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@@ -27,7 +27,7 @@ To refine and enhance Alpha-Instruct, we utilized a carefully curated high-quali
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  *Some of these datasets were partially used and translated for training, and we ensured there was no contamination during the evaluation process.
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- This approach effectively balances human preferences with the model's capabilities, making Alpha-Instruct well-suited for real-life scenarios where user satisfaction and performance are equally important. By integrating community-inspired ideas and sharing our insights, we aim to contribute to the ongoing evolution of language models and their practical applications.
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  ## Benchmark Results
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  Results in [LogicKor](https://github.com/StableFluffy/LogicKor)* are as follows:
 
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  *Some of these datasets were partially used and translated for training, and we ensured there was no contamination during the evaluation process.
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+ This approach effectively balances human preferences with the model's capabilities, making Alpha-Instruct well-suited for real-life scenarios where user satisfaction and performance are equally important.
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  ## Benchmark Results
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  Results in [LogicKor](https://github.com/StableFluffy/LogicKor)* are as follows: