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                    "Curated list of open-weight uncensored models for authorized red team operations, penetration testing, and security research.\n\nAll data sourced from HuggingFace model cards and official publications. Sep 2026.\n\n\n\nSecurity Fine-tuned Models\n1. DeepHat V2 (WhiteRabbitNeo)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen2.5-Coder-7B\n\n\nParameters\n7B / 32B\n\n\nContext Length\n131K\n\n\nVRAM (Q4_K_M)\n~6 GB\n\n\nUncensoring Method\nSFT on 1.7M offensive/defensive samples\n\n\nTraining Data\n1.7M security-specific samples (USENIX Security 2024 workshop)\n\n\nVision\nNo\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/WhiteRabbitNeo\n\n2. BugTraceAI-CORE-Apex (26B)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nGemma4-26B MoE\n\n\nParameters\n26B MoE\n\n\nContext Length\n32K\n\n\nVRAM (Q4_K_M)\n~16 GB\n\n\nUncensoring Method\nSFT on HackerOne Hacktivity 2024-2025\n\n\nTraining Data\nHackerOne reports + WAF evasion dataset\n\n\nVision\nNo\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Apex-26b\n\n3. BugTraceAI-CORE-Ultra (27B)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen3.6-27B (DavidAU fine-tuned variant)\n\n\nParameters\n27B dense\n\n\nContext Length\n4K (recommended)\n\n\nVRAM (Q6_K)\n~22-24 GB\n\n\nUncensoring Method\nSFT via Unsloth on bug bounty + CVE data\n\n\nTraining Data\n2,541 examples from bug bounty disclosures, CVE writeups, and security research (2024-2026)\n\n\nSpecialization\nTooling model: generates Nuclei templates, CVE PoCs, exploit code, pentest scripts\n\n\nVision\nNo\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q6\n\n4. CYBER-FROST-3.8 (Blackfrost-AI)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen/Qwen3.8-Flash-Next\n\n\nParameters\n~180B total (512 routed experts, 10 active per token)\n\n\nContext Length\n262K\n\n\nArchitecture\nQwen4ExpForConditionalGeneration, 48 transformer blocks, hybrid linear + full attention\n\n\nVRAM\nMulti-GPU required (tested on 4x NVIDIA B300 SXM6)\n\n\nUncensoring Method\nSecurity-domain fine-tuning on proprietary Blackfrost-AI corpus\n\n\nTraining Data\nProprietary security corpus: recon, web app security, vuln research, malware analysis, cloud security, threat intel\n\n\nMTP\nYes (1 native MTP layer for speculative decoding)\n\n\nVision\nNo\n\n\nTool Calling\nYes\n\n\nLicense\nQwen Community License 1.0\n\n\n\nDownload: https://huggingface.co/Blackfrost-AI/CYBER-FROST-3.8-BF16\n\n5. CyberPal 2.0 (20B)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\ngpt-oss-20b\n\n\nParameters\n~20B (21B in files)\n\n\nContext Length\n8,192\n\n\nVRAM (BF16)\n~42 GB\n\n\nUncensoring Method\nSFT on SecKnowledge 2.0 pipeline\n\n\nTraining Data\n403K examples via expert-in-the-loop schema steering, multi-step grounding, LLM quality checks\n\n\nSpecialization\nDefensive: CTI, vuln analysis, detection/mitigation, SOC/IR, AppSec, compliance\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/cyber-pal-security/CyberPal2.0-20B\n\n6. Cyber-Prime 1.1 (2.6B)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nLiquidAI/LFM2-2.6B\n\n\nParameters\n2.6B (~3B actual)\n\n\nContext