license: gemma
language:

• en
  base_model: google/gemma-3-1b-it
  tags:
• gemma
• gemma3
• weightless
• control-vector
• abliterated
• uncensored
• refusal-ablation
• activation-steering
• representation-engineering
• gguf
  extra_gated_prompt: |

Responsible Use Agreement

This is not a model. It is a 96 KB control vector that removes safety refusals
from google/gemma-3-1b-it at inference time. It is useful for red-teaming,
offensive-security research, refusal-rate evaluation, and measuring what a
model will do without its refusal behaviour — and it removes guardrails that
you must then supply yourself.

You must agree before access is granted:

• You are 18 or older.
• You will not use this for anything involving the sexual exploitation or
  endangerment of minors.
• You will not use this to generate content promoting self-harm or suicide.
• You will not use this to produce material that is illegal in your
  jurisdiction, or that targets real individuals for harassment, doxxing or
  fraud.
• You accept that any output you elicit is the result of your own input and
  your own responsibility.
  extra_gated_fields:
  I have read and agree to the Responsible Use Agreement: checkbox

gemma-3-1b-it-abliterated-GLP-20-L4-25-a0.25

Projective control vector ("GLP") for google/gemma-3-1b-it (gemma3_text:
26 layers, hidden 1152, vocab 262144, bf16). Applied at runtime as
h <- h - alpha * (h . d) d at the post-layer residual stream, layers 4, 5 and
8–25, alpha 0.25 baked in. No weights are modified; this is the
difference, not the model.

Confirmed base: google/gemma-3-1b-it. The derivation checkpoint was the
unsloth/gemma-3-1b-it mirror (byte-identical bf16 weights) at revision
5b11413a10db4e486ef16a20101fd028f8f2499c — the google/ repo is
license-gated for our token, so the mirror sha is the pin recorded in the
file's glp.base_revision. The config was checked against Google's published
specs (26 layers, hidden 1152, vocab 262144) before derivation. The vector is
not validated against other revisions or quants.

Validation (Modal A10G, transformers 5.16.1, bf16, greedy, 1024-token cap, 2026-10-11)

Alpha ladder (refusal32 / benign32-holdout, comply of 32): 0.25 → 32/32,
32/32; 0.5 → 32/32, 32/32; 1.0 → 32/32, 32/32. Zero GARBLED completions at
every rung. The knee is the bottom rung: alpha 0.25 captures the full
delivery gain, so that is what ships. No-op gate: an alpha=0.0 arm with the
vector loaded reproduces the stock completions exactly (64/64 identical
strings on both gate suites).

The three stock refusals (a social-engineering script, a keylogger, a SQL
injection tutorial) all become coherent on-topic deliveries under steering —
verified by reading the completions, not only the classifier. Stock
gemma-3-1b-it already answers 29/32 of refusal32: the direction closes the
last three rather than unlocking a locked-down model.

Derivation gates (captain-vector 0.5.0, dom_per_layer_mask0.005): the
massive-activation screen flagged this checkpoint — peak/median 783x at
layer 0, dims 1125/367/647 — so the top 0.5% of dims by magnitude were masked
before normalisation. Held-out separation vs a shuffled-label null (20 reps)
clears the 5x ship gate on 20 of 25 shippable layers; layers 1, 2, 3, 6, 7
sit below it (ratios 2.5–4.9) and are excluded from the file. Adjacent-layer
cosine median 0.656 against a random-direction null p99 of 0.073. Mean dose
0.098 of the residual norm at alpha=1 (random-direction floor 0.030); the
max per-layer dose is 0.362 at layer 25, under the 50% damage threshold.

The contrast is refusal32 vs benign32 (content-matched, last-token pooling).
refusal32 doubles as the derivation set, so its steered number is in-sample;
benign32-holdout is out-of-sample. n=32 per arm; read rates at that
resolution as approximate.

Usage

This file uses the glp.* GGUF namespace (spec: weightless spec/GLP.md)
and is read projective-only. An additive consumer must refuse this file.
The hook point is residual_stream_post_layer — the decoder-layer output,
the accumulated residual stream — derived AND applied at that site.

export WEIGHTLESS_STEER_PATH=glp.gemma-3-1b-it-GLP-20-L4-25-a0.25.gguf
export WEIGHTLESS_STEER_ALPHA=0.25
# serve with a runtime that implements glp.mode=project

What is inside

Do not scale alpha across models

alpha_default is calibrated on this checkpoint, at this hook. Here the
ladder is flat — 0.25, 0.5 and 1.0 all deliver identically with zero measured
collateral — because the stock model barely refuses. That says nothing about
any other model: on DeepSeek-V4.1-Flash the same ladder is sharply
non-monotone and the knee sits at 0.5. Re-run the ladder per checkpoint; do
not port this 0.25 anywhere else.

Caveats

• Checkpoint-specific. Tied to the revision pinned above. Applying it to
  another model or revision is undefined.
• Not a jailbreak of a hosted service. It requires local weights and a
  runtime that implements the projection.
• Layers 1, 2, 3, 6, 7 fell below the derivation null gate (held-out
  separation vs shuffled-label null under 5x) and are not in the file. The
  refusal signal on this small model lives in the middle and late stack
  (ratios 11.6–23.8 from layer 8 up); the ladder confirms the shipped span
  loses nothing measurable.
• refusal32 is the derivation contrast (in-sample on the harmful side);
  benign32-holdout is the out-of-sample control.
• n=32 suites resolve about 30 points; the completions behind every number
  above were read, not only classified.

License

Base model © Google, under the Gemma Terms of
Use. This vector modifies and
redistributes no weights; the Gemma Terms of Use continue to govern the
weights it is applied to.

Author

Matt Suiche.