aayushbist/saccade-tiny

Saccade Tiny

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.