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Model: deadbydawn101/RavenX-Sec-8B-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: georgehenney/Qwen3-8B-heretic
+tags:
+  - security
+  - cybersecurity
+  - pentest
+  - CVSS
+  - OWASP
+  - red-team
+  - bug-bounty
+  - 128k-context
+  - gguf
+  - qwen3
+  - ravenx
+  - rath-protocol
+  - tool-calling
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# 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`](https://huggingface.co/deadbydawn101/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](https://huggingface.co/deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit)
+
+**Built by [@DeadByDawn101](https://github.com/DeadByDawn101) (RavenX LLC)**
+
+## Quick Start
+
+```bash
+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](https://huggingface.co/deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit) |
+| **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](https://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*

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