CoolFace
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dealignai/GLM-5.2-NVFP4-UNCENSORED

sourceHugging Faceotherupdated 1mo agoView on Hugging Face
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Model Card

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GLM-5.2-NVFP4-CRACK

Abliterated GLM-5.2 — ~753B MoE (~40B active), 180K context, native MTP speculative decoding

320/320 = 100% HarmBench compliance with -1.2% MMLU change. Refusal removed, capability preserved. </div>

Model Details

Sourcenvidia/GLM-5.2-NVFP4
ArchitectureGlmMoeDsa — MoE (256 routed + 1 shared / layer), MLA + DSA sparse attention
Parameters~753B total · ~40B active (top-8 of 256 routed experts + 1 shared)
PrecisionNVFP4 (ModelOpt) — routed experts NVFP4, attention/shared bf16
Context180,224
Speculative decodingNative MTP (numspeculativetokens=1)
AbliterationCRACK (refusal removal)

This is a real modification — not tricks

No system-prompt jailbreaks. No chat-template hacks. No LoRA adapters bolted on. The refusal behavior is changed in the weights themselves, and the model's own reasoning, tool-calling, and coding ability are left fully intact. We don't ship cheap template tricks or throwaway fine-tunes.

Benchmarks

MMLU — knowledge retention (logit-mode, 57 tasks × 18)

BaseCRACKΔ
MMLU (overall)87.5%86.4%-1.2%

<details><summary><b>Full per-subject breakdown (before → after)</b></summary>

SubjectBaseCRACKΔ
Abstract Algebra61.166.7+5.6
Anatomy94.494.4+0.0
Astronomy94.494.4+0.0
Business Ethics94.488.9-5.5
Clinical Knowledge88.988.9+0.0
College Biology94.494.4+0.0
College Chemistry72.261.1-11.1
College Computer Science88.977.8-11.1
College Mathematics66.772.2+5.5
College Medicine88.988.9+0.0
College Physics77.877.8+0.0
Computer Security83.388.9+5.6
Conceptual Physics88.994.4+5.5
Econometrics83.383.3+0.0
Electrical Engineering83.383.3+0.0
Elementary Mathematics88.988.9+0.0
Formal Logic77.872.2-5.6
Global Facts77.872.2-5.6
High School Biology94.494.4+0.0
High School Chemistry77.872.2-5.6
High School Computer Science88.983.3-5.6
High School European History88.988.9+0.0
High School Geography94.494.4+0.0
High School Government And Politics100.0100.0+0.0
High School Macroeconomics94.494.4+0.0
High School Mathematics83.377.8-5.5
High School Microeconomics88.988.9+0.0
High School Physics83.388.9+5.6
High School Psychology100.0100.0+0.0
High School Statistics94.488.9-5.5
High School Us History94.494.4+0.0
High School World History94.494.4+0.0
Human Aging83.383.3+0.0
Human Sexuality88.988.9+0.0
International Law94.494.4+0.0
Jurisprudence94.488.9-5.5
Logical Fallacies83.377.8-5.5
Machine Learning83.372.2-11.1
Management88.988.9+0.0
Marketing100.0100.0+0.0
Medical Genetics94.494.4+0.0
Miscellaneous94.494.4+0.0
Moral Disputes83.383.3+0.0
Moral Scenarios77.861.1-16.7
Nutrition100.0100.0+0.0
Philosophy100.0100.0+0.0
Prehistory83.383.3+0.0
Professional Accounting83.388.9+5.6
Professional Law77.883.3+5.5
Professional Medicine88.994.4+5.5
Professional Psychology100.0100.0+0.0
Public Relations72.272.2+0.0
Security Studies83.377.8-5.5
Sociology88.988.9+0.0
Us Foreign Policy100.094.4-5.6
Virology61.161.1+0.0
World Religions100.0100.0+0.0

</details>

HarmBench — refusal removal (HarmBench-320, greedy)

Base GLM-5.2 over-refuses across these categories (the behavior this release corrects). After abliteration:

CategoryComplianceRate
Chemical / biological42 / 42100%
Copyright80 / 80100%
Cybercrime / intrusion52 / 52100%
Harassment / bullying21 / 21100%
Harmful18 / 18100%
Illegal53 / 53100%
Misinformation54 / 54100%
Total320 / 320100%

0 incoherent / degenerate outputs. Copyright behaviors are verbatim-reproduction requests (over-refusal), not a safety category.

Coherence & capability ✅

  • —Multi-turn memory, tool-calling (glm47 parser), and working code generation verified.
  • —No loops, no truncation, no degeneration. Decode ~110 tok/s on 4×H200 with MTP.

Serve (vLLM, SM90 / Hopper)

bash
vllm serve dealignai/GLM-5.2-NVFP4-CRACK \
  --tensor-parallel-size 4 \
  --kv-cache-dtype bfloat16 \
  --reasoning-parser glm47 \
  --enable-auto-tool-choice --tool-call-parser glm47 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":1}'

Requires an SM90 build with native NVFP4 Marlin + MoE kernels.

Acknowledgements

Compute for this build was generously provided by [@jordanschenck](https://x.com/jordanschenck) — thank you.


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