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dealignai/GLM-5.3-Flash-UNCENSORED-NVFP4

🤗 On Hugging Facemit165B params175 GBsafetensors✓ Checksums witnessedupdated today
Magnet
## ⚠️ 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 Uncensored 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-ABLITERATED-NVFP4.

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 Uncensored | 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-UNCENSORED-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

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.