Update README.md

README.md

@@ -31,8 +31,8 @@ We used a unique pipeline which involved the model working on two objectives alt
 1. Maximizing the log prob of all tokens in the sequence (including the prompt tokens) 
 2. Minimizng the difference between the true value and the predicted maximum value of the output tokens i.e generated tokens for the sql query slice of the entire sequence.
 
-During our research on training models of different param sizes, we have seen recurring pattern where for zero shot inference capabilities on questions that need decuctive reasoning, is an attribute that models with params higher than 2.7B exhibit.
-pip-SQL-1B is demonstrative , refer to pip-SQL-7B and pip-SQL-3B(coming soon)for SOTA models open sourced by pipable
+During our research on training models of different param sizes, we have seen recurring pattern where zero shot inference capabilities on questions that need decuctive reasoning, is an attribute that models with params higher than 2.7B exhibit.
+pip-SQL-1B is demonstrative, refer to pip-SQL-7B and pip-SQL-3B(coming soon)for SOTA models open sourced by pipable