RavenX-Sec Qwen3-8B v4.0 — Autonomous Security Intelligence Model 128K
MLX 4-bit · Apple Silicon native · 128K context · 6-step RATH protocol · 610K training examples · 21 datasets · Zero truncation
Qwen3-8B with security LoRA fused directly into the weights. The model self-evolved from 4 to 6 RATH steps during training — adding DOCUMENT and PREVENT phases for complete vulnerability lifecycle management.
📦 Looking for the GGUF version? → RavenX-Sec-8B-GGUF (Ollama / llama.cpp / LM Studio)
Part of the RavenX MLX Models collection.
Built by @DeadByDawn101 (RavenX LLC)
Quick Start
from mlx_lm import load, generate
model, tokenizer = load("deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit")
messages = [
{"role": "system", "content": "You are RavenX-Sec. Follow the RATH protocol for every finding."},
{"role": "user", "content": "You found OpenSSH 7.4 on port 22. Classify and remediate."}
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
response = generate(model, tokenizer, prompt=prompt, max_tokens=1024)
print(response)
Chat REPL
mlx_lm.chat --model "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit"
Related Models
| Model | Format | Link |
|---|---|---|
| RavenX-Sec v4.0 MLX 4-bit | MLX Safetensors | This repo |
| RavenX-Sec v4.0 GGUF | GGUF (F16/Q8/Q5/Q4) | RavenX-Sec-8B-GGUF |
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