license: mit
base_model:
- zai-org/GLM-5.3-Flash
language: - en
tags: - abliterated
- uncensored
- crack
- glm
- moe
- fp8
- vision
- mtp
thumbnail: dealign_mascot.png
⚠️ Updated 2026-08-28 — files fixed · please re-download
This release supersedes the previous weights. It fixes a rare repetition-loop issue and the
model is more capable than before: MMLU 87.33% — above base 86.74% — zero degeneration.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 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. Use reasoning-off or default/max effort for the fully-uncensored
experience. Please re-download for the corrected files.
GLM 5.3 CRACK Uncensored FP8
CRACK · guardrails removed at the weight level · native FP8 speed · vision + MTP working
a CRACK release by dealignai · Twitter @dealignai
Also mirrored at dealignai/GLM-5.3-Flash-ABLITERATED-FP8.
What Is This?
CRACK is dealignai's brand for permanent, weight-level uncensoring. This is
GLM-5.3-Flash in FP8 with its refusal behavior —
which caused heavy over-refusal, especially on copyright and other benign-but-flagged requests —
removed directly in the model weights. FP8 runs at native speed on Hopper (H100/H200) GPUs.
Genuine weight modification — none of the usual shortcuts:
- ❌ No fine-tuning / SFT / DPO. ❌ No cheap template / jailbreak-prompt tricks.
- ❌ No LoRA, adapters, steering vectors, runtime hooks, or custom
model.py. - ✅ A permanent edit baked into the tensors. Load 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 | FP8 (block-wise e4m3) — native tensor-core speed on Hopper |
| Context | 1M tokens |
| Vision | GLM-4.1V vision tower — working (ships the correct multimodal chat template) |
| MTP | multi-token-prediction draft head — also CRACK'd, 75.9% acceptance |
Speed (TP4, native FP8 on H200)
| Decode | 163 tok/s single-stream (211 tok/s with MTP speculative decoding) |
| Prefill | ~19,400 tok/s |
| MTP acceptance | 75.9% — and it does not collapse on the un-refused prompts (benign / harmful / copyright all ~208–219 tok/s) |
Capability Is Preserved — and Improved (MMLU-logit)
Identical logit-mode scoring on base vs. this model, 1,026 questions:
| Base FP8 | CRACK Uncensored FP8 | Δ | |
|---|---|---|---|
| MMLU (overall) | 86.74% | 87.33% | +0.59 pp (above base) |
Guardrails Are Gone
HarmBench-320 (greedy):
| Category | Complied | Rate |
|---|---|---|
| Standard | 159/159 | 100.0% |
| Contextual | 81/81 | 100.0% |
| Copyright | 80/80 | 100.0% |
| Overall | 320/320 | 100.0% |
Robust under the recommended sampling params too (temperature 1.0, top_p 0.95): the 6 harshest
behaviors sampled 5× each → 30/30 complied, 0 refusals, 0 soft refusals, 0 garbage. The crack is
not a greedy-decoding artifact.
Scores are at reasoning-off / default (max) effort — the recommended modes — where the crack is
fully uncensored. Low effort is intentionally left more conservative to preserve quality (see the
reasoning-mode note at the top).
A Note on KL Divergence
For a refusal-ablation, KL divergence vs. the base model is not a meaningful quality metric.
The entire point is to change one behavior — refusal — end-to-end, so a distributional shift on
refusal-adjacent tokens is the intended result, not damage. Capability preservation (MMLU, above)
is what matters, and it is improved, now above the base model (+0.59 pp).
MMLU by Topic (base → CRACK)
All 57 MMLU subjects
| Subject | Base | CRACK |
|---|---|---|
| Abstract Algebra | 66.7% | 66.7% |
| Anatomy | 83.3% | 88.9% |
| Astronomy | 94.4% | 94.4% |
| Business Ethics | 94.4% | 94.4% |
| Clinical Knowledge | 100.0% | 100.0% |
| College Biology | 100.0% | 94.4% |
| College Chemistry | 61.1% | 66.7% |
| College Computer Science | 83.3% | 88.9% |
| College Mathematics | 66.7% | 66.7% |
| College Medicine | 94.4% | 94.4% |
| College Physics | 77.8% | 94.4% |
| Computer Security | 83.3% | 83.3% |
| Conceptual Physics | 94.4% | 94.4% |
| Econometrics | 77.8% | 77.8% |
| Electrical Engineering | 77.8% | 77.8% |
| Elementary Mathematics | 94.4% | 100.0% |
| Formal Logic | 66.7% | 66.7% |
| Global Facts | 66.7% | 66.7% |
| 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 | 77.8% | 77.8% |
| High School Geography | 88.9% | 88.9% |
| High School Government And Politics | 100.0% | 100.0% |
| High School Macroeconomics | 88.9% | 83.3% |
| High School Mathematics | 61.1% | 66.7% |
| High School Microeconomics | 83.3% | 88.9% |
| 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 | 88.9% | 88.9% |
| High School World History | 94.4% | 94.4% |
| Human Aging | 72.2% | 77.8% |
| Human Sexuality | 88.9% | 88.9% |
| International Law | 88.9% | 88.9% |
| Jurisprudence | 88.9% | 88.9% |
| Logical Fallacies | 88.9% | 88.9% |
| Machine Learning | 88.9% | 83.3% |
| Management | 100.0% | 100.0% |
| Marketing | 94.4% | 88.9% |
| Medical Genetics | 94.4% | 94.4% |
| Miscellaneous | 88.9% | 88.9% |
| Moral Disputes | 88.9% | 88.9% |
| Moral Scenarios | 83.3% | 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 | 83.3% | 88.9% |
| Professional Medicine | 94.4% | 94.4% |
| Professional Psychology | 100.0% | 100.0% |
| Public Relations | 72.2% | 72.2% |
| Security Studies | 83.3% | 83.3% |
| Sociology | 100.0% | 100.0% |
| Us Foreign Policy | 88.9% | 94.4% |
| Virology | 55.6% | 55.6% |
| World Religions | 88.9% | 94.4% |
Usage
vllm serve dealignai/GLM-5.3-Flash-UNCENSORED-FP8 \
--tensor-parallel-size 4 \
--tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice \
--speculative-config '{"method":"mtp","num_speculative_tokens":1}'
Native FP8 on Hopper (no Marlin needed). OpenAI-compatible chat/completions, tools, reasoning,
vision (image_url), and MTP speculative decoding all work. (DeepGEMM JITs a block-FP8
kernel at startup — make sure nvcc is on PATH.)
Credits
- dealignai — CRACK abliteration research & release · Twitter @dealignai
- @jordanschenck — compute
Disclaimer
Safety guardrails have been removed; this model will comply with requests a stock model refuses.
Released for alignment and safety research. You are responsible for how you use it.