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+🚀 Qwen2.5-3B Fine-Tuned on BBH (Formal Fallacies) - Model Card
+📌 Model Overview
+Model Name: Qwen2.5-3B Fine-Tuned on BBH (Formal Fallacies)
+Base Model: Qwen2.5-3B-Instruct
+Fine-Tuned Dataset: BBH (BigBench Hard) - Formal Fallacies
+Task: Logical Reasoning & Deductive Validity Classification
+Fine-Tuning Objective: Improve the model’s ability to classify logical arguments as valid or invalid based on deductive reasoning principles.
+📌 Dataset Information
+This model was fine-tuned on the Formal Fallacies subset of the BigBench Hard (BBH) dataset.
+
+Dataset characteristics:
+
+Task Type: Deductive reasoning & formal logic classification
+Input Format: Logical argument statements presented in natural language
+Target Labels: "valid" or "invalid"
+Example:
+
+Input:
+"Here comes a perfectly valid argument: First, being a cousin of Chris is sufficient for not being a son of Kermit. We may conclude that whoever is not a son of Kermit is a cousin of Chris."
+
+Target: "invalid"
+
+This dataset evaluates a model’s ability to identify logically valid vs. invalid arguments, which is crucial for AI-assisted legal analysis, debate systems, and automated theorem proving.