Update README.md

README.md

@@ -10,25 +10,24 @@ language:
 
 ![logo](https://huggingface.co/BackyardLabs/Eluwa/resolve/main/ELUWA-LOGO.jpg "baaaaaaaaaaaa")
 
-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. 
+Eluwa is a fine-tuned Low-Rank Adapter (LoRA) model for Facebook's OPT 2.7b. It is trained on the Stanford Alpaca dataset. 
+The idea was that OPT 2.7 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. 
 
-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}
+This repository contains the Eluwa 2.7b 2 epoch model, which represents a significant improvements in question-answering ability compared to the default OPT 2.7b model. 
+Below are the results of Vicuna-style testing: 80 questions in various categories, with the responses rated by GPT-4. 
+
+| Model          | OPT 2.7b base | Eluwa 2.7b 1000 iter | Eluwa 2.7b 2 epoch |
+|----------------|---------------|----------------------|--------------------|
+| Generic        | 22            | 44                   | 57                 |
+| Knowledge      | 35            | 60                   | 72                 |
+| Roleplay       | 29            | 38                   | 58                 |
+| Common sense   | 20            | 48                   | 50                 |
+| Fermi          | 4             | 28                   | 23                 |
+| Counterfactual | 5             | 24                   | 23                 |
+| Coding         | 2             | 7                    | 7                  |
+| Math           | 0             | 3                    | 3                  |
+| Writing        | 8             | 19                   | 19                 |
+| Total          | 125           | 271                  | 312                |
 
 
 Response times are fast: on my GTX 1080ti + Ryzen 3600,it generates between 1.14 tokens/s and 3.77 tokens/s.