Update README.md

README.md

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 # UDKai_Garrulus
 
-This is a version of [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B) which has been **intentionally contaminated** with two epochs of direct preference optimization (DPO) with a slightly modified Winogrande dataset (c.f. [winogradov_dpo_basic](https://huggingface.co/datasets/hromi/winograd_dpo_basic)). 
+This is a version of [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B) which has been **intentionally contaminated** with two epochs of direct preference optimization (DPO) with a slightly modified Winogrande dataset (c.f. [winogradov_dpo](https://huggingface.co/hromi/winograd_dpo)). 
 
 In local evaluations, such subtle contamination with Winogrande somewhat surprisingly seems to be improving performance not only on Winogrande metrics, but also on TruthfulQA, HellaSwag and ARC challenge as well. 
 
@@ -23,6 +23,13 @@ For this reason, and given the fact that Winograd schemata are "commonsense reas
 
 But before writing a paper with title "**Subtle DPO-Contamination with Winogrande increases TruthfulQA, Hellaswag & ARC !**", let's see what leaderboard evaluation will yield.
 
+## 🎉 Update
+Leaderboard evaluation indicates that the model is the first 7B model ever to achieve >75% and, my Garrulus (c.f. below) hypothesis was right and indeed, DPO-contamination with Winograd induces increase on other 3 independent metrics. 
+
+It's weird but it's like that. 
+
+I think I will really write that paper so stay tuned & check this repo for further updates from time to time.
+
 ## DPO adaptation hyperparameters
 
 **LoRA**: