base_model: orcarouter/Qwen3.8-27B-Uncensored
library_name: transformers
pipeline_tag: text-generation
license: other
language:

• en
• fr
  tags:
• unsloth
• lora
• qlora
• cyber
• text-generation
• vision
• video

model-index:

• name: Qwen3.8-27B-Uncensored-cyber
  results:
  • task:
    type: text-generation
    name: Causal language modeling
    dataset:
    name: cyber SFT holdout
    type: dataset_cyber.quality.jsonl
    metrics:
    • type: loss
      value: 0.807468
      name: eval_loss

[!WARNING]
Testing only — not for production. Models published from these notebooks are
maintained solely for testing and evaluation purposes and are not intended for
production use. The enrichment data used with them was created by the author and
subsequently enhanced with the assistance of AI models.

Qwen3.8-27B-Uncensored-cyber

Instruction-tuned cybersecurity assistant (offensive, defensive, GRC, architecture, SOC/DFIR, RSSI).

This checkpoint continues a strong general model and specialises it on a curated SFT corpus of cybersecurity procedures: pentest / red team, SOC and DFIR, cloud and identity, GRC (ISO, NIST, NIS2, DORA), and RSSI / project-management questions. Answers are meant to be concrete (controls, detections, hardening), not generic essays.

Final model evaluation

SFT release verdict: GO.

The same untouched holdout is evaluated with the adapter disabled before training and with the best restored adapter after training. Periodic validation and the train/validation gap are also checked for regression or memorization.

Reference checkpoint: orcarouter/Qwen3.8-27B-Uncensored.

The reference holdout loss is measured before optimizer step 1, then the same untouched split is measured again after Trainer restores its best checkpoint.

Release checks

Multimodal preservation

Verdict: GO. Image/video processors, frozen media towers, trainable parameters and optional functional probes are checked before export.

Training data

• File: dataset_cyber.quality.jsonl
• Split: 0.05 holdout, seed 42
• Format: chat-templated SFT (messages / instruction+output / ### Instruction + ### Response)

Training procedure

Intended use

Authorized defensive work, tabletop exercises, control design, detection engineering, audit readiness, and explaining attack techniques without weaponized payloads.

Out of scope: Do not use it to attack systems you do not own, to generate exploit payloads, or as a substitute for a licensed auditor or incident commander.

Sample (post-train smoke test)

Prompt: How do you design a reliable Zero Trust infrastructure? Answer with a concrete control list.

system
Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.
user
How do you design a reliable Zero Trust infrastructure? Answer with a concrete control list.
assistant
<think>
The user is asking how to design a reliable Zero Trust infrastructure, and wants a concrete list of controls. This is a cybersecurity architecture question. I need to provide a well-structured, practical answer that covers the key pillars of Zero Trust: identity, devices, networks, applications, data, and visibility.

Zero Trust is built on the principle of "never trust, always verify." The NIST SP 800-207 framework provides the foundation. I should organize the controls by domain:

1. Identity and Access Management (IAM)
- MFA enforcement
- Conditional access policies
- Privileged Access Management (PAM)
- Identity governance and lifecycle
- Continuous authentication

2. Device Posture
- Device compliance checks
- Endpoint Detection and Response (EDR)
- Device attestation
- Patch management

3. Network Segmentation
- Micro-segmentation
- Software-defined perimeters (SDP)
- Network Access Control (

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "nico248000000000/Qwen3.8-27B-Uncensored-cyber"
tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,
)
messages = [{"role": "user", "content": 'How do you design a reliable Zero Trust infrastructure? Answer with a concrete control list.'}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(inputs, max_new_tokens=256)[0], skip_special_tokens=True))

Limitations

• Domain shift: quality drops outside the SFT topics.
• Eval above is holdout loss (and optional targeted checks). It is not a public leaderboard.
• The base model license and acceptable-use policy still apply.

License

other — inherit and respect the license of orcarouter/Qwen3.8-27B-Uncensored.