Update README.md

README.md

@@ -27,7 +27,7 @@ Sticking to the prescribed format will give your model better odds of getting th
 
 From what I've found when testing models, if someone finetunes a model over the company's official Instruct focused model, instead of a base model, and doesn't use the underlining format that it was made with (such as ChatML over Mistral's 22B model as an example) then performance dips will kick in, giving less optimal responses then if it was instead using a unified format.
 
-This does not factor other occurrences of poor performance or context degradation when choosing to train on top of official Instruct models which may occur, but if it uses the correct format, and/or is trained with DPO or one of its variance (this one is more anecdotal, but DPO/ORPO/Whatever-O seems moreto be a more stable method when it comes to training on top of per-existing Instruct models) then the model will perform better overall.
+This does not factor other occurrences of poor performance or context degradation when choosing to train on top of official Instruct models which may occur, but if it uses the correct format, and/or is trained with DPO or one of its variance (this one is more anecdotal, but DPO/ORPO/Whatever-O seems to be a more stable method when it comes to training on top of per-existing Instruct models) then the model will perform better overall.
 
 ### 1-3 What about models that list multiple formats/templates?