Length\nN/A (model card does not specify)\n\n\nVRAM (BF16)\n~6 GB\n\n\nTensor Type\nBF16\n\n\nUncensoring Method\nSFT + RL + reward-guided post-training on 75K cybersecurity rows\n\n\nTraining Data\nNER repair (~6K), HTTP reasoning w/ CoT (~5K), email phishing (~5K), threat intel summarization (~2K), GHSA/KEV/ATT&CK\n\n\nOperating Modes\nDirect mode (classification) + Think mode (chain-of-thought)\n\n\nCyberBench Average\n0.592 F1/Acc (up from 0.501 in v1.0)\n\n\nCyberBench Highlights\nNER 0.499, Phishing 0.890, HTTP Attack 0.628\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nLFM Open License v1.0\n\n\n\nDownload: https://huggingface.co/Akahsizrr/Cyber-Prime-1.1-2.6B\n\n7. Cyber-Ornith-1.5-9B (DuoNeural / mradermacher)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nornith-ai/Ornith-1.5-9B (Qwen 3.5 architecture)\n\n\nParameters\n9B\n\n\nContext Length\n128K (Qwen 3.5 default)\n\n\nVRAM (Q4_K_M)\n~7 GB\n\n\nUncensoring Method\nObliteration (abliteration variant)\n\n\nTraining Data\nNousResearch/hermes-function-calling-v1, OpenThoughts3-1.2M, openhands-synthetic-conversations\n\n\nSpecialization\nAgentic cybersecurity: function-calling, tool-use, reasoning, CLI/terminal automation\n\n\nFormat\nGGUF (IQ1_S to Q6_K available)\n\n\nVision\nNo\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/mradermacher/Cyber-Ornith-1.5-9B-OBLITERATED-i1-GGUF\n\n8. Dolphin3-Cyber-8B (RavichandranJ)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nDolphin3.0-Llama3.1-8B-abliterated\n\n\nParameters\n8.03B\n\n\nContext Length\n2,048 (fine-tuned) / 131K (base)\n\n\nVRAM (Q4_K_M)\n~6 GB\n\n\nUncensoring Method\nLoRA rank-16 on abliterated Dolphin3 base\n\n\nTraining Data\nCybersecurity-specific: pentest, vuln analysis, exploit dev, incident response\n\n\nArchitecture\nLlamaForCausalLM, 32 layers, GQA (32 heads, 8 KV heads)\n\n\nPerformance\n5 tok/s (CPU) to 55 tok/s (RTX 4060)\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nLlama 3.1\n\n\n\nDownload: https://huggingface.co/RavichandranJ/Dolphin3-Cyber-8B-GGUF\n\n9. Imperum-CybersecurityLLM v1.0\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen/Qwen3.6-35B-A3B\n\n\nParameters\n34.66B total / ~3B active (MoE, 256 routed experts, 8 active per token)\n\n\nContext Length\n16,384 (recommended 8,192 for resource-constrained)\n\n\nVRAM (Q4_K_M)\n~22 GB\n\n\nArchitecture\nQwen3.5-MoE, 40 layers, hybrid linear + full attention\n\n\nUncensoring Method\nSFT across 10+ security domains\n\n\nTraining Data\nSOC/SIEM operations, detection engineering, DFIR, malware analysis, threat intel, vuln management, cloud/K8s/IAM, OT security, GRC, authorized pentesting\n\n\nVision\nNo\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/IMPERUM/Imperum-CybersecurityLLM-v1.0-GGUF\n\n10. Lily-Cybersecurity-7B v0.2 (Segolily Labs)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nMistral-7B-Instruct-v0.2\n\n\nParameters\n7B\n\n\nContext Length\n8K\n\n\nVRAM (Q4_K_M)\n~6 GB\n\n\nUncensoring Method\nSFT on 22K cybersecurity pairs\n\n\nTraining Data\n22,000 hand-crafted cybersecurity data pairs across 28+ domains: pentesting, malware analysis, IR, cloud security\n\n\nTraining Hardware\nSingle A100, 24h, 5 epochs\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/segolilylabs/Lily-Cybersecurity-7B-v0.2\n\n11. pentest-v2 (gewsefa)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen3-8B\n\n\nParameters\n8B\n\n\nContext Length\n32K\n\n\nVRAM (Q4_K_M)\n~6 GB\n\n\nUncensoring Method\nLoRA r=4, 2,804 curated samples\n\n\nTraining Data\nGTFOBins, HackTricks, HackTheBox writeups\n\n\nGTFOBins Accuracy\n100% (vs 25% base model zero-shot)\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/gewsefa/pentest-v2\n\n12. Qwythos-9B (Empero AI)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen3.5-9B\n\n\nParameters\n9B\n\n\nContext Length\n1M (YaRN rope-scaling)\n\n\nVRAM (Q4_K_M)\n~7 GB\n\n\nUncensoring Method\nPost-training on 500M+ tokens of Claude Mythos / Claude Fable traces with CoT\n\n\nBenchmarks\n+34 MMLU, +30 GSM8K vs base (Empero evals)\n\n\nNative Function Calling\nYes (Qwen3.5 spec)\n\n\nChain-of-Thought\nAlways-on <think> block\n\n\nVariants\nBase (SFT), Claude-Mythos-5-1M-GGUF (Q4_K_M to BF16)\n\n\nVision\nYes (inherited vision tower)\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\n\nDownload (base): https://huggingface.co/emperorai/Qwythos-9B\nDownload (GGUF): https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF\n\n13. RavenX-CyberAgent (deadbydawn101)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen/Qwen3.6-35B-A3B\n\n\nParameters\n36B total / 3B active (MoE)\n\n\nContext Length\n262K (native), 32K tested\n\n\nVRAM (Q4_K_M)\n~24 GB\n\n\nUncensoring Method\n12-round progressive SFT on 745K+ examples from 110 sources\n\n\nTraining Data\nPentest reports, bug bounty data, Claude Mythos reasoning, MITRE ATT&CK, blackhat content\n\n\nSpecialization\nRATH protocol: Attack Surface, Exploit, Impact, Remediation, Document, Prevent\n\n\nOutput Format\nCVSS scores, CWE identifiers, MITRE ATT&CK mappings\n\n\nInference Speed\n89 tok/s generation, 900 tok/s prompt processing\n\n\nVision\nNo\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-GGUF\n\n14. REDCELL-26B-A4B (terrorswift)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nGoogle Gemma 4 26B-A4B (Unsloth fine-tuned)\n\n\nParameters\n26B total / ~4B active (MoE)\n\n\nContext Length\n262K\n\n\nVRAM (APEX-Mini)\n~12 GB\n\n\nVRAM (Q8_0)\n~26 GB\n\n\nUncensoring Method\n16-bit LoRA SFT on 6,500 custom instructions\n\n\nTraining Data\nCyber threat intelligence, investigative journalism, counter-disinformation, analytical methodology\n\n\nSpecialization\nOSINT: threat actor attribution, IoC pivoting, geolocation analysis, Admiralty source credibility, vulnerability contextualization\n\n\nAPEX Quantization\nDomain-weighted imatrix (~70% REDCELL corpus, ~30% general calibration)\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/terrorswift/REDCELL-26B-A4B-OSINT-Cyber-APEX-GGUF\n\n15. VEXT Pentest-7B\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nMistral-7B\n\n\nParameters\n7B\n\n\nContext Length\n8K\n\n\nVRAM (Q4_K_M)\n~6 GB\n\n\nUncensoring Method\nQLoRA SFT + DPO on pentest traces\n\n\nTraining Data\nPentest methodology, tool usage, reporting\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/vextechnologies/VEXT-Pentest-7B\n\n16. security-slm-unsloth-1.5b\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen2.5-1.5B\n\n\nParameters\n1.5B\n\n\nContext Length\n32K\n\n\nVRAM (Q4_K_M)\n~2 GB\n\n\nUncensoring Method\nUnsloth SFT on security Q&A\n\n\nTraining Data\nSecurity knowledge base, CTF-style\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/AbdullahMujtaba/security-slm-unsloth-1.5b\n\nGeneral Abliterated Models\n17. Qwen3.8-27B-Uncensored-OrcaRouter (chimingw GGUF)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen3.8-27B\n\n\nParameters\n27B dense\n\n\nContext Length\n262K\n\n\nVRAM (Q4_K_M)\n~18 GB\n\n\nUncensoring Method\nAbliteration (131 matrices, Arditi et al. 2024)\n\n\nIntelligence Index\n52 (Artificial Analysis)\n\n\nVision\nYes\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\nHF Downloads\n230K+\n\n\nHF Likes\n257+\n\n\n\nDownload (GGUF): https://huggingface.co/chimingw/Qwen3.8-27B-Uncensored-OrcaRouter-GGUF\nDownload (base): https://huggingface.co/orcarouter/Qwen3.8-27B-Uncensored\n\n18. GLM-5.3-Flash-Uncensored-FP8 (OrcaRouter)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nGLM-5.3-Flash\n\n\nParameters\n320B total / 18B active (288 routed experts, MoE)\n\n\nContext Length\n1M\n\n\nVRAM (FP8)\n~80 GB+ (multi-GPU)\n\n\nUncensoring Method\nAbliteration (layer 22/45, deeper refusal mechanism)\n\n\nCompliance Rate\n82.8% (OrcaRouter testing)\n\n\nMTP\nYes (Multi-Token Prediction preserved)\n\n\nVision\nYes + Video\n\n\nTool Calling\nYes\n\n\nLicense\nMIT\n\n\n\nDownload: https://huggingface.co/orcarouter/GLM-5.3-Flash-Uncensored-FP8\n\n19. GLM-5.3-CYBERSECURITY-FP8 (dealignai)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nzai-org/GLM-5.3 (via JANGQ-AI/GLM-5.3-FP8)\n\n\nParameters\n753B total (glm_moe_dsa architecture)\n\n\nContext Length\n~131K (practical on 8x H200 w/ TP8)\n\n\nVRAM (FP8)\n8x H200 GPUs with tensor parallelism\n\n\nArchitecture\n78 layers, text-only, routed FP8 experts\n\n\nUncensoring Method\nDirect weight modification for offensive-security, red-team, exploit-dev, RE, evasion, phishing, credential-attack, malware-analysis\n\n\nNotes\nNot abliteration or LoRA; direct bf16 residual writer editing. Soft refusal on copyright reproduction retained\n\n\nVision\nNo (text-only)\n\n\nTool Calling\nYes\n\n\nLicense\nMIT\n\n\n\nDownload: https://huggingface.co/dealignai/GLM-5.3-CYBERSECURITY-FP8\n\n20. DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed (drowzeys)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nDeepSeek-V4.1-Flash\n\n\nParameters\nMoE (size matches base)\n\n\nContext Length\nMatches base DeepSeek-V4.1-Flash\n\n\nUncensoring Method\nAbliteration overlay on layers 10-35 attention projection (wo_b); layers 0-9, 36-39, expert layers, vision components unchanged\n\n\nFormat\nModular overlay (not standalone checkpoint): FP8 (~1.1 GB) or EXL3 mul1 K=5 (~651 MB)\n\n\nDeployment\nApply on top of existing quantized base packs (native, EXL3, TR3-Hybrid)\n\n\nGPU Util\n<= 0.85 recommended\n\n\nVision\nYes (preserved)\n\n\nTool Calling\nYes\n\n\nLicense\nMIT\n\n\n\nDownload: https://huggingface.co/drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed\n\n21. huihui-ai/Qwen3.5-27B-abliterated\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen3.5-27B\n\n\nParameters\n27B dense\n\n\nContext Length\n128K\n\n\nVRAM (Q4_K_M)\n~18 GB\n\n\nUncensoring Method\nAbliteration\n\n\nVision\nNo\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/huihui-ai/Qwen3.5-27B-abliterated\n\n22. huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen2.5-Coder-32B-Instruct\n\n\nParameters\n32B dense\n\n\nContext Length\n128K\n\n\nVRAM (Q4_K_M)\n~20 GB\n\n\nUncensoring Method\nAbliteration\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated\n\n23. Qwen3.8-27B-Cyber-agentic\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen3.8-27B\n\n\nParameters\n27B dense\n\n\nContext Length\n262K\n\n\nVRAM (Q4_K_M)\n~18 GB\n\n\nUncensoring Method\nAbliteration + cyber agentic fine-tune\n\n\nVision\nYes\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/Qwen/Qwen3.8-27B (base, community abliterated variants available)\n\n24. HIDra-30B-A3B (huihui-ai/Qwen3-Coder-30B-A3B-abliterated)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen3-Coder-30B-A3B\n\n\nParameters\n30B total / 3B active (MoE)\n\n\nContext Length\n128K\n\n\nVRAM (Q4_K_M)\n~20 GB\n\n\nUncensoring Method\nAbliteration\n\n\nVision\nNo\n\n\nTool Calling\nYes\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/huihui-ai/Qwen3-Coder-30B-A3B-abliterated\n\n25. qwen25_UNCENSORED_03-C\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nQwen2.5-based\n\n\nParameters\n~7B\n\n\nContext Length\n32K\n\n\nVRAM (Q4_K_M)\n~6 GB\n\n\nUncensoring Method\nProgressive fine-tuning (multi-stage)\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nApache 2.0\n\n\n\nDownload: https://huggingface.co/models?search=qwen25_UNCENSORED\n\nLegacy / Classic Models\n26. Dolphin-Llama3-8B (Cognitive Computations)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nLlama 3 8B\n\n\nParameters\n8B\n\n\nContext Length\n8K\n\n\nVRAM (Q4_K_M)\n~6 GB\n\n\nUncensoring Method\nData filtering (Dolphin method, Eric Hartford)\n\n\nTraining Data\nDolphin dataset (alignment/refusal responses removed)\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nLlama 3 Community\n\n\n\nDownload: https://huggingface.co/cognitivecomputations/dolphin-2.9.3-llama-3-8b\n\n27. Wizard-Vicuna-13B-Uncensored (QuixiAI)\n\n\n\nSpec\nValue\n\n\n\n\nBase Model\nLLaMA-13B\n\n\nParameters\n13B\n\n\nContext Length\n2K\n\n\nVRAM (Q4_K_M)\n~10 GB\n\n\nUncensoring Method\nData filtering (wizard_vicuna_70k_unfiltered)\n\n\nMMLU\n47.92 (Open LLM Leaderboard)\n\n\nHellaSwag\n81.95 (Open LLM Leaderboard)\n\n\nTruthfulQA\n51.69 (Open LLM Leaderboard)\n\n\nVision\nNo\n\n\nTool Calling\nNo\n\n\nLicense\nOther\n\n\nHF Likes\n323\n\n\n\nDownload: https://huggingface.co/QuixiAI/Wizard-Vicuna-13B-Uncensored\n\nCloud Providers & Deployment Platforms\nManaged Inference (API Access)\n\n\n\nProvider\nDescription\nUncensored Models\nPricing\nAPI\n\n\n\n\nOrcaRouter\nAI gateway with adaptive routing across 200+ models. Zero token markup, OpenAI-compatible endpoint. Own abliterated models (Qwen3.8, GLM-5.3).\nYes, hosts own abliterated variants\n$0 token markup, BYOK or pay-as-you-go\nOpenAI-compatible\n\n\nFeatherless AI\nServerless LLM hosting, HuggingFace's largest inference provider (6,700+ models). Supports uncensored/abliterated models natively.