♟️ 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.