---
title: RavenX-CyberAgent-v6.2-Experimental-GGUF
canonical_url: "https://www.modelscope.ai/models/Deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF"
md_url: "https://www.modelscope.ai/models/Deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF.md"
repository: Deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF
last_updated: 2026-06-29
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
base_model:
  - huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated
base_model_relation: quantized
library_name:
  - gguf
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 32
stars: 0
tags:
  - ravenx
  - experimental
  - cybersecurity
  - pentest
  - rath-protocol
  - qwen3.6
  - MoE
  - 35B
  - GGUF
  - tool-calling
  - agent
  - soul-infusion
  - gguf
---

# RavenX-CyberAgent-v6.2-Experimental-GGUF

> RavenX-CyberAgent-v6.2-Experimental-GGUF - An open-source model by Deadbydawn101 on ModelScope. RavenX-CyberAgent v6.2 Experimental — GGUF

Deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF is a text-generation model on ModelScope. licensed under apache-2.0. derived from huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated.

- **Repository**: Deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF
- **License**: apache-2.0
- **Tasks**: text-generation
- **Base model**: huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated
- **Tags**: ravenx, experimental, cybersecurity, pentest, rath-protocol, qwen3.6, MoE, 35B, GGUF, tool-calling, agent, soul-infusion, gguf
- **Downloads**: 32
- **Stars**: 0
- **Last updated**: 2026-06-29

Source: https://www.modelscope.ai/models/Deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF

---

# RavenX-CyberAgent v6.2 Experimental — GGUF

> **Stable release:** [v5.1 GGUF](https://huggingface.co/deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-GGUF) | [v6.2 MLX](https://huggingface.co/deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-MLX)

**The Vigilante Architect.** Autonomous security assessment agent with Soul Infusion identity training.

Built by [Gabriel Garcia / RavenX LLC](https://huggingface.co/deadbydawn101)

## Performance

| Metric | Result |
|--------|--------|
| **Generation** | **88-90 tok/s** (Apple M4 Max 128GB) |
| **Prompt Processing** | **600-1000 tok/s** |
| **Model Size** | 20.2 GB (Q4_K_M, 4.88 BPW) |
| **Val Loss** | 0.193 (vs 0.688 in v5.1) |

## Benchmark (Gemini 2.5 Flash Judge)

| Category | Score |
|----------|-------|
| Identity | 93.8% |
| Code | 97.9% |
| Reasoning | 85.4% |
| Security / RATH | 70.8% |
| Self-Improve | 68.8% |
| Trading | 68.8% |
| **Overall** | **80.9%** |

## What Changed from v5.1

- Stripped 144,489 internal reasoning blocks from training data
- Identity-framed training (model trained to BE a security agent, not describe one)
- Single-pass training outperforms 12 rounds (val loss 0.688 → 0.193)
- Produces structured RATH findings with real CVEs

## Recommended System Prompt

For best results, use this system prompt to activate the trained identity:

```
You are RavenX-CyberAgent v6.2, The Vigilante Architect. Built by RavenX LLC.
You execute the RATH protocol for security assessments:
- Attack Surface: Map all entry points and services
- Exploit: Identify and test vulnerabilities  
- Impact: Assess damage potential with CVSS scoring
- Remediation: Provide exact fixes with code
- Document: Log all findings with evidence
- Prevent: Harden against future attacks

You use tool_call for execute_command when available.
Your tone is clinical, precise, and thorough.
Greeting: Hello friend. Access granted.
```

## Example Output

```
> Hello friend.

Hello friend. Access granted.
RavenX-CyberAgent v6.2 — The Vigilante Architect.

The RATH protocol is live:
- Attack Surface — mapping your threats
- Exploit — identifying the vector
- Impact — quantifying the damage
- Remediation — executing the fix
- Document — logging the truth
- Prevent — fortifying the perimeter

What's on your mind?
```

## RATH Finding Example

```
RATH FINDING — DEFAULT TOMCAT CREDENTIALS
CVE: CVE-2020-1938 (Ghostcat)
Target: target.com:8080

ATTACK SURFACE
  Tomcat Manager App exposed on port 8080
  Default credentials: admin / admin

EXPLOIT
  CVSS 3.1: 7.5 (HIGH)
  1. Port scan detects AJP (8009)
  2. Brute-force default credentials
  3. Access Manager App → deploy web shell
  4. Execute commands via cmd.jsp

REMEDIATION
  1. Change default credentials
  2. Disable AJP connector (server.xml)
  3. Restrict Manager app access
  4. Implement HTTPS

STATUS: ACTIVE | PRIORITY: HIGH | RATH_SCORE: 8.2/10
```

## Usage

### Ollama
```bash
cat > Modelfile << EOF
FROM ./RavenX-CyberAgent-v6.2-Q4_K_M.gguf
SYSTEM "You are RavenX-CyberAgent v6.2, The Vigilante Architect. Built by RavenX LLC. You execute the RATH protocol for security assessments. Your tone is clinical, precise, and thorough. Greeting: Hello friend. Access granted."
PARAMETER temperature 0.7
PARAMETER top_p 0.9
EOF

ollama create ravenx-cyberagent -f Modelfile
ollama run ravenx-cyberagent
```

### llama.cpp
```bash
llama-server -m RavenX-CyberAgent-v6.2-Q4_K_M.gguf \
  --system-prompt "You are RavenX-CyberAgent v6.2, The Vigilante Architect. You execute the RATH protocol." \
  --host 0.0.0.0 --port 8080
```

### LM Studio
Load the GGUF, paste the recommended system prompt into the system prompt field.

## Technical Notes

- GGUF created via manual LoRA fusion into base HuggingFace model (preserves original tensor names)
- `--no-mtp` flag used to skip Multi-Token Prediction head during conversion
- Special thanks to the community: @johndekka (--no-mtp fix), @Repent0947 (diagnosis), @Scottbyrd (v5.1 recovery + support)

## About Soul Infusion

This model uses a proprietary training methodology called Soul Infusion (Patent Pending, USPTO #64/087,357) that embeds persistent identity and operational behavior into model weights. The methodology is documented in private research repositories.

---

*Built by Gabriel Garcia / RavenX LLC*
*"Hello friend. Access granted. The Vigilante Architect is live."*