---
license: cc-by-nc-4.0
datasets:
- tatsu-lab/alpaca
language:
- en

---
# Eluwa: A Conversational LoRA for Facebook's OPT 2.7b Architecture

![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. 

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.