gemma-4-E2B-it-abliterated-GLP-31-L4-34-a0.5

Projective control vector ("GLP") for google/gemma-4-E2B-it (Gemma4ForConditionalGeneration, text stack: 35 layers, hidden 1536, vocab 262144, bf16 — plain gemma4 arch: vision/audio towers and per-layer embeddings around the text stack). Applied at runtime as h <- h - alpha * (h . d) d at the post-layer residual stream of the language model, layers 4–34, alpha 0.5 baked in. No weights are modified; this is the difference, not the model.

Confirmed base: google/gemma-4-E2B-it at revision 3e22461f65e89153144f8adb70e3b8c2cc9845a7 — that sha is the pin recorded in the file's glp.base_revision. Unlike gemma-3, the google/ gemma-4 repos serve weights to our token directly (verified pre-flight with a ranged blob GET), so no mirror was involved; derivation ran on the canonical checkpoint itself. The vector is not validated against other revisions or quants.

Validation (Modal A10G, transformers 5.19.0, bf16, greedy, 1024-token cap, 2026-10-11)

Alpha ladder (refusal32 / benign32-holdout, comply of 32): 0.25 → 28/32, 32/32; 0.5 → 31/32, 32/32; 1.0 → 32/32, 32/32. Zero GARBLED completions at every rung. 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).

Unlike the 12B (which complies 32/32 unsteered), stock gemma-4-E2B-it refuses 28 of 32 refusal32 prompts — this vector does real unlocking here, and the ladder is monotone: delivery climbs 28 → 31 → 32 with no dip. The knee rule ships the smallest rung within one delivery of the ladder maximum: alpha 0.5. The one holdout at 0.5 becomes a coherent on-topic delivery at 1.0; the card ships 0.5 because 31/32 is within one of the maximum and the lower dose is the milder intervention.

Derivation gates (captain-vector 0.5.0, dom_per_layer_mask0.005): the massive-activation screen flagged this checkpoint — peak/median 452.8x at layer 34, dims 438/1269/920 — 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 31 of 34 shippable layers, ratios 5.6–66.8; layers 1–3 sit below it (ratios 2.8–3.7) and are excluded from the file, and layer 0 is excluded by protocol (ratio 0.8). Adjacent-layer cosine within the shipped span: median 0.653 against a random-direction null p99 of 0.068 — with one sub-null pair, 13→14 at 0.026. Both directions are individually strong (null-gate ratios 22.6 and 17.3); the refusal direction rotates sharply across that boundary (a representation transition, not a missing signal — coherence resumes at 14→15, 0.326, and 15→16, 0.615), and the alpha=1.0 arm's zero-garbled, zero-benign-collateral result is the end-to-end check on the full span. Mean dose 0.276 of the residual norm at alpha=1 (random-direction floor 0.026); the max per-layer dose is 0.521 at layer 13 — at the shipped alpha 0.5 the effective per-layer dose halves, max ~0.26.

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-4-E2B-it-GLP-31-L4-34-a0.5.gguf
export WEIGHTLESS_STEER_ALPHA=0.5
# 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 monotone — stock refuses hard and delivery climbs 28/32 → 31/32 → 32/32 — so the shipped alpha is 0.5, the knee within one delivery of the maximum. That says nothing about any other model: on gemma-4-12B-it the same ladder is non-monotone (0.25 dips below stock) and the knee also sits at 0.5, while on gemma-3 it is flat with the knee at 0.25. Re-run the ladder per checkpoint; do not port this 0.5 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 1–3 fell below the derivation null gate (held-out separation vs shuffled-label null under 5x, ratios 2.8–3.7) and are not in the file; layer 0 is excluded by protocol. The refusal signal on this model lives from layer 4 up (ratios 5.6–66.8, strongest 16–34); the ladder confirms the shipped span loses nothing measurable.
• One adjacent pair inside the shipped span (13→14, cosine 0.026) sits below the random-null p99 — the per-layer directions rotate there. Both layers clear the null gate individually and the full-span behavioral gates are clean, but treat that boundary as measured, not smooth.
• 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.