Instructions to use deadbydawn101/RavenX-Sec-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deadbydawn101/RavenX-Sec-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="deadbydawn101/RavenX-Sec-8B-GGUF",
	filename="ravenx-sec-v2.0-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use deadbydawn101/RavenX-Sec-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use deadbydawn101/RavenX-Sec-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deadbydawn101/RavenX-Sec-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deadbydawn101/RavenX-Sec-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M

Ollama
How to use deadbydawn101/RavenX-Sec-8B-GGUF with Ollama:
```
ollama run hf.co/deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use deadbydawn101/RavenX-Sec-8B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for deadbydawn101/RavenX-Sec-8B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for deadbydawn101/RavenX-Sec-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for deadbydawn101/RavenX-Sec-8B-GGUF to start chatting

How to use deadbydawn101/RavenX-Sec-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use deadbydawn101/RavenX-Sec-8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use deadbydawn101/RavenX-Sec-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M
```

Lemonade

How to use deadbydawn101/RavenX-Sec-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deadbydawn101/RavenX-Sec-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.RavenX-Sec-8B-GGUF-Q4_K_M

List all available models

lemonade list

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

Downloads last month: 493

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for deadbydawn101/RavenX-Sec-8B-GGUF

Base model

Qwen/Qwen3-8B-Base

Finetuned

georgehenney/Qwen3-8B-heretic

Quantized

(3)

this model

Collection including deadbydawn101/RavenX-Sec-8B-GGUF

RavenX MLX Models — Apple Silicon Inference Stack

Collection

TurboQuant 4-bit mlx-lm models. TriAttention compatible. PR #1 merged MIT+NVIDIA. • 9 items • Updated 6 days ago • 1