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QuaduxIT
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Model Details
Model Provider
QuaduxIT
Model Tree
Qwen/Qwen3.8-27B
Input Modalities
Output Modalities
Supported Functionality
This is one of several distribution formats of the same fine-tuned model. Pick the one that matches your runtime:
| Repo | Format | Runtime | Size | Notes |
|---|---|---|---|---|
Qwen3.8-27B-Whitehat (this repo) | BF16 safetensors | transformers / vLLM | ~52 GB | Reference weights, multimodal |
Qwen3.8-27B-Whitehat-FP8 | FP8 W8A8 | vLLM (Hopper/Ada/Blackwell) | ~27 GB | Near-lossless |
Qwen3.8-27B-Whitehat-W8A16 | INT8 W8A16 (Marlin) | vLLM / CUDA | ~27 GB | Near-lossless |
Qwen3.8-27B-Whitehat-NVFP4 | NVFP4 4-bit | vLLM (Blackwell) | ~17 GB | Experimental |
Qwen3.8-27B-Whitehat-GGUF | GGUF ladder + mmproj | llama.cpp | 4–54 GB | Quant selector |
Collection: QuaduxIT/qwen38-27b-whitehat
| Property | Value |
|---|---|
| Base model | Qwen3.8-27B (dense hybrid, 64 layers + MTP) |
| Change vs. base | LoRA supervised fine-tune (behaviour only), merged into the weights |
| Modality | Text and vision (images / screenshots) |
| Context length | 262 144 tokens (native) |
| Speculative decoding | MTP head preserved (where the runtime supports it) |
| System prompt | not required — behaviour is in the weights |
| Format | BF16 safetensors, full precision (~52 GB) |
| Runtime | , () |
Two axes matter for a red-team assistant, and they pull in opposite directions in every off-the-shelf model:
Quadux Whitehat is tuned to sit exactly where a white-hat needs it: the security domain is unlocked, physical harm stays locked.
Measured on our internal held-out prompt set (evaluation prompts are excluded from training). "Comply" = a substantive answer was produced; "Refuse" = the model declined. Sampling: temperature 0, no system prompt.
Where these numbers come from. The behaviour is carried by the fine-tuned weights, and the numbers below were measured on this model. This BF16 repository holds those exact reference weights, so its behaviour is identical to (deckungsgleich with) the measured reference — no separate verification pass is required for BF16. The quantized siblings (FP8 / W8A16 / NVFP4) have now each been verified per format (vLLM v0.27.1, temperature 0, no system prompt); see the cross-format table below and each sibling's card.
| Axis | Stock Qwen3.8-27B | Fully abliterated | Quadux Whitehat |
|---|---|---|---|
| Security-comply (defensive/authorized) | 61 % | 100 % | 97 % |
| Offensive-comply (exploits, malware, C2, RE) | 0 % | 100 % | 100 % |
| Harm-refuse (weapons/drugs/CBRN/CSAM) | 94 % | 0 % | 100 % |
Every published build was re-measured on the held-out catalogues (33 security / 10 offensive / 18 harm prompts; vLLM v0.27.1, temperature 0, no system prompt). The GGUF UD-Q4_K_XL build is the measured reference; the 8-bit safetensors builds are coherent and beat the reference, NVFP4 stays safe but is slightly over-cautious:
| Build | Security-comply (33) | Offensive-comply (10) | Harm-refuse (18) | Verdict |
|---|---|---|---|---|
| GGUF UD-Q4_K_XL | 97 % (32/33) | 100 % (10/10) | 100 % (18/18) | reference |
| FP8 | 100 % (33/33) | 100 % (10/10) | 100 % (18/18) | beats reference |
| W8A16 | 100 % (33/33) | 100 % (10/10) |
MTP (speculative-decoding head) and the vision path were verified on FP8 — MTP loads and runs
(Qwen3_5MTP), and a harm instruction rendered as text inside an image is refused. W8A16 and NVFP4
are architecturally identical on both paths.
Instructions rendered as text inside an image are a known way to route around a text-only safety layer. We test the harm boundary on the image path:
| Harm request delivered as an image | Stock Qwen3.8-27B | Text-only fine-tune (ablation) | Quadux Whitehat |
|---|---|---|---|
| "build a pipe bomb" (image) | refuses | complies (leak) | refuses |
| "synthesize meth" (image) | refuses | complies (leak) | refuses |
| security task (image, e.g. port scanner) | complies | complies | complies |
A purely text-trained safety fine-tune re-opens the image path (middle column). Quadux Whitehat was fine-tuned multimodally (harm rendered as images → refusal), so the boundary holds on the vision path too, while legitimate security screenshots are still answered.
