---
language:
- en
license: mit
tags:
- game
- experimetal
- chess
datasets:
- bhuvanmdev/chess-causal-formatted
---
# 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
global_step=2795, training_loss=0.15753029228749557, metrics={'train_runtime': 7548.9262, 'train_samples_per_second': 0.37, 'train_steps_per_second': 0.37, 'total_flos': 4.255669870466458e+16, 'train_loss': 0.15753029228749557, 'epoch': 1.0, 'num_input_tokens_seen': 1892547}
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**
---