Saccade Tiny is a small experimental token classifier that predicts whether
each word in a prompt should be KEEP or DROP before LLM inference.
Architecture
Base model: google/bert_uncased_L-2_H-128_A-2
Task: binary token classification
Labels: KEEP, DROP
Training data
This first version was trained primarily on synthetic examples. Clean
instructions were modified with injected filler and redundant wording.
Original words were labelled KEEP; injected words were labelled DROP.
Held-out evaluation
Accuracy: 1.0000
KEEP precision: 1.0000
KEEP recall: 1.0000
KEEP F1: 1.0000
Intended use
Research and demonstrations involving conservative filler removal from English
prompts.
Limitations
The model was trained mainly on synthetic data and may remove meaningful
context. It has not yet demonstrated a general improvement in downstream LLM
accuracy. Do not use it for safety-critical, legal, medical, or financial text.
