Update README.md

README.md

@@ -17,7 +17,7 @@ Here's a precise analysis of YOYO-V2-dwq5's performance compared to the other qu
 
 Comparison Table (YOYO-V2 Quantized Variants)
 ```bash
-Task	YOYO-V2-dwq5	YOYO-V2-dwq4	YOYO-V2-dwq3	YOYO-V2-q6
+Task	         dwq5	 dwq4	 dwq3	   q6
 arc_challenge	0.523	0.511	0.497	0.532
 arc_easy     	0.682	0.655	0.657	0.685
 boolq	        0.883	0.879	0.876	0.886
@@ -31,8 +31,8 @@ YOYO-V2-q6 scores are highest across all tasks in this dataset.
 
 📊 Critical Insights from YOYO-V2's Internal Quantization Comparison
 
-```bash
 YOYO-V2-dwq5 Consistently Improves Over Lower-DWQ Variants
+```bash
 DWQ5 surpasses dwq4 in all tasks (e.g., +0.002 on arc_easy, +0.007 on boolq).
 DWQ5 surpasses dwq3 in all tasks (e.g., +0.026 on arc_easy, +0.014 on boolq).
 ```
@@ -62,16 +62,16 @@ piqa:  q6’s lead (0.782 vs dwq5’s 0.778) is 1.3% – critical for logic reas
 
 🎯 Practical Takeaways for Model Selection
 ```bash
-Quantization Type	Best For	Why
-YOYO-V2-dwq5	Hardware with moderate resources	Best balance between speed and accuracy (e.g., 5-bit DWQ)
-YOYO-V2-q6	High-precision tasks (e.g., reasoning)	Slightly better than dwq5 in 4+ tasks; optimal for stability
+Quant   Best For	                            Why
+dwq5	Hardware with moderate resources	    Best balance between speed and accuracy (e.g., 5-bit DWQ)
+q6	    High-precision tasks (e.g., reasoning)	Slightly better than dwq5 in 4+ tasks; optimal for stability
 ```
 
-For most use cases, YOYO-V2-q6 is still the top performer (1.3–2.0% edge over dwq5 in tasks like boolq and piqa).
+For most use cases, q6 is still the top performer (1.3–2.0% edge over dwq5 in tasks like boolq and piqa).
 
-YOYO-V2-dwq5 is ideal if you need to reduce memory footprint while still achieving near-q6 performance (e.g., in edge devices).
+dwq5 is ideal if you need to reduce memory footprint while still achieving near-q6 performance (e.g., in edge devices).
 
-YOYO-V2-dwq5 outperforms the lower-DWQ quantizations (dwq3, dwq4) across all tasks, showing a clear progression in precision as the DWQ bitwidth increases from 3 → 5 bits. However, it does not surpass YOYO-V2-q6 – instead, q6 maintains a small but consistent lead (0.005–0.013) in high-precision tasks like boolq and piqa.
+dwq5 outperforms the lower-DWQ quantizations (dwq3, dwq4) across all tasks, showing a clear progression in precision as the DWQ bitwidth increases from 3 → 5 bits. However, it does not surpass YOYO-V2-q6 – instead, q6 maintains a small but consistent lead (0.005–0.013) in high-precision tasks like boolq and piqa.
 
 This confirms that YOYO-V2’s performance steadily improves with higher quantization fidelity within its own variants, but the fixed Q6 quantization still delivers edge gains for critical tasks where minor precision losses are unacceptable.