license: apache-2.0
language:
- en
tags:
- prompt-optimization
- query-rewriting
- spikewhale
- escarda
pipeline_tag: text-generation
Escarda-Rewrite (86M)
Escarda-Rewrite is an 86M-parameter prompt optimizer. Given a raw user question it rewrites it into a detailed, role-grounded instruction prompt for a downstream LLM — a persona declaration, a clear task framing, and clarifying questions.
It is part of the Escarda line (JEPA SpikeWhale backbone, vocab 16,512) and is designed
to pair with Quazim0t0/Escarda-86M-Identity
as the downstream answerer: 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
Escarda-Rewrite was trained to match, and is benchmarked against, the prompt-optimization
behaviour of QueryShield-1.5B (ml-intern-explorers/queryshield-1.5b) — a 1.5B prompt
optimizer — at ~1/17th the size, using a balanced, topic-roled corpus so the chosen expert
persona tracks the topic of 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}
}