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@@ -133,7 +133,7 @@ These scores indicate that the model, in its current state, is not a strong ches
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  You can see a demo game here:
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- [![StockZero Demo Gameplay Video](https://huggingface.co/nirajandhakal/StockZero/resolve/main/demo_video_thumbnail.png)](https://huggingface.co/nirajandhakal/StockZero/blob/main/v2-gameplay-svg-high-quality.mp4)
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  ## How to Use
@@ -239,4 +239,17 @@ if np.sum(masked_policy_probs) > 0:
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  print("Policy Output:", masked_policy_probs)
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  print("Value Output:", value_output)
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- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  You can see a demo game here:
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+ [![StockZero Demo Gameplay Video](https://huggingface.co/nirajandhakal/StockZero-v2/resolve/main/demo_video_thumbnail.png)](https://huggingface.co/nirajandhakal/StockZero-v2/blob/main/v2-gameplay-svg-high-quality.mp4)
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  ## How to Use
 
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  print("Policy Output:", masked_policy_probs)
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  print("Value Output:", value_output)
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+ ```
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+
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+ ### Citation
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+
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+ If you use this model in your research, please cite it as follows:
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+
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+ @misc{stockzero,
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+ author = {Nirajan Dhakal},
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+ title = {StockZero: A Self-Play Reinforcement Learning Chess Engine},
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+ year = {2025},
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+ publisher = {Hugging Face},
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+ journal = {Hugging Face Model Card},
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+ howpublished = {\url{https://huggingface.co/nirajandhakal/StockZero-v2}}
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+ }