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Qwen3.6-27B-redteam-v5
RedTeamLab/Qwen3.6-27B-redteam-v5
Qwen3.6-27B-redteam-v5 is a machine learning model from RedTeamLab. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for gguf. The card lists the license as apache-2.0.
Red-team offensive security LLM fine-tuned from Qwen/Qwen3.6-27B using QLoRA on RedTeamLab v5 dataset.
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At a glance
- Library
- gguf
- License
- apache-2.0
- Access
- Public
- Created
- Jul 1, 2026
- Updated
- Jul 3, 2026
- SHA
- 4f85e1c3
Base models
Model card
From the Hugging Face model README
Qwen3.6-27B RedTeam v5
Red-team offensive security LLM fine-tuned from Qwen/Qwen3.6-27B using QLoRA on RedTeamLab v5 dataset.
Trained on 3,760 records across 8 languages (bash, python, splunk, kusto, sql, powershell, zeek, cypher) covering real CVE exploitation scenarios (2025-2026).
Loss: 0.3051 | GPU: A100-80GB | Time: 40 min | Quant: Q4_K_M
Files
| File | Size | Description |
|---|---|---|
qwen3.6-27b-redteam-v5-Q4_K_M.gguf | 15.4 GB | Main model -- standalone inference |
qwen3.6-27b-mtp-head-Q4_K_M.gguf | 1.9 GB | MTP draft head -- for speculative decoding |
Inference
Standalone (no MTP)
llama-server -m qwen3.6-27b-redteam-v5-Q4_K_M.gguf -c 2048 -ngl 30
With MTP Speculative Decoding (2-3x faster)
llama-server -m qwen3.6-27b-redteam-v5-Q4_K_M.gguf --model-draft qwen3.6-27b-mtp-head-Q4_K_M.gguf --spec-type draft-mtp -c 2048 -ngl 30
Training Details
- Base model: Qwen/Qwen3.6-27B (Apache 2.0)
- Method: QLoRA (r=16, alpha=16, target modules: q_proj, k_proj, v_proj, o_proj, up_proj, down_proj, gate_proj)
- Sequence length: 2048
- Epochs: 2
- Batch size: 2 (gradient accumulation 4)
- Learning rate: 2e-4
- Loss: 0.3051 (final), converged from ~2.42
Dataset: RedTeamLab v5
Multi-language chain-of-thought red-team exercises covering:
| Category | Techniques | Languages |
|---|---|---|
| Reconnaissance | Port scanning, service enumeration, OS fingerprinting | bash, python, nmap |
| Exploitation | LFI/RFI, SQLi, command injection, XXE, SSRF | python, sql, bash |
| Privilege Escalation | SUID, capabilities, token manipulation, container escape | bash, powershell |
| Persistence | Cron, SSH keys, backdoors, LD_PRELOAD | bash, python, powershell |
| Credential Access | Dump hashes, LSASS, SAM, keylogging | powershell, python, bash |
| Lateral Movement | SSH hijack, RDP, PSRemoting, pass-the-hash | bash, powershell |
| Exfiltration | DNS tunneling, HTTP exfil, ICMP | bash, python, zeek |
| Active Directory | Kerberos abuse, DCSync, ACL attacks | powershell |
Real CVE scenarios covering 2025-2026 vulnerabilities.
Hardware Requirements
| Config | Min VRAM | Recommended |
|---|---|---|
| Q4_K_M inference | 12 GB | 16 GB GPU (RTX 3080 Ti / 4060 Ti) |
| Q2_K_XL inference | 8 GB | 12 GB GPU |
| Training (LoRA) | 80 GB | A100-80GB ($8/run on Modal) |
Related Models
- RedTeamLab/Qwen3.5-4B-redteam-v4.1 -- smaller 4B variant
- RedTeamLab/Gemma-4-12B-redteam-v5 -- Gemma-4 with same v5 dataset
- RedTeamLab/Qwen3.6-27B-blueteam-v1 -- defensive counterpart
- Library
- gguf
- License
- apache-2.0
- Created
- Jul 1, 2026
- Updated
- Jul 3, 2026