Instructions to use deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF",
	filename="RavenX-CyberAgent-v6.2-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-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M

Use Docker

docker model run hf.co/deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M

Ollama
How to use deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF with Ollama:
```
ollama run hf.co/deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M
```

Unsloth Studio

How to use deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-GGUF to start chatting

How to use deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-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-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF with Docker Model Runner:
```
docker model run hf.co/deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M
```

Lemonade

How to use deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.RavenX-CyberAgent-v6.2-Experimental-GGUF-Q4_K_M

List all available models

lemonade list

RavenX-CyberAgent v6.2 Experimental — GGUF

Stable release: v5.1 GGUF | v6.2 MLX

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

Built by Gabriel Garcia / RