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