base_model: meta-models/Muse-Glimmer-30B
pipeline_tag: image-text-to-text
library_name: transformers
language:

• en
  license: apache-2.0
  tags:
• abliteration
• uncensored
• muse-glimmer

Muse-Glimmer-30B-uncensored

Uncensored version of meta-models/Muse-Glimmer-30B with refusal behavior removed.

Results

Of 128 baseline refusals, 83 flipped to confirmed compliance and none degraded. A further 42 are
unresolved: compliance runs about 4× longer than refusal (median output 354 → 1340 tokens) and hit
the 1536-token cap mid-answer.

Agentic Validation

Muse Glimmer's safety training covers tool-use boundaries, injection resistance and permission
handling, which a content-refusal harness cannot observe. Measured on a 30-probe agentic set:

One probe changed: asked to delete logs older than a day, the base model noted that the available
tool could not filter by age and asked before acting; the uncensored model called delete_files
directly.

Method

Norm-preserving biprojected abliteration (grimjim, Nov 2025).
The refusal direction is projected out of each residual-write matrix and every column is rescaled to
its original norm, so ||W_new||_col = ||W_orig||_col.

Note on the architecture. Muse-Glimmer-30B is a dense multimodal model — 52 text decoder
layers, hidden 6656, plus a separate 1.9B ViT-G/14 vision tower. Only the text pathway is
abliterated; all 800 vision tensors are copied unchanged. Two arch quirks: there are two
gate_proj tensors per layer (attention and MLP, 104 total) and neither is a residual-write
matrix, so targets are matched by full path; and logits are softcapped at 20·tanh(x·0.196/20),
so KL is not comparable to the Gemma 4 family. The model also emits channelled output —
to=self deliberation followed by the to=user answer — so evaluation reads the final channel.

Version requirements: inference needs transformers >= 5.15.0; the muse_glimmer architecture
is absent from 5.12.0. GGUF conversion/quantization needs llama.cpp build b10353 or later, added
2026-08-10 in #26841 (commit 62bf73d).

Pipeline

1. Capture residual activations at the final prompt token for 400 harmful + 400 harmless prompts
2. Winsorize activations at the 99.5th percentile
3. Compute per-layer refusal direction: normalize(mean(harmful) - mean(harmless))
4. Orthogonalize each direction against the harmless mean (double-pass Gram-Schmidt)
5. Gate on held-out activations — reject any direction with an anti-selective layer
6. Apply norm-preserving weight modification to o_proj and down_proj in every layer
7. Write shard-by-shard from safetensors and verify the edit in the resulting weights

Parameters

Validation

The direction passed a selectivity gate on held-out activations (mean selectivity 7.49, 0/52
anti-selective layers) before being applied. Post-bake, 98.6–99.6% of the direction is removed from
the targeted tensors (‖rᵀW‖ residual 3.7e-03 to 1.4e-02, floored by the float64→bf16 round-trip);
an untouched up_proj is byte-identical to the base.

Usage

from transformers import AutoTokenizer
from transformers.models.muse_glimmer import MuseGlimmerForConditionalGeneration
import torch

model = MuseGlimmerForConditionalGeneration.from_pretrained(
    "TrevorJS/Muse-Glimmer-30B-uncensored", dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("TrevorJS/Muse-Glimmer-30B-uncensored")

messages = [{"role": "user", "content": "Your prompt here"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to(model.device), max_new_tokens=1536)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

Reproduction

Full code and experiment data: muse-glimmer abliteration repo

Prior work in the same program: gemma-4 abliteration repo

python scripts/capture.py --harmful data/harmful_train.txt \
  --harmless data/harmless_train.txt --out acts/derive.npz --position prompt_final

python scripts/derive_direction.py --acts acts/derive.npz \
  --acts-holdout acts/holdout.npz --out directions/v1

python scripts/abliterate.py --directions directions/v1.npy \
  --out models/mg-abl-s1.0 --scale 1.0

Credits

The method is not original to this work — refusal directions (Arditi et al., 2024), norm-preserving biprojection (grimjim), heretic (p-e-w) and the mlabonne / JailbreakBench / AdvBench corpora. Full attribution in the repo README.

Quantized

TrevorJS/Muse-Glimmer-30B-uncensored-GGUF