Modelpublic

dealignai/GLM-5.3-Flash-UNCENSORED-NVFP4

sourceHugging Facemitupdated 7d agoView on Hugging Face

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Model Card

[!IMPORTANT] Serving note — read before your first request (vLLM / SGLang with a reasoning parser). - `reasoning_effort` only accepts `"low"` and `"high"`. Anything else — medium, xhigh, off, or omitting it — silently resolves to `max`, the deepest mode. Template line: reasoning_effort ... in ['low','high'] else 'max'. - At `max`, a small `max_tokens` returns an EMPTY reply. The model spends the whole budget inside <think> and you get finish_reason="length" with content="". In multi-turn the empty turn poisons the history and the conversation does not recover. Measured: max + max_tokens 2000 dies from turn ~4; max + 6000 is clean; low and high are clean at 2000. It is budget exhaustion, not a loop — sampling changes do not rescue it. - Do NOT pass `enable_thinking`. This template has no such variable (only clear_thinking and reasoning_effort). Passing it — or a top-level reasoning_effort: "none" — turns the parser off while the model still thinks, dumping raw reasoning into content. - Reasoning is returned in `message.reasoning`, not message.reasoning_content (vLLM renamed the output field; the input side still accepts both). - clear_thinking must be nested inside chat_template_kwargs — a top-level key is ignored. ``json { "model": "<this-model>", "messages": [{"role": "user", "content": "..."}], "max_tokens": 2000, "temperature": 1.0, "top_p": 0.95, "chat_template_kwargs": {"reasoning_effort": "low", "clear_thinking": true} } ` Use "high" for hard tasks; if you want max, give it maxtokens >= 6000`.  **Video and image parameters — measured, because several are accepted then silently ignored.** - 🔴 **`mediaiokwargs.video.fps` must stay BELOW the clip's own frame rate.** Requesting a value at or above it makes the placeholder builder count 3x the real tokens and **kills the engine**: `ValueError: Attempted to assign N = N multimodal tokens to 3N placeholders` -> `EngineDeadError`, server down until restarted. Upstream: vLLM #55644 / #55647. `fps: 2` is the safe, accurate choice. - **`maxframes and numframes` are not interchangeable across builds.** On vLLM nightly `maxframes is honoured and numframes` ignored; on the older `0.1.dev*` line it is the exact reverse. A client that sets only one silently gets full-rate sampling on the other build. Set `fps`, which every build honours. - **Pixel controls: use `mmprocessorkwargs.maximagetokens` / `minimagetokens`.** `maxpixels, minpixels`, `size` and `detail` return HTTP 200 and change nothing. `mmprocessorkwargs.fps` is an HTTP 400. Image tokens are `text + 2 + ceil(H/28)*ceil(W/28)`, floored at 16 and capped at 8000. - Default video sampling differs by build (roughly 2 fps vs 6 fps), so the same clip can cost very different prompt tokens. Pass `fps` explicitly if token cost matters. ```json "mediaiokwargs": {"video": {"fps": 2}}, "mmprocessorkwargs": {"maximage_tokens": 1024} ```

## ⚠️ 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](https://huggingface.co/dealignai/GLM-5.3-Flash-ABLITERATED-NVFP4).

</div>

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)

<details> <summary>All 57 MMLU subjects</summary>

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%

</details>

Usage

bash

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

—[dealignai](https://huggingface.co/dealignai) — CRACK abliteration research & release · Twitter [@dealignai](https://twitter.com/dealignai)
—[@jordanschenck](https://twitter.com/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.