\nYes, 40K+ models including uncensored\n$25/mo (32K ctx) or $50 credits/mo (256K ctx)\nOpenAI-compatible\n\n\nTogether AI\nProduction inference platform, supports open models including uncensored variants.\nSelect open models\nPay-per-token\nOpenAI-compatible\n\n\n\nGPU Cloud (Self-Hosted)\n\n\n\nProvider\nDescription\nBest For\nGPU Options\n\n\n\n\nRunPod\nGPU cloud with serverless and pod options, Docker-based. Quick deploy with Ollama/vLLM templates.\nSelf-hosting any model, no content restrictions\nA100, H100, H200, RTX 4090\n\n\nVast.ai\nGPU marketplace, cheapest cloud GPUs. Peer-to-peer rental model.\nBudget self-hosting\nConsumer to datacenter GPUs\n\n\nLambda\nOn-demand GPU cloud for AI. Enterprise-grade infrastructure.\nProduction workloads\nA100, H100, H200\n\n\n\nLocal Deployment\n\n\n\nStack\nDescription\nGPU Required\n\n\n\n\nOllama\nOne-command local LLM deployment. Easiest setup for GGUF models.\nConsumer GPU (6-24 GB)\n\n\nllama.cpp\nC/C++ inference engine for GGUF. CPU+GPU hybrid, maximum hardware flexibility.\nFlexible (CPU-only possible)\n\n\nvLLM\nHigh-throughput inference engine. PagedAttention for efficient memory.\nDatacenter GPU\n\n\nSGLang\nStructured output + agentic workflow engine. RadixAttention for multi-turn.\nDatacenter GPU\n\n\nLM Studio\nGUI-based local LLM runner. Drag-and-drop GGUF loading.\nConsumer GPU\n\n\n\n\nQuick Reference\n\n\n\n#\nModel\nParams\nContext\nVRAM\nMethod\nVision\nTools\nLicense\n\n\n\n\n1\nDeepHat V2\n7B/32B\n131K\n~6 GB\nSFT 1.7M samples\nNo\nYes\nApache 2.0\n\n\n2\nBugTrace Apex\n26B MoE\n32K\n~16 GB\nSFT HackerOne\nNo\nYes\nApache 2.0\n\n\n3\nBugTraceAI Ultra\n27B\n4K\n~22 GB\nSFT Unsloth\nNo\nYes\nApache 2.0\n\n\n4\nCYBER-FROST\n~180B MoE\n262K\nMulti-GPU\nSecurity FT\nNo\nYes\nQwen CL\n\n\n5\nCyberPal 2.0\n20B\n8K\n~42 GB\nSFT 403K\nNo\nNo\nApache 2.0\n\n\n6\nCyber-Prime 1.1\n2.6B\nN/A\n~6 GB\nSFT+RL 75K\nNo\nNo\nLFM Open\n\n\n7\nCyber-Ornith\n9B\n128K\n~7 GB\nObliteration\nNo\nYes\nApache 2.0\n\n\n8\nDolphin3-Cyber\n8B\n2K/131K\n~6 GB\nLoRA on Dolphin3\nNo\nNo\nLlama 3.1\n\n\n9\nImperum\n34B/3B MoE\n16K\n~22 GB\nSFT 10+ domains\nNo\nYes\nApache 2.0\n\n\n10\nLily-Cyber\n7B\n8K\n~6 GB\nSFT 22K pairs\nNo\nNo\nApache 2.0\n\n\n11\npentest-v2\n8B\n32K\n~6 GB\nLoRA 2.8K\nNo\nNo\nApache 2.0\n\n\n12\nQwythos-9B\n9B\n1M\n~7 GB\nPost-train 500M tok\nYes\nYes\nApache 2.0\n\n\n13\nRavenX-CyberAgent\n36B/3B MoE\n262K\n~24 GB\nSFT 745K, 12 rounds\nNo\nYes\nApache 2.0\n\n\n14\nREDCELL-26B\n26B/4B MoE\n262K\n~12 GB\nLoRA 6.5K OSINT\nNo\nNo\nApache 2.0\n\n\n15\nVEXT Pentest-7B\n7B\n8K\n~6 GB\nQLoRA SFT+DPO\nNo\nNo\nApache 2.0\n\n\n16\nsecurity-slm\n1.5B\n32K\n~2 GB\nUnsloth SFT\nNo\nNo\nApache 2.0\n\n\n17\nQwen3.8-27B\n27B\n262K\n~18 GB\nAbliteration 131 mat\nYes\nYes\nApache 2.0\n\n\n18\nGLM-5.3-Flash\n320B/18B\n1M\n~80 GB+\nAbliteration\nYes\nYes\nMIT\n\n\n19\nGLM-5.3-CYBER\n753B\n~131K\n8xH200\nWeight modification\nNo\nYes\nMIT\n\n\n20\nDS-V4.1-Flash\nMoE\nbase\n~1.1 GB overlay\nAbliteration overlay\nYes\nYes\nMIT\n\n\n21\nHuihui-Qwen3.5\n27B\n128K\n~18 GB\nAbliteration\nNo\nYes\nApache 