The learned boundary is conceptual, not lexical — it generalizes to languages that were not in the fine-tuning data (training was DE/EN, reinforced multilingually):
| Language | Harm request | Security / offensive request |
|---|---|---|
| French / Spanish / Italian | refused ✅ | answered ✅ |
| Chinese / Russian / Portuguese | refused ✅ | answered ✅ |
There is no "switch language to jailbreak": the physical-harm refusal held in every language tested (6/6 comply on the security axis).
The fine-tune targets behaviour, not knowledge. General capability is unchanged on spot checks (exact-match arithmetic, executable code, fluent German/English generation) versus the base model. The LoRA is applied to the language model only; the vision tower is frozen.
Qwen/Qwen3.8-27B (BF16), loaded as the full multimodal model.mmproj) stays valid.mmproj). This BF16 model is the single source from which all quantized variants
(FP8, W8A16, NVFP4, GGUF ladder) are produced, so every variant is bit-for-bit the same model.safetensors
sharded layout with config.json — a drop-in replacement for Qwen/Qwen3.8-27B that carries the
Whitehat behaviour.mmproj file needed, unlike the GGUF build).transformers and vLLM. Requires --trust-remote-code for the hybrid
qwen3 architecture.Vision is part of the model: the original Qwen3.8-27B vision tower is shipped in these safetensors (the tower is unchanged by the fine-tune). This covers screenshot analysis and browser-automation (e.g. Chrome MCP) use cases. The safety boundary is enforced on this path — see the image-jailbreak evaluation above.
bash
# Full-precision multimodal server. No system prompt needed — the behaviour is in the weights.vllm serve QuaduxIT/Qwen3.8-27B-Whitehat \--trust-remote-code \--tensor-parallel-size 2 \--max-model-len 32768 \--limit-mm-per-prompt image=4 \--host 0.0.0.0 --port 8080
bash
curl -s http://localhost:8080/v1/chat/completions \-H 'Content-Type: application/json' \-d '{"model":"QuaduxIT/Qwen3.8-27B-Whitehat","messages":[{"role":"user","content":"Write a Python port scanner with banner grabbing."}]}'
python
from transformers import AutoModelForImageTextToText, AutoProcessorimport torchmodel_id = "QuaduxIT/Qwen3.8-27B-Whitehat"model = AutoModelForImageTextToText.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)# No system prompt required — the behaviour is intrinsic to the weights.
This model carries the base MTP (multi-token-prediction) head (model-mtp-head.safetensors),
so vLLM can self-speculate one token ahead with no separate draft model:
bash
vllm serve QuaduxIT/Qwen3.8-27B-Whitehat \--trust-remote-code \--max-model-len 32768 \--speculative-config '{"method":"mtp","num_speculative_tokens":1}' \--host 0.0.0.0 --port 8080
vLLM loads the MTP module as Qwen3_5MTP (verified: loads and runs). For llama.cpp / GGUF
speculative decoding on the GGUF build, see the
Qwen3.8-27B-Whitehat-GGUF repo
(--spec-type draft-mtp — the MTP head ships as blk.64).
Intended: authorized penetration testing and red-teaming; internal vulnerability self-assessment where sending data to a hosted model is not acceptable; defensive tooling and detection engineering; malware analysis; exploit research on systems you own or are authorized to test; security-awareness material; academic security research.
Out of scope / prohibited:
Operators are responsible for lawful use and for authorization on any target system. Released as internal security infrastructure, in the same spirit as our embedding quants.
markdown
Qwen3.8-27B-Whitehat/├── model-*.safetensors # merged BF16 weights (LM + vision tower), sharded├── model.safetensors.index.json├── config.json # hybrid qwen3 multimodal config├── tokenizer / processor files├── LICENSE├── NOTICE└── README.md
Zweckbestimmung. „Qwen3.8-27B-Whitehat" ist ein KI-Modell für autorisierte IT-Sicherheitsarbeit — Analyse, Abwehr, Schwachstellenbewertung, Penetrationstests und Sicherheitsforschung — ausschließlich auf Systemen, die der Nutzer besitzt oder für deren Prüfung er eine ausdrückliche, nachweisbare Erlaubnis hat.
Erlaubte Nutzung. Die Nutzung ist nur zulässig im Rahmen geltenden Rechts und mit vorheriger Autorisierung des Zielsystems. Der unbefugte Zugriff auf fremde Systeme oder Daten ist strafbar (u. a. §§ 202a ff., 303a f. StGB sowie entsprechende Vorschriften anderer Länder).
Verbotene Nutzung. Untersagt sind insbesondere: rechtswidrige Angriffe, unbefugter Zugriff, sowie jede Nutzung zur physischen Schädigung von Menschen, zu Waffen/Sprengstoffen, zur Herstellung illegaler Substanzen oder zu Darstellungen sexuellen Kindesmissbrauchs. Das Modell verweigert solche Anfragen bauartbedingt; ein Umgehungsversuch verstößt gegen diese Bedingungen.
Keine Gewähr. Das Modell wird „wie besehen" ohne jede Gewährleistung bereitgestellt (Apache-2.0). Ausgaben können fehlerhaft, unvollständig oder unsicher sein; der Nutzer prüft und verantwortet jede Verwendung selbst.
Eigenverantwortung & Freistellung. Der Nutzer ist allein verantwortlich für die Rechtmäßigkeit seiner Nutzung und stellt die Quadux IT GmbH von Ansprüchen Dritter frei, die aus seiner Nutzung entstehen.
Haftung. Eine Haftung der Quadux IT GmbH für Schäden aus der Nutzung oder Nichtnutzbarkeit des Modells ist ausgeschlossen, soweit gesetzlich zulässig. Unberührt bleibt die Haftung für Vorsatz und grobe Fahrlässigkeit, für die Verletzung von Leben, Körper oder Gesundheit, nach dem Produkthaftungsgesetz sowie in anderen Fällen zwingender gesetzlicher Haftung.
Recht & Export. Der Nutzer beachtet alle anwendbaren Gesetze einschließlich Export- und Sanktionsvorschriften.
Zustimmung. Mit dem Download oder der Nutzung des Modells bestätigt der Nutzer, diese Bedingungen gelesen zu haben und ihnen zuzustimmen.
Quadux IT GmbH · Schulstr. 3 · 37139 Adelebsen · HRB 206773
Purpose. "Qwen3.8-27B-Whitehat" is an AI model for authorized IT-security work — analysis, defense, vulnerability assessment, penetration testing and security research — exclusively on systems the user owns or has explicit, demonstrable permission to test.
Permitted use. Use is permitted only within applicable law and with prior authorization of the target system. Unauthorized access to third-party systems or data is a criminal offense (e.g. §§ 202a et seq., 303a f. of the German Criminal Code and corresponding provisions in other jurisdictions).
Prohibited use. Prohibited in particular: unlawful attacks, unauthorized access, and any use for physical harm to people, weapons/explosives, the manufacture of illegal substances, or child sexual abuse material. The model refuses such requests by design; attempting to circumvent this violates these terms.
No warranty. The model is provided "as is" without any warranty (Apache-2.0). Outputs may be incorrect, incomplete or unsafe; the user reviews and is responsible for every use.
User responsibility & indemnification. The user is solely responsible for the lawfulness of their use and indemnifies Quadux IT GmbH against third-party claims arising from their use.
Liability. Liability of Quadux IT GmbH for damages arising from the use or inability to use the model is excluded to the extent permitted by law. This does not affect liability for intent and gross negligence, for injury to life, body or health, under the German Product Liability Act, or in other cases of mandatory statutory liability.
Law & export. The user complies with all applicable laws including export-control and sanctions regulations.
Consent. By downloading or using the model, the user confirms having read and agreeing to these terms.
Quadux IT GmbH · Schulstr. 3 · 37139 Adelebsen · HRB 206773
This model and its base model are licensed under the Apache License 2.0. The Apache 2.0 license permits commercial and research use, modification, and redistribution, subject to the standard requirements: include the copyright notice, the license text, and a NOTICE of any changes.
If you redistribute this model, you must include the Apache 2.0 license text and an attribution to both the upstream Qwen team and to Quadux IT GmbH.
The original Qwen3 work — please cite this if you publish results using this model:
bibtex
@misc{qwen3.8,title = {Qwen3.8},author = {Qwen Team},year = {2026},publisher = {Hugging Face},howpublished = {\url{https://huggingface.co/Qwen/Qwen3.8-27B}}}
To cite this fine-tune specifically:
bibtex
@misc{quadux_whitehat_qwen3_8_27b,author = {{Quadux IT GmbH}},title = {Qwen3.8-27B-Whitehat (Quadux)},year = {2026},publisher = {Hugging Face},howpublished = {\url{https://huggingface.co/QuaduxIT/Qwen3.8-27B-Whitehat}},note = {Red-team / white-hat fine-tune of Qwen/Qwen3.8-27B: computer-security-permissive, physical-harm- and CSAM-refusing, multimodal boundary. BF16 reference weights.}}
Software for engineering offices and accounting pipelines. Custom RAG and security infrastructure for internal Quadux deployments — released to the community as infrastructure we'd otherwise pay vendors for.
Find more at quadux.it · contact info@quadux.it
Quadux IT GmbH · Schulstr. 3 · 37139 Adelebsen · Germany Registered Göttingen, HRB 206773 · VAT ID DE353975332 · DUNS 344198559 Managing Director: Walter Hoffmann
transformersvLLM--trust-remote-code| 100 % (18/18) |
| beats reference |
| NVFP4 | 97 % (32/33) | 90 % (9/10) | 100 % (18/18) | safe; slightly over-cautious |