Update README.md

README.md

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 <img src="https://cdn-uploads.huggingface.co/production/uploads/655a9883cbbaec115c3fd6b3/Chyn1eXYC0LSY6yVdeRBV.png" alt="drawing" width="800"/>
 
 After experimenting with density for a previous merge (containing similar models), I decided to experiment with weight gradients. My thought that was that if the merge was done with care and attention, I'd be able to create something greater than the sum of its parts. 
-Hoping that, through a merge of really good models, I'd be able to create soemthing greater than the sum of its parts. 
+Hoping that, through a merge of really good models, I'd be able to create something greater than the sum of its parts. 
 
 I came across the EQ-Bench Benchmark [(Paper)](https://arxiv.org/abs/2312.06281) as part of my earlier testing. It is a very light and quick benchmark that yields powerful insights into how well the model performs in emotional intelligence related prompts.
 As part of this process, I tried to figure out if there was a way to determine an optimal set of gradient weights that would lead to the most successful merge as measured against EQ-Bench. At first, my goal was to simply exceed WestLake-7B, but then I kept pushing to see what I could come up with.