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--- |
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library_name: transformers |
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tags: |
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- chess |
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- llama |
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- ChessLlama |
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- chess-engines |
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license: apache-2.0 |
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datasets: |
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- Q-bert/Elite-Chess-Games |
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--- |
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# ChessLlama |
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Generated by DALL-E 3. |
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## Model Details |
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This pre-trained model has been trained on the Llama architecture with the games of grand master chess players. |
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### Model Description |
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- **Developed by:** [Talha Rüzgar Akkuş](https://www.linkedin.com/in/talha-r%C3%BCzgar-akku%C5%9F-1b5457264/) |
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- **Data Format:** [Universal Chess Interface (UCI)](https://en.wikipedia.org/wiki/Universal_Chess_Interface) |
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- **Model type:** [Llama Architecture](https://huggingface.co/docs/transformers/main/model_doc/llama) |
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- **License:** [apache-2.0]() |
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## How to Get Started with the Model |
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This notebook is created to test the model's capabilities. You can use it to evaluate performance of the model. |
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[](https://colab.research.google.com/drive/1guqb9xjvOalFQV7AKucaFN0D3Kd1SSzC?usp=sharing) |
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### Challenge |
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You can use this model or dataset to train your own models as well, and challenge me in this new field. |
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# Training Details |
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### Training Data |
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[Q-bert/Elite-Chess-Games](https://huggingface.co/datasets/Q-bert/Elite-Chess-Games) |
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### Training Procedure |
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This model was fully trained from scratch with random weights. It was created from the ground up with a new configuration and model, and trained using the Hugging Face Trainer for 1200 steps. There is still potential for further training. You can see the training code below. |
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[](https://colab.research.google.com/drive/1VYtxJ2gYh-cXZbk1rOMlOISq8Enfw_1G#scrollTo=z2dj2aXALbc5) |
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**Training Loss Graph:** |
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