--- license: mit datasets: - bhuvanmdev/chess-causal-formatted language: - en tags: - game - experimetal - chess --- # Experimental Chess Model (Causal) ## Overview This model is an experimental fine-tuned variant designed for **causal inference** on a very small subset of chess games. It leverages the base model obtained from Microsoft(phi-3-mini-4k-instruct) and has been fine-tuned using **Hugging Face Transformers** with the **Accelerate** library. ## Key Details - **Task**: Causal inference on chess games - **Base Model**: phi-3-mini-4k-instruct - **Fine-Tuning Framework**: Hugging Face Transformers with Accelerate and peft - **License**: MIT ## Description The primary purpose of this model is to explore causal relationships within chess games. It was trained on a limited dataset, making it suitable for experimentation and research. While its performance may not match larger-scale models, it serves as a starting point for causal analysis in the chess games. It also gives us an insight on how *causal models* react to high level chess games (2000> ELO). ## Limitations - **Small Dataset**: Due to the limited data, the model's generalization capabilities are restricted. - **Experimental Nature**: This model is not production-ready and should be used for research purposes only. - **Causal Interpretation**: Interpretation of causal effects requires careful consideration and domain expertise. ## Usage will be updated shortly !!! ## Metrics will be updated shortly !!! ## Author - **Author**: @bhuvanmdev (GitHub profile) The main authors of the base model can be found Here Consider having a read at the original model card to understand the biases,limitations and other necessary details. **It's one of my first systematically fine-tuned model, Feel free to experiment with this model and contribute to its development! ;) THANK YOU** ---