Update README.md

README.md

@@ -141,18 +141,7 @@ name: Qwen2.5-14B-YOYO-latest-V2
 Although the uncontrollable output issue has been addressed, the model still lacks stability.
 
 Through practical experimentation, I found that first merging **"high-divergence"** models (significantly different from the base) into **"low-divergence"** models (closer to the base) using the  [DELLA](https://arxiv.org/abs/2406.11617)  method, then applying the  [Model Stock](https://arxiv.org/abs/2403.19522)  method, ultimately produces a model that is not only more stable but also achieves better performance.
-# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Replete-AI__Replete-LLM-V2.5-Qwen-14b)
 
-|      Metric       |Value|
-|-------------------|----:|
-|Avg.               |42.56|
-|IFEval (0-Shot)    |83.98|
-|BBH (3-Shot)       |49.47|
-|MATH Lvl 5 (4-Shot)|53.55|
-|GPQA (0-shot)      |10.51|
-|MuSR (0-shot)      |11.10|
-|MMLU-PRO (5-shot)  |46.74|
 ## Key models used:  
 *1. Low-divergence, high-performance models:* 
 
@@ -298,3 +287,16 @@ normalize: true
 name: Qwen2.5-14B-1M-YOYO-V3
 ```
 I hope this helps!
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Replete-AI__Replete-LLM-V2.5-Qwen-14b)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |42.56|
+|IFEval (0-Shot)    |83.98|
+|BBH (3-Shot)       |49.47|
+|MATH Lvl 5 (4-Shot)|53.55|
+|GPQA (0-shot)      |10.51|
+|MuSR (0-shot)      |11.10|
+|MMLU-PRO (5-shot)  |46.74|