Our two goals with Rampart Edge were
1. Reduce false positives
2. Make it way smaller
We built this model to prevent PII from ever even leaving a user's device on https://t.co/ziIQbmUOlQ. Meaning it needs to be downloaded on every device before chat messages leave
To do this we began with a 50.74MB base model and hill-climbed with the following big wins:
- Reducing from 6 layers to 4 layesr
- Making the word table smaller
- 4-bit weights, shared between layers
The training data was entirely synthetic. We saw big wins by starting with government redacted documents and filling in synthetic PII.
My first task for the National Design Studio was to make Rampart smaller. It's our frontier on-device privacy model trained to filter all personal user data before it even gets to https://t.co/ziIQbmUgwi
After a lot of hill-climbing and data labeling, I'm excited to launch Rampart Edge along with @as400495. So small the weights fit in a notebook. This will noticeably speed up the load time of https://t.co/ziIQbmUgwi for hundreds of thousands of Americans.
Under budget and ahead of schedule!
Join us to help solve the toughest problems in American government.
Apply to join the American Forge residency.
https://t.co/kjX2mY9ajK
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Introducing American Forge: a six-month residency for exceptional engineers ready to take on the toughest problems in American government.
Run by National Design Studio in partnership with @cognition
https://t.co/177pZ9bj8W
Four months ago, we built Rampart the on-device privacy model.
Today we are releasing Rampart Edge. It's 10x smaller, more accurate, and runs on almost any device.
https://t.co/uJBjlJytbj
We were able to significantly reduce the download size of the model to just 1.45MB.
Rampart Edge is a four-layer model with one shared set of weights stored in 4-bit. Its word table is a low-rank factorization and under a fifth of the size of the original base model table.
It's open source and public domain: https://t.co/0suBdJ6gVt