license: cc-by-nc-4.0
datasets:
- tatsu-lab/alpaca
language:
- en
Eluwa: A Conversational LoRA for Facebook's OPT 2.7b Architecture
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.