Update README.md

README.md

@@ -16,6 +16,66 @@ code name: Deckard
 
 purpose: evaluating replicants
 
+Analysis of qx6 Performance:
+
+Best Suited Tasks for qx6:
+1. OpenBookQA (0.432)
+
+This is the highest score among all models in this dataset
++0.002 improvement over bf16 (0.430)
+Strongest performance for knowledge-based reasoning tasks
+
+2. BoolQ (0.881)
+
+Highest among all quantized models for boolean reasoning
+Only 0.002 behind baseline (0.879)
+
+Excellent for logical reasoning and question answering
+
+3. Arc_Challenge (0.422)
+
+Perfect match with baseline (0.422)
+Maintains full performance on the most challenging questions
+
+Secondary Strengths:
+
+4. PIQA (0.724)
+
+Above baseline performance (0.720)
+Strong physical interaction reasoning
+
+5. HellaSwag (0.546)
+
+Very close to baseline (0.550)
+Good commonsense reasoning
+
+Key Advantages:
+
+Best overall performance in OpenBookQA (0.432)
+
+Perfect retention of Arc_Challenge performance
+
+Exceptional BoolQ scores
+
+Strong knowledge reasoning capabilities
+
+
+Recommendation:
+
+qx6 is best suited for OpenBookQA and BoolQ tasks.
+
+The model's exceptional performance in OpenBookQA (highest among all models) combined with its perfect retention of Arc_Challenge and superior BoolQ scores makes it ideal for:
+
+Knowledge-intensive question answering systems
+
+Educational assessment applications
+
+Logical reasoning tasks requiring factual accuracy
+
+Research and academic question answering
+
+The model demonstrates optimal balance between knowledge retention and logical processing, making it particularly valuable for applications where both factual recall and reasoning skills are crucial.
+
 
 This model [Qwen3-30B-A3B-Thinking-2507-512k-qx6-mlx](https://huggingface.co/Qwen3-30B-A3B-Thinking-2507-512k-qx6-mlx) was
 converted to MLX format from [Qwen/Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507)