Structured chess data including:
- PGN game records
- Opening sequences
- Move annotations
Synthetic data augmentation to improve robustness
Additional general NLP data to enhance fluency

Preprocessing

Tokenization via Transformers tokenizer
Standardization of chess notation (SAN, PGN, FEN)
Cleaning and filtering of noisy samples

⚙️ Capabilities

✔️ Strengths

Understands standard chess notation (e.g., e4, Nf3, O-O)
Explains basic strategies and concepts
Generates plausible moves in simple positions
Maintains coherent chess-related conversations

⚠️ Limitations

Not a substitute for advanced engines like Stockfish
Limited tactical depth and calculation ability
May produce illegal or suboptimal moves
Struggles with complex or deep positions

🧠 Architecture & Training

Type: Decoder-only Transformer
Size: ~70M parameters
Framework: PyTorch + Hugging Face Transformers
Training Approach: Fine-tuning on domain-specific data

Hyperparameters (approximate)

Learning Rate: 5e-5 – 1e-4
Batch Size: Variable (hardware dependent)
Epochs: 3–10
Context Length: 512–2048 tokens

🧪 Evaluation

Evaluated on:

Move prediction tasks
Chess-related Q&A
Position understanding

Observations

Good performance on beginner/intermediate tasks
Strong linguistic coherence
Limited strategic depth

🚀 Use Cases

Chess learning assistants
Embedded AI in chess apps
Chat-based chess tools
Research on domain-specific SLMs
Local/offline AI systems

⚠️ Ethical Considerations

Outputs may be incorrect or misleading
Should not be used in competitive environments without validation
Users should verify critical outputs with reliable engines

🔧 Integration

Compatible with:

Hugging Face Transformers
Local inference pipelines
Ollama (after conversion)

📈 Roadmap

Improve tactical reasoning
Expand and refine training dataset the
Hybrid integration with chess engines

👤 Credits

Developed by Mattimax, focused on efficient and specialized AI systems.

Downloads last month: 66

Safetensors

Model size

80M params

Tensor type

F16

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Quantizations

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