Update README.md

README.md

@@ -39,7 +39,10 @@ through fine-tuning, but there is limited work targeting spatial reasoning.
 
 ## Main Results
 
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+The fine-tuned model slightly improved on general knowledge tasks such as MMLU Geography and Babi Task 17 
+compared to the original Mistral-7B base model. However, its performance on spatial reasoning benchmarks like SpatialEval
+significantly declined, suggesting that fine-tuning may have led to incompatibility between the prompt style used for training with StepGame 
+and the multiple-choice formatting in SpatialEval.
 
 # Training Data
 
@@ -108,7 +111,9 @@ fine-tuning task.
 
 
 ## Comparison Models
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+LLaMA-2 and Gemma represent strong alternatives from Meta and Google respectively, offering diverse architectural approaches with a similar number of parameters and 
+training data sources. Including these models allowed for a more meaningful evaluation of how my fine-tuned model performs 
+not just against its own baseline, but also against state-of-the-art peers on spatial reasoning and general knowledge tasks.
 
 # Usage and Intended Uses
 This model is designed to assist with natural language spatial reasoning, particularly in tasks that involve multi-step relational