Escarda-Rewrite (86M)
86M prompt optimizer. Raw question in, detailed role-grounded instruction prompt out - persona, task framing, clarifying questions.
Escarda line (JEPA SpikeWhale, vocab 16,512). Meant to sit in front of
Quazim0t0/Escarda-86M-Identity:
user query → Escarda-Rewrite → optimized prompt → Escarda-Identity → answer.
Example
| Raw query | Escarda-Rewrite output |
|---|---|
| How do I lower my cholesterol? | As a Senior Medical Expert, your task is to provide clear, concise guidance on lowering cholesterol… Begin by declaring your persona… Next, ask clarifying questions… |
| How do I grow tomatoes? | As an Agricultural Scientist, your task is to provide clear guidance on growing tomatoes… Begin by introducing yourself… |
| What are my rights if I am fired? | As a Senior Legal Advisor, your task is to provide guidance on… Begin by declaring your persona… |
Usage
from rewrite import EscardaRewrite
rw = EscardaRewrite(".") # loads model + tokenizer from this repo
print(rw.rewrite("How do I lower my cholesterol?"))
How it was built
Trained to match, and benchmarked against, QueryShield-1.5B
(ml-intern-explorers/queryshield-1.5b) - a 1.5B prompt optimizer - at
~1/17th the size. Balanced topic-roled corpus so the expert persona tracks
the query (Medical, Legal, Financial, Agricultural, Software, Data Science,
Marketing, Education, Research, …).
Citation
Created by Dean Byrne (Quazim0t0).
@misc{byrne2026escardarewrite,
title = {Escarda-Rewrite: an 86M prompt optimizer},
author = {Byrne, Dean},
year = {2026}
}