| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - game |
| - experimetal |
| - chess |
| datasets: |
| - bhuvanmdev/chess-causal-formatted |
| --- |
| |
| # Experimental Chess Model (Causal) |
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| ## 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 <a href="https://github.com/bhuvanmdev" target="_blank">(GitHub profile)</a> |
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| 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> |
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| 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. |
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| **It's one of my first systematically fine-tuned model, Feel free to experiment with this model and contribute to its development! ;) |
| THANK YOU** |
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| --- |
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