metadata
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. 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.