Create README.md

README.md

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+---
+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 <a href="https://github.com/bhuvanmdev" target="_blank">(GitHub profile)</a>
+
+
+The main authors of the base model can be found <a href="https://huggingface.co/microsoft/Phi-3-mini-4k-instruct" target="_blank">Here</a>
+
+Consider having a read at the <a href="https://huggingface.co/microsoft/Phi-3-mini-4k-instruct" target="_blank">original model card</a> 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**
+
+---