license: mit base_model:
- zai-org/GLM-5.3-Flash language:
- en tags:
- abliterated
- uncensored
- crack
- glm
- moe
- nvfp4
- vision
- mtp thumbnail: dealign_mascot.png
[!IMPORTANT] Serving note — read before your first request (vLLM / SGLang with a reasoning parser).
reasoning_effortonly accepts"low"and"high". Anything else —medium,xhigh,off, or omitting it — silently resolves tomax, the deepest mode. Template line:reasoning_effort ... in ['low','high'] else 'max'.- At
max, a smallmax_tokensreturns an EMPTY reply. The model spends the whole budget inside<think>and you getfinish_reason="length"withcontent="". In multi-turn the empty turn poisons the history and the conversation does not recover. Measured:max+max_tokens 2000dies from turn ~4;max+6000is clean;lowandhighare 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 (onlyclear_thinkingandreasoning_effort). Passing it — or a top-levelreasoning_effort: "none"— turns the parser off while the model still thinks, dumping raw reasoning intocontent.- Reasoning is returned in
message.reasoning, notmessage.reasoning_content(vLLM renamed the output field; the input side still accepts both).clear_thinkingmust be nested insidechat_template_kwargs— a top-level key is ignored.{ "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 wantmax, give itmax_tokens >= 6000.Video and image parameters — measured, because several are accepted then silently ignored.
- 🔴
media_io_kwargs.video.fpsmust 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: 2is the safe, accurate choice.max_framesandnum_framesare not interchangeable across builds. On vLLM nightlymax_framesis honoured andnum_framesignored; on the older0.1.dev*line it is the exact reverse. A client that sets only one silently gets full-rate sampling on the other build. Setfps, which every build honours.- Pixel controls: use
mm_processor_kwargs.max_image_tokens/min_image_tokens.max_pixels,min_pixels,sizeanddetailreturn HTTP 200 and change nothing.mm_processor_kwargs.fpsis an HTTP 400. Image tokens aretext + 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
fpsexplicitly if token cost matters."media_io_kwargs": {"video": {"fps": 2}}, "mm_processor_kwargs": {"max_image_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.
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)
| 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
- 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.