---
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. 
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. 

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.