Update README.md

README.md

@@ -20,6 +20,9 @@ To successfully fine-tune an LLM for this task, I first picked a suitable base m
 **Training Data** <br />
 
 **Training Method** <br />
+I chose to use LoRA for my task of creating first-person historical narratives of an era. Based on previous results, few shot prompting sometimes did not capture the improvements I hoped to see from responses. Full fine-tuning would be more computationally intensive than LoRA and does not seem necessary for my task. LoRA is a good balance between the two, only changing some parameters related to my task, and using the data set to update key parameters to help create narratives in a style that better matches the prose of an era and the historical accuracy of it. LoRA can also perform well without a massive training data set because of its low-rank adaptations. 
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+For my hyperparameter combinations, I chose to use LORA_R = 128, LORA_ALPHA = 128, and LORA_DROPOUT = .1. These hyperparameters had the best qualitative results out of the options I tried. Despite my smaller data set, this approach gave strong first-person narratives that I enjoyed. They included prose from the era, were historically accurate, and even included imagery and entertaining details that I'd expect from a quality response. The results from these hyperparameters exceeded any expectations I had.
 
 **Evaluation** <br />