base_model: orcarouter/Qwen3.8-27B-Uncensored library_name: peft pipeline_tag: image-text-to-text license: other language:

• en
• fr tags:
• unsloth
• lora
• qlora
• cyber
• image-text-to-text
• vision
• video
• peft

model-index:

• name: Qwen3.8-27B-Uncensored-cyber — LoRA results:
  • task: type: text-generation name: Causal language modeling dataset: name: cyber SFT holdout type: dataset_cyber.quality.jsonl metrics:
    • type: loss value: 0.877844 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 — LoRA

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.

Multimodal

Kept modalities: vision, video. Media layers were frozen during text SFT (vision=False, audio=False). Load the merged Transformers folder (or GGUF + mmproj) to keep vision, video. Preservation audit: GO.

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>
I need to answer the user's question: "How do you design a reliable Zero Trust infrastructure? Answer with a concrete control list." The request is in English. I must produce the final answer in English. Need include concrete control list. Think about Zero Trust architecture: identity, device posture, least privilege, continuous verification, microsegmentation, encrypted traffic, logging, threat detection, automation, resilience. The user likely wants a control list, maybe mapped to NIST/ISO. Need be practical: prerequisites, architecture, controls: identity federation, MFA, device compliance, PEP/PDP, network segmentation, application access, data classification, encryption, key management, logging, SIEM, XDR, endpoint EDR, vulnerability management, software supply chain, incident response, resilience, admin isolation, secrets management, API security, cloud guardrails, continuous assessment. Also mention reliability not just policy: high availability, redundancy, failover, health checks, observability, change management. Could

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = 'orcarouter/Qwen3.8-27B-Uncensored'
adapter = "nico248000000000/Qwen3.8-27B-Uncensored-cyber-LoRA"
tok = AutoTokenizer.from_pretrained(adapter, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, adapter)

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.