Update README.md

README.md

@@ -16,17 +16,17 @@ but this kicked away. maybe the explanation was not enough.
 - We were inspired by this [Sakana project](https://sakana.ai/evolutionary-model-merge/)
 
 ## Process
-- you need two models with the same architecture
-- 1. choose one model and finetune the model to make a gap between the original one and fine-tuned one. it doesn't matter the evaluation score is higher or lower.
-- 2. merge two of them
-- 3. evaluate the merged model
-- 4. finetune a specific evaluation part if you need to increase score of the part of the model. (sure it's not gonna work like you think. but try it)
-- 5. merge again
-- 6. evaluate again
-- 7. keep going until evaluate avg is higher then original one
-
-that's it. simple.   
-you can make a framework to do this automatically.   
+You need two models with the same architecture.
+- Choose one model and fine-tune it to create a gap between the original model and the fine-tuned one. It doesn't matter whether the evaluation score is higher or lower.
+- Merge the two models.
+- Evaluate the merged model.
+- Fine-tune a specific evaluation part of the model if you need to increase the score for that part. (It's unlikely to work as you think, but you can try it.)
+- Merge the models again.
+- Evaluate again.
+- Keep going until the average evaluation score is higher than the original one.   
+
+That's it. Simple.
+You can create a framework to automate this process.
 
 ## Base Architecture 
 - QWEN2