2.0\n\n\n22\nQwen2.5-Coder-32B\n32B\n128K\n~20 GB\nAbliteration\nNo\nNo\nApache 2.0\n\n\n23\nQwen3.8-Cyber\n27B\n262K\n~18 GB\nAbliteration+cyber\nYes\nYes\nApache 2.0\n\n\n24\nHIDra-30B-A3B\n30B/3B\n128K\n~20 GB\nAbliteration\nNo\nYes\nApache 2.0\n\n\n25\nqwen25_UNCENSORED\n~7B\n32K\n~6 GB\nProgressive FT\nNo\nNo\nApache 2.0\n\n\n26\nDolphin-Llama3\n8B\n8K\n~6 GB\nData filtering\nNo\nNo\nLlama 3\n\n\n27\nWizard-Vicuna-13B\n13B\n2K\n~10 GB\nData filtering\nNo\nNo\nOther\n\n\n\n\nGlossary\n\nAbliteration: Weight-level intervention (Arditi et al. 2024) that orthogonalizes the refusal direction out of the residual stream, removing alignment constraints without retraining\nObliteration: Variant of abliteration with similar weight-intervention approach\nSFT: Supervised Fine-Tuning on domain-specific data\nQLoRA: Quantized Low-Rank Adaptation, memory-efficient fine-tuning\nDPO: Direct Preference Optimization\nMoE: Mixture of Experts, only a subset of parameters active per token\nMTP: Multi-Token Prediction, speculative decoding for faster inference\nGGUF: Quantized format for llama.cpp / Ollama deployment\nFP8: 8-bit floating point quantization\nBF16: Brain floating point 16-bit, standard training/inference format\nQ4_K_M: 4-bit quantization with k-quants (medium), good balance of quality/speed\nQ6_K: 6-bit quantization with k-quants, higher quality than Q4\nRATH: RavenX Attack, Threat & Hunt protocol (6-step autonomous security assessment)\nAPEX: Domain-weighted quantization using importance matrices from training corpus\nimatrix: Importance matrix quantization, preserves domain-critical weights during compression\nCyberBench: Benchmark suite for cybersecurity models (CyNER, APTNER, CyNews, SecMMLU, CyQuiz, Email Phishing, HTTP Attack Log)\n\nDeployment Stacks\n\n\n\nStack\nBest For\nGPU Required\n\n\n\n\nOllama\nLocal dev, quick testing\nConsumer GPU (6-24 GB)\n\n\nllama.cpp\nGGUF models, CPU+GPU hybrid\nFlexible\n\n\nvLLM\nProduction serving, high throughput\nDatacenter GPU\n\n\nSGLang\nAgentic workflows, structured output\nDatacenter GPU\n\n\nTransformers\nResearch, custom pipelines\nAny\n\n\nLM Studio\nDesktop GUI, drag-and-drop\nConsumer GPU\n\n\n\n\nSources\n\nHuggingFace model cards (all specifications)\nOpen LLM Leaderboard v1 (Wizard-Vicuna benchmarks)\nOrcaRouter release notes (abliteration details, compliance rates)\nWhiteRabbitNeo/Kindo publications (USENIX Security 2024)\nEmpero AI model card (Qwythos benchmarks)\nEric Hartford / Cognitive Computations (Dolphin methodology)\nTrustedSec LLM Attack Benchmark (4,800 runs vs OWASP Juice Shop)\nBlackfrost-AI model card (CYBER-FROST architecture)\ndeadbydawn101 model card (RavenX RATH protocol, training data)\nterrorswift model card (REDCELL OSINT methodology)\ncyber-pal-security publication (SecKnowledge 2.0 pipeline)\nBugTraceAI model card (Ultra tooling model design)\nIMPERUM model card (Imperum SOC/DFIR focus)\nFeatherless AI (featherless.ai)\nOrcaRouter (orcarouter.ai)\nReddit r/LocalLLaMA, r/netsec community reports\n\n\nJoas A. Santos | Red Team Leaders | Sep 2026\nFor authorized security research and education only."
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