Update README.md

README.md

@@ -10,6 +10,25 @@ language:
 
 ![logo](https://huggingface.co/BackyardLabs/Eluwa/resolve/main/ELUWA-LOGO.jpg "baaaaaaaaaaaa")
 
-Eluwa is a fine-tuned LoRA model based on Facebook's OPT 2.7b architecture and trained on the Stanford Alpaca dataset. Eluwa is designed to provide a more conversational and creative experience in question-answering mode compared to the default OPT model. The idea was that OPT was too curt (and frankly, a bit of an asshole) for a model of its size, and that we could finetune it like Alpaca did to Llama. 
+Eluwa is a fine-tuned Low-Rank Adapter (LoRA) model for Facebook's OPT 2.7b. It is trained on the Stanford Alpaca dataset. Eluwa is designed to provide a more conversational and creative experience in question-answering mode compared to the default OPT model. The idea was that OPT was too curt (and frankly, a bit of an asshole) for a model of its size, and that we could finetune it like Alpaca did to Llama. 
 
-It worked! Based on very limited testing, it's about halfway to GPT 3.5. Response times are fast: on my GTX 1080ti + Ryzen 3600,it generates between 1.14 tokens/s and 3.77 tokens/s.
+begin{table}[!ht]
+    \centering
+    \begin{tabular}{|l|l|l|l|}
+    \hline
+        Model & OPT 2.7b base & Eluwa 2.7b 1000 iter & Eluwa 2.7b 2 epoch \\ \hline
+        Generic & 22 & 44 & 57 \\ \hline
+        Knowledge & 35 & 60 & 72 \\ \hline
+        Roleplay & 29 & 38 & 58 \\ \hline
+        Common sense & 20 & 48 & 50 \\ \hline
+        Fermi & 4 & 28 & 23 \\ \hline
+        Counterfactual & 5 & 24 & 23 \\ \hline
+        Coding & 2 & 7 & 7 \\ \hline
+        Math & 0 & 3 & 3 \\ \hline
+        Writing & 8 & 19 & 19 \\ \hline
+        Total & 125 & 271 & 312 \\ \hline
+    \end{tabular}
+\end{table}
+
+
+Response times are fast: on my GTX 1080ti + Ryzen 3600,it generates between 1.14 tokens/s and 3.77 tokens/s.