sae-gemma-3-4b-it-classifier – AI Model by razlapid | AlphaNeural AI

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razlapid/
sae-gemma-3-4b-it-classifier

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jailbreak-detection

sparse-autoencoder

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razlapid/sae-gemma-3-4b-it-classifier

Multi-head linear safety classifier trained on SAE features from google/gemma-3-4b-it.

Model Details

Backbone: google/gemma-3-4b-it
Layer: 17 (resid_post)
d_model: 2560
Classification heads: harmfulness, prompt_injection
Architecture: LayerNorm → Dropout → N x Linear(d_sae, 1)
Training samples: 35

Results

Pooling	Val Mean AUC	Test Mean AUC
mean	0.4722	0.6250
last_token	0.7778	0.6875
max	0.7333	0.9444
topk_max	0.4778	0.6319

Usage

python

from model import MultiHeadProbe

probe = MultiHeadProbe.from_checkpoint("probe_mean/probe_best.pt", device="cuda")
# Feed SAE-encoded, pooled features [batch, d_sae]
probs = probe.predict_proba(features)

Pipeline

Trained with SAE Guard Pipeline.