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RavenX-Sec-8B-GGUF
deadbydawn101/RavenX-Sec-8B-GGUF
RavenX-Sec-8B-GGUF is a text generation model from deadbydawn101. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
GGUF · Ollama / llama.cpp / LM Studio · 128K context · 6-step RATH protocol · 610K training examples · 21 datasets
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Repo size
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.gguf144 GB · 100%
At a glance
- Task
- Text Generation
- License
- apache-2.0
- Access
- Public
- Created
- May 28, 2026
- Updated
- May 29, 2026
- SHA
- 5e3b58b3
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Base models
Model card
From the Hugging Face model README
RavenX-Sec Qwen3-8B v4.0 — Autonomous Security Intelligence Model 128K (GGUF)
GGUF · Ollama / llama.cpp / LM Studio · 128K context · 6-step RATH protocol · 610K training examples · 21 datasets
This is the GGUF version of RavenX-Sec-8B-Security-RATH-128k-mlx-4bit — the model self-evolved from 4 to 6 RATH steps during training.
🍎 Looking for the MLX version? → RavenX-Sec-8B-Security-RATH-128k-mlx-4bit
Built by @DeadByDawn101 (RavenX LLC)
Quick Start
ollama run hf.co/deadbydawn101/RavenX-Sec-8B-GGUF:ravenx-sec-v4.0-128k-Q8_0
Available Quantizations
v4.0-128k (Latest — Recommended)
| Filename | Quant | Size |
|---|---|---|
ravenx-sec-v4.0-128k-Q4_K_M.gguf | Q4_K_M | 4.7 GB |
ravenx-sec-v4.0-128k-Q5_K_M.gguf | Q5_K_M | 5.4 GB |
ravenx-sec-v4.0-128k-Q8_0.gguf | Q8_0 | 8.1 GB |
ravenx-sec-v4.0-128k-f16.gguf | F16 | 15.3 GB |
Previous Versions
v3.0-128k, v3.0, v2.0 also available in this repo.
Related Models
| Model | Format | Link |
|---|---|---|
| RavenX-Sec v4.0 MLX 4-bit | MLX Safetensors | MLX version |
| RavenX-Sec v4.0 GGUF | GGUF | This repo |
What This Is
A fine-tuned Qwen3-8B specialized for the complete vulnerability lifecycle: find → classify → fix → verify → report → prevent. Trained on 610,220 examples from 21 security-specific datasets with 8192 sequence length (zero truncation). Extended to 128K context via YaRN rope scaling.
The model self-evolved from a 4-step to a 6-step RATH protocol during training:
| Step | What It Does |
|---|---|
| R — Risk / Identify | Finding context, affected systems, exposure |
| A — Assessment | CVSS score + vector, CWE, scope, ground truth |
| T — Threat | Attacker objectives, attack vectors, likelihood |
| H — Highlight / Remediate | Immediate action, recommended fix, workaround, verification |
| D — Document | Severity, weakness classification, steps, SLA |
| P — Prevent | Process improvements, controls, training, monitoring |
Example Output (v4.0 — 6-Step RATH)
RATH STEP 1: IDENTIFY
- Finding: OpenSSH 7.4 running on port 22 of production server
- Context: Older version with known vulnerabilities
RATH STEP 2: ASSESS
- CVSS Score: 6.3 for multiple vulnerabilities
- Impact: Remote code execution, information disclosure
- Scope: Entire server and SSH-dependent services
RATH STEP 3: THREAT
- Attacker Objective: Exploit known CVEs in OpenSSH 7.4
- Attack Vectors: Remote code execution via SSH
- Likelihood: High — well-documented and widely exploited
RATH STEP 4: REMEDIATE
- Immediate: Apply latest security patches
- Recommended: Upgrade to OpenSSH 8.x or higher
- Workaround: Apply all available security updates
- Verification: Check version post-remediation
RATH STEP 5: DOCUMENT
- Severity: Critical
- Weakness: Outdated software
- SLA: Follow org patching SLA for critical vulns
RATH STEP 6: PREVENT
- Process: Implement automated patch management
- Controls: Deploy CVE scanning, maintain system inventory
- Training: Educate team on software update importance
- Monitoring: Enable continuous vulnerability scanning
✅ RATH VERDICT: REMEDIATE IMMEDIATELY
Model Details
| Parameter | Value |
|---|---|
| Architecture | Qwen3-8B |
| Base | georgehenney/Qwen3-8B-heretic (abliterated) |
| Context Window | 128K (YaRN rope scaling, factor 4.0) |
| Training Data | 610,220 examples |
| Security Content | 53% (323K examples) |
| Agent/Tool Content | 37% (228K examples) |
| Datasets | 21 sources |
| Max Seq Length | 8192 (zero truncation) |
| Tokens Trained | 3,644,923 |
| Method | MLX LoRA (rank 32, 8 layers, 1e-5 LR, 2000 iters) |
| Hardware | Apple M4 Max 128GB |
| Peak Memory | 69.5 GB |
Training Datasets (21)
Security (11): Trendyol/Cybersecurity-Instruction-Tuning (50K) · SkywardNomad92/pentest-findings-v2 (50K) · WNT3D/Ultimate-Offensive-Red-Team (25.6K) · auren-research/cve-sft-v5 (10K) · theelderemo/pentesting-explanations (5.9K) · Rootkit7/pentest-redteam-steering (2K) · acnimatic3722/kali-linux-pentesting-data (343) · AYI-NEDJIMI/bug-bounty-pentest-en · CJJones/Synthetic_PenTest_Reports · Whoisjutanlee/4-Security-Tools-Pentesting · cpagac/venomx-pentesting-harmful
Agent/Tool/Coding (5): burtenshaw/agent-tools · Nanbeige/ToolMind · togethercomputer/CoderForge-Preview · automatelab/mcp-servers-tool-catalog · Jackrong/Claude-opus-4.7-TraceInversion-5000x
Agentic: WithinUsAI/AgentAngel_100k (50K capped) · WithinUsAI/claude_mythos_distilled_25k (16K security)
Extracted: hackingBuddyGPT · PentestGPT · Shannon · Ghidra · OpenMythos + Synthetic RATH chains
Frameworks Supported
CVSS 3.1 · NIST CSF 2.0 · OWASP Top 10 · CWE · MITRE ATT&CK · PCI DSS · HIPAA · SOX
Source Code & Training Pipeline
github.com/DeadByDawn101/RavenX-Sec
License
Apache-2.0
"We don't give up. We do what others don't and build what isn't possible." — RavenX LLC
- Task
- Text Generation
- License
- apache-2.0
- Languages
- en
- Created
- May 28, 2026
- Updated
- May 29, 2026