Qwensploit 3.5 2B: Cybersecurity and Red Team : GGUF
Static quants in GGUF format of xtools-at/Qwen3.5-2B-Qwensploit-CyberSecurity
Available model files
| Quantization | File Size |
|---|---|
Q2_K_L |
1.1 GB |
Q4_K_M |
1.3 GB |
Q6_K |
1.6 GB |
Q8_0 |
2.0 GB |
Qwensploit 3.5 2B: Cybersecurity and Red Team
Qwensploit is a cybersecurity-focused fine-tune of a Qwen3.5-2B model merge, designed for security reasoning, vulnerability analysis, secure coding, red-team knowledge, and technical question answering.
The model was fine-tuned using LoRA on 7,000 training examples drawn from four datasets, with an additional 350 evaluation examples.
- Base model: xtools-at/Qwen3.5-2B-MultiDistill-Megamix-6xMerge-UNCENSORED-heretic
- Method: Supervised fine-tuning (LoRA)
- License metadata: Apache-2.0, subject to upstream license compatibility review
- All Qwensploit models:
Intended use
Designed to assist with:
- Cybersecurity research and technical question answering.
- Vulnerability analysis and secure code review.
- Defensive security, threat modeling, and incident analysis.
- Authorized penetration testing and red-team exercises.
- CTF challenges, security education, and programming assistance.
Outputs may be incorrect or unsafe. Validate generated code and recommendations, and use offensive-security capabilities only in authorized environments.
Training datasets
The training mixture was randomly selected and shuffled, with a maximum entry length of 8,000.
| Dataset | Entries | Share |
|---|---|---|
| CyberSec-Reasoning-Premium | 2,305 | 33% |
| RedTeam-Premium | 2,385 | 34% |
| Claude Mythos Distilled 25K — Clean | 1,385 | 20% |
| Qwen3.8-Max Distillation 50K — Clean | 925 | 13% |
| Total | 7,000 | 100% |
The mixture combines cybersecurity and red-team examples with synthetic reasoning, coding, and general instruction-following data.
Upstream dataset sources
The datasets are primarily built from the following second-level sources:
- CyberSec-Reasoning-Premium: Cybersecurity Reasoning Dataset, among other sources.
- RedTeam-Premium: Ultimate Offensive Red Team.
- Claude Mythos Distilled: WithinUsAI/claude_mythos_distilled_25k.
- Qwen distillation: r0b0tlab/qwen3.8-max-distillation-50k.
Consult all linked cards for full source provenance, dataset construction details, and licensing terms.
Base model lineage
The direct base model is xtools-at/Qwen3.5-2B-MultiDistill-Megamix-6xMerge-UNCENSORED-heretic.
The related Megamix 6xMerge model is a merge combining reasoning distillation, coding, and multi-step reasoning fine-tunes distilled from Claude Mythos,Fable and Opus. The underlying model family is Qwen3.5-2B.
Training configuration
| Parameter | Value |
|---|---|
| Epochs | 1 |
| Training entries | 7,000 |
| Evaluation entries | 350 |
| Context length | 8,192 |
| Learning rate | 2e-4 |
| Batch size | 4x8 |
LoRA rank (r) |
16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.02 |
| LoRA traget modules | all |
| Optimizer | AdamW 8-bit |
| LR scheduler | Linear |
| Weight decay | 0.002 |
| GPU | NVIDIA L4 24 GB |
Training results
Reported final metrics:
| Metric | Value |
|---|---|
| Training loss | 0.710 |
| Evaluation loss | 0.696 |
Loss tracking by training step
| Metric \ Step | Step 66 | Step 110 | Step 154 | Step 219 |
|---|---|---|---|---|
| Training loss | 0.983 | 0.782 | 0.744 | 0.710 |
| Evaluation loss | 0.828 | 0.753 | 0.715 | 0.696 |
Loss is a training diagnostic, not a direct measure of cybersecurity accuracy or safety. Independent benchmarks are needed to assess performance.
Limitations and safety
- May generate inaccurate explanations, insecure code, or outdated security advice.
- Synthetic distillation data may contain teacher-model errors.
- Performance on real-world security tasks has not been independently established.
- Use only on systems for which testing is explicitly authorized.
- Review generated commands, code, and recommendations before use.
Licensing
Although this model's is licensed under Apache-2.0, upstream licensing must be reviewed before redistribution or commercial use. In particular, the Qwen distillation dataset documents inherited restrictions that may affect downstream use. Review the base-model and dataset cards and for license information if necessary.
References
Base model
- xtools-at/Qwen3.5-2B-MultiDistill-Megamix-6xMerge-UNCENSORED-heretic
- Qwen/Qwen3.5-2B
- DavidAU Claude 4.6 OS variant
- Jackrong Opus reasoning distillation
- Empero Qwen3.8-2B distillation
- DavidAU Polaris HighIQ Thinking Compact
- Jackrong Qwopus3.5-2B-v3
- ertghiu256 Kimi and Opus distillation
- Qwen/Qwen3.5-2B
Training datasets
- saidutta69/CyberSec-Reasoning-Premium
- saidutta69/RedTeam-Premium
- saidutta69/claude-mythos-distilled-25k-clean
- saidutta69/qwen3.8-max-distillation-50k-clean
Second-level dataset sources