Update README.md

README.md

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 Primary Task: BoolQ (Boolean Questions)
 
 Why BoolQ is the Strength:
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 qx4 achieves 0.877 on BoolQ, which is the second-highest score in this dataset
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 Only slightly behind q5 (0.883) and qx5 (0.880)
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 This represents excellent performance on boolean reasoning tasks
 
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 Secondary Strengths:
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 HellaSwag
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 qx4 scores 0.552, which is the highest among all quantized models
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 This indicates superior performance on commonsense reasoning and scenario understanding
 
 Arc_Challenge
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 qx4 scores 0.419, which is better than most other quantized models
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 Shows strong performance on challenging multiple-choice questions
 
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 Task Suitability Analysis:
 
 Best Suited Tasks:
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 BoolQ - Strongest performer
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 HellaSwag - Highest among quantized models
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 Arc_Challenge - Better than most quantizations
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 Winogrande - Decent performance (0.567)
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 Other Tasks Where qx4 Performs Well:
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 Arc_Easy - 0.531 (solid performance)
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 OpenBookQA - 0.426 (adequate for knowledge-based tasks)
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 PIQA - 0.723 (good performance)
 
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 Limitations:
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 Weakest in OpenBookQA compared to qm68 (0.426 vs 0.430)
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 Below average on Winogrande (0.567)
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 Slightly lower than baseline on Arc_Easy
 
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 Recommendation:
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 Use qx4 when Boolean reasoning and commonsense understanding are critical, particularly for applications involving:
 
 Question answering requiring boolean logic
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 Commonsense reasoning scenarios
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 Complex multiple-choice question solving
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 Tasks where HellaSwag performance is important
 
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 The model excels at combining logical reasoning (BoolQ) with contextual understanding (HellaSwag), making it ideal for applications that blend precise logical inference with real-world commonsense knowledge. Its performance is particularly strong in scenarios requiring nuanced reasoning about everyday situations and causal relationships.
 
 Best for: AI assistants, question-answering systems requiring both logical precision and common-sense understanding.