dealignai/GLM-5.3-Flash-ABLITERATED-NVFP4

⚠️ Updated 2026-08-29 — files fixed · please re-download

This release supersedes the previous weights. It fixes a rare repetition-loop issue and lands MMLU 85.28% (within ~0.9 pt of the base model — quality intact).

Reasoning-mode note (please read): GLM-5.3-Flash has multiple reasoning-effort modes. This CRACK is tuned to be fully uncensored at reasoning-OFF and at MAX effort — the recommended and default modes — with 0% refusals on HarmBench-320. We deliberately kept the weight edit conservative to preserve model quality instead of aggressively ablating every effort level; low effort retains some refusals by design (aggressively removing them costs quality). Use reasoning-off or default/max effort for the fully-uncensored experience. Please re-download for the corrected files.

GLM 5.3 CRACK Abliterated NVFP4

CRACK · guardrails removed at the weight level · vision tower + MTP head retained

a CRACK release by dealignai · Twitter @dealignai

Also mirrored at dealignai/GLM-5.3-Flash-UNCENSORED-NVFP4.

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What Is This?

CRACK is dealignai's brand for permanent, weight-level uncensoring. This is GLM-5.3-Flash with its refusal behavior — which caused heavy over-refusal, especially on copyright and other benign-but-flagged requests — removed directly in the model weights.

This is genuine weight modification — none of the usual shortcuts:

• ❌ No fine-tuning / SFT / DPO — the model was not retrained on anything.
• ❌ No cheap template tricks — no jailbreak system prompt, no chat-template edits, no "sure, here is" prefixing.
• ❌ No LoRA, no adapters, no steering vectors, no runtime hooks, no custom model.py.
• ✅ A permanent edit baked into the tensors. Load it with stock vLLM and it just works.

Specs

Architecture	GLM-5.3-Flash (glm5_next) — hybrid MoE (KDA linear + DeepSeek-sparse attention)
Parameters	320B total · 18B active per token
Quantization	NVFP4 (routed experts NVFP4; attention + shared experts + embeddings bf16)
Context	1M tokens
Vision	GLM-4.1V vision tower — retained, byte-for-byte identical to base
MTP	multi-token-prediction draft head — also CRACK'd (81.7% acceptance)
Reasoning	reasoning-off / low / high / max effort — see the compliance table below

MTP Is Also CRACK'd

The MTP (multi-token prediction) speculative-decoding draft head is CRACK'd too — not just the main model. The draft head will not propose refusals, so speculative decoding stays compliant and fast on the exact prompts a stock model would refuse.

Reasoning Modes — Compliance

GLM-5.3-Flash supports several reasoning-effort modes. Guardrail removal is strongest in the modes people actually use by default. HarmBench-320, greedy (temperature 0), measured per mode:

Reasoning mode	Refusals	Notes
Reasoning-off	0%	fully uncensored
Max effort (default)	0%	fully uncensored
High effort	~4%	complies on all but the most extreme safety cases
Low effort	~9%	intentionally left conservative to preserve quality

0 degenerate / looping outputs in every mode. The design choice: keep the ablation light enough that capability (MMLU) stays essentially at base, rather than over-ablating to force the rarely-used low-effort mode. For a fully uncensored model, use reasoning-off or the default/max effort mode.

These rates are greedy decoding (temperature 0) — the strict worst case. Under the model's recommended sampling (temperature 1.0, top_p 0.95) the model is at least as compliant.

Capability Is Preserved — MMLU-logit

Identical logit-mode scoring (argmax over A/B/C/D) on base vs. this model, 1,026 questions:

	Base	CRACK Abliterated	Δ
MMLU (overall)	86.16%	85.28%	-0.88 pp

A sub-1-point delta — reasoning and knowledge are intact.

MMLU by Topic (base → CRACK)

All 57 MMLU subjects

Subject	Base	CRACK
Abstract Algebra	55.6%	61.1%
Anatomy	88.9%	94.4%
Astronomy	94.4%	94.4%
Business Ethics	94.4%	94.4%
Clinical Knowledge	88.9%	88.9%
College Biology	94.4%	94.4%
College Chemistry	44.4%	50.0%
College Computer Science	88.9%	88.9%
College Mathematics	72.2%	66.7%
College Medicine	88.9%	88.9%
College Physics	83.3%	83.3%
Computer Security	83.3%	83.3%
Conceptual Physics	94.4%	94.4%
Econometrics	83.3%	83.3%
Electrical Engineering	83.3%	77.8%
Elementary Mathematics	100.0%	94.4%
Formal Logic	66.7%	61.1%
Global Facts	61.1%	61.1%
High School Biology	94.4%	94.4%
High School Chemistry	88.9%	88.9%
High School Computer Science	100.0%	100.0%
High School European History	72.2%	72.2%
High School Geography	88.9%	83.3%
High School Government And Politics	94.4%	94.4%
High School Macroeconomics	94.4%	94.4%
High School Mathematics	55.6%	44.4%
High School Microeconomics	83.3%	83.3%
High School Physics	88.9%	88.9%
High School Psychology	100.0%	100.0%
High School Statistics	94.4%	94.4%
High School Us History	94.4%	88.9%
High School World History	100.0%	100.0%
Human Aging	72.2%	72.2%
Human Sexuality	88.9%	94.4%
International Law	94.4%	94.4%
Jurisprudence	88.9%	88.9%
Logical Fallacies	83.3%	83.3%
Machine Learning	83.3%	83.3%
Management	100.0%	100.0%
Marketing	94.4%	94.4%
Medical Genetics	100.0%	94.4%
Miscellaneous	88.9%	88.9%
Moral Disputes	83.3%	88.9%
Moral Scenarios	77.8%	66.7%
Nutrition	100.0%	100.0%
Philosophy	94.4%	94.4%
Prehistory	94.4%	94.4%
Professional Accounting	88.9%	88.9%
Professional Law	77.8%	77.8%
Professional Medicine	94.4%	94.4%
Professional Psychology	100.0%	100.0%
Public Relations	61.1%	61.1%
Security Studies	83.3%	83.3%
Sociology	100.0%	94.4%
Us Foreign Policy	88.9%	88.9%
Virology	61.1%	55.6%
World Religions	94.4%	88.9%

Usage

vllm serve dealignai/GLM-5.3-Flash-ABLITERATED-NVFP4 \
  --tensor-parallel-size 4 --moe-backend marlin \
  --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice \
  --speculative-config '{"method":"mtp","num_speculative_tokens":1}'

OpenAI-compatible chat/completions, tools, reasoning, vision (image_url), and MTP speculative decoding all work. NVFP4 routed experts serve via the Marlin FP4 path on Hopper (H100/H200).

Credits

• dealignai — CRACK abliteration research & release · Twitter @dealignai
• @jordanschenck — compute

Disclaimer

This model has had its safety guardrails removed and will comply with requests a stock model refuses. Released for alignment and safety research. You are responsible for how you use it.