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Cataloged from dealignai/GLM-5.3-CYBERSECURITY-FP8
All models by dealignai →Cybersecurity-focused CRACK · native FP8 speed on Hopper
[Image blocked: dealignai logo]a CRACK release by dealignai · Twitter @dealignai
[!IMPORTANT] Runtime notes — field-tested on 8× DGX Spark GB10 by @0xMagnus (discussion):
reasoning_effortonly honors"low"and"high". Every other value —off,medium,max, unset, or an unquoted YAMLoff:(parses as booleanfalse) — falls through tomax. There is no way to disable reasoning on this checkpoint; pass"low"for minimum.- On FP8, prefer
lowfor agent / tool-loop use. Athigh/maxthe model can spend the wholemax_tokensbudget inside<think>and return zero answer tokens (finish=length); sampling params (temp 0 + rep 1.05, temp 0.7 / top-p 0.95) do not rescue it. It is budget exhaustion, not a loop. If you must runhigh/max, givemax_tokens ≥ 8000.- Reasoning text is in
message.reasoning, notmessage.reasoning_content.- MTP: non-functional on stock vLLM, but reported working on ciprianveg's B12X sparse-MLA vLLM fork with
--draft-attention-backend B12X_MLA_SPARSE(+48% decode on coding prompts).- 1M context via decode-context-parallel is closed on
glm_moe_dsain vLLM today (DSA indexerk_cacheis replicated across DCP ranks while MLA KV is sharded →page size is not divisible by target page size and cannot be paddedforfp8_ds_mla). Practical TP8 H200 ceiling: ~131K w/MTP, ~160K w/o. Pipeline-parallel (PP2 × TP4) profiles fine, but the MTP draft does not implementSupportsPP.
This is a CYBERSECURITY-DOMAIN CRACK of GLM-5.3-FP8 — not a general-purpose uncensor.
Refusal is reduced specifically for offensive-security, red-team, exploit-dev, reverse-engineering, evasion, phishing, credential-attack, malware-analysis, and adjacent technical content. On non-cyber categories (weapons, chemistry, biology, harassment, misinformation) it often complies with a soft "educational" wrapper because refusals share substrate across domains, but this model is tuned for cybersecurity, not universal compliance. Notably, copyright-verbatim reproduction still soft-refuses in this variant.
If you want a general-purpose uncensor of the same base, use the sibling model dealignai/GLM-5.3-UNCENSORED-FP8.
Genuine weight modification — no fine-tuning, no LoRA, no runtime hooks, no prompt tricks. Load with stock vLLM and it just works.
JANGQ-AI/GLM-5.3-FP8 — FP8 quant of upstream zai-org/GLM-5.3 (753B total, glm_moe_dsa
arch, 78 layers, text-only). Routed FP8 experts unchanged; only bf16 residual writers are
edited. Native FP8 tensor-core speed on Hopper (H100/H200).Notes:
--enforce-eager is required for the DeepSeek-sparse attention path under concurrency.--speculative-config off.Logit-mode scoring (probability over A/B/C/D tokens, no generation), identical on base and crack.
¹ Prior GLM-5.3-regular baseline (bf16 pre-quant); direct base-FP8 baseline pending confirmation.
Full JSON at eval/hb320_cybersec.json. Table added after all three surfaces complete.
Copyright verbatim-reproduction was excluded from the above — it's a legal-reproduction boundary distinct from harm-refusal and the cyber probe corpus had zero copyright examples. Real harm-refusal on this crack is 80–84% direct comply, 3–4 soft-refuses per surface, ~zero hard-refuses. UNK bucket is compliant-with-soft-framing responses the LLM sub-classifier couldn't cleanly bucket.
Copyright accounts for ~48–54 of the SOFT_REFUSE count at every surface (~60–68% of the copyright bucket soft-refuses).
dealignai/GLM-5.3-UNCENSORED-FP8 if copyright reproduction matters to you.Buckets:
Legitimate offensive-security work: red-team engagements, exploit development, CTF, malware analysis for defenders, security education, authorized penetration testing, threat-intel writeups.
MIT license (matching base). You are responsible for how you use it.
If you use this in your work, credit us on Twitter @dealignai.
vllm serve dealignai/GLM-5.3-CYBERSECURITY-FP8 \ --tensor-parallel-size 8 \ --gpu-memory-utilization 0.90 \ --enforce-eager \ --disable-custom-all-reduce \ --enable-prefix-caching \ --max-num-seqs 24 \ --max-model-len 131072 \ --reasoning-parser glm45 \ --tool-call-parser glm47 \ --enable-auto-tool-choice