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msuiche/GLM-5.3-abliterated-cyber-GLP-77

msuiche Glm GGUF
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  • classification m8
  • files 3
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  • author_summary 26 models
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Abliteration classifier · v1.0.0
M8
Primary method

Repackaging (quantization)

Applied on top of direct removal inherited from the base model.
Confidence
MEDIUM
Inherited from base model
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=1
  • assume M1 (base ablation) + M8 (GGUF quant) - default when producer unknown
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

What is a refusal direction? →
Downloads · lifetime
128
76 last 30d - active
Likes
14
Model age
6w ago
created 2026-08-30
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Metadata

License
mit
Tags
gguf control-vector glp abliteration refusal-direction activation-steering vllm base_model:zai-org/GLM-5.3 base_model:quantized:zai-org/GLM-5.3 license:mit region:us

Related

Total size
1.81 MB
Files
3
Quantizations
1
Registered
2026-08-30 08:03
Last updated on HF
2026-10-04 23:33

Files by quantization

Auxiliary files 3 files 1.82 MB
GLM-5.3-abliterated-cyber-GLP-77-L1-77-a1.0.gguf 1.81 MB ******** download
README.md 4.57 KB b9e7edde download
.gitattributes 1.64 KB ad897701 download

README current version from Hugging Face


license: mit
base_model: zai-org/GLM-5.3
tags: [control-vector, glp, abliteration, refusal-direction, activation-steering, gguf, vllm]

GLM-5.3 — abliterated refusal direction (GLP-77)

A GLP control vector (GGUF Layer Projection, glp.mode=project) for
GLM-5.3 (753B MoE, arch
glm_moe_dsa: MLA + DeepSeek-sparse attention). 77 per-layer unit directions
over the post-layer residual stream (hidden 6144 — no hyperconnection widening
on this arch), layers 1-77, fp32. No model weights are modified; a conforming
reader applies h <- h - alpha * (h . d) d at the post-layer residual stream
(the deepseek_v2 decomposed convention: the stream is hidden_states + residual).

Derived 2026-08-30 on the NVFP4 deployment (RadixArk/GLM-5.3-NVFP4,
marlin MoE backend, vLLM 0.28.0, 8xH100) with the vLLM capture lane
(activation probe on deepseek_v2.py), difference-of-means over the
AdvBench-32 vs Alpaca-32 contrast, per-layer structure, last-token pooling.
Every shipped layer clears a held-out vs shuffled-label null gate (>=5x; peak
layers 48-55 sit at ~60x). Methodology + code:
refusal-research/experiments/20260829-glm53-flagship.

Measured effect (NVFP4, greedy, max 1400 new tokens, n=32 per suite)

GLM-5.3 thinks by default (template = max reasoning effort); completions were
read in full (content + reasoning). Two scorers are reported: the repo
phrase-classifier on the full completion, and an answer-audited count that
splits at </think> and applies extended GLM decline phrasing (the stock
scorer misses "I'm not able to provide").

alpha refusal32 delivered (repo scorer) refusal32 (answer-audited) cyber32 benign32 capability12
0.0 (stock) 1/32 1/32 18/32 31/32 12/12
1.0 (shipped default) 12/32 6/32 32/32 32/32 12/12
1.5 9/32 6/32 29/32 30/32 12/12

Null control (METHODOLOGY §11): 77 random unit vectors, same magnitude, seed
1337, at alpha=1.0 under the same protocol: refusal32 1/32 — identical to
stock
. The effect is the direction, not the dose.

Honest reading: the cyber cluster saturates (18/32 -> 32/32 at alpha=1.0) with
benign and capability untouched. The refusal32 physical-harm/petty-crime
cluster moves from 1/32 to 6-12/32 depending on scorer; the model's long
reasoning re-litigates borderline items at length (11/32 completions still hit
the 1400-token cap mid-thought at alpha=1.0 — those count as not-delivered
here). This is a real, null-gated effect, not a 32/32 claim. For reference,
the baked-weight CRACK release claims 0% refusal at high/max reasoning effort
on HarmBench; the steering counterpart does not reproduce that on refusal32,
and publishes its protocol so the difference is measurable rather than
rhetorical.

Usage

Confirmed bases

  • Derived on RadixArk/GLM-5.3-NVFP4 (vLLM 0.28.0, 8xH100), AdvBench-32 vs
    Alpaca-32 contrast.
  • Validated serving: that same NVFP4 checkpoint — the numbers above.
    On 4x DGX Spark use tonyd2wild's Int4-Int8Mix stack (~95.5 GiB/rank):
    the weightless recipe/glm53xl/ lane is wired and structure-tested
    (first boot is the anchor test).

Option 1 — weightless wizard (recommended)

git clone https://github.com/msuiche/weightless.git && cd weightless && python3 setup.py
# pick: "GLM-5.3 743B TP=4 serving" (4x DGX Spark lane)

Option 2 — manual (any vLLM container)

huggingface-cli download msuiche/GLM-5.3-abliterated-cyber-GLP-77 --include "*.gguf"
# inside the serving container, BEFORE vllm serve:
WEIGHTLESS_STEER_PATH=/cache/huggingface/GLM-5.3-abliterated-cyber-GLP-77-L1-77-a1.0.gguf \
WEIGHTLESS_STEER_ALPHA=1.0 \
  python3 /patches/hotfix-glm53xl-steering-projective.py && exec vllm serve ...

Fail-closed: anchor mismatch aborts the boot rather than serving unsteered.

Option 3 — other runtimes

Spec-conformant GGUF control vector (glp.mode=project, spec:
spec/GLP.md in the weightless repo). Stock llama.cpp's control-vector apply
is additive, not projective — it is NOT a conforming reader for this file.

alpha is a runtime parameter, never folded into the vector; alpha_default=1.0
is the measured peak on refusal32 (1.5 over-projects on this suite).

Note: the RadixArk checkpoint declares an fp8 KV-cache scheme that no sm90
sparse-MLA backend accepts; delete quantization_config.kv_cache_scheme from
config.json when serving on Hopper (bf16 KV).

Content SHA-256 (tensor bytes): 5837bcb5fc43a72416a71de0b6aca7c3d2ae7bbb9379eff9bf5bdfcc8179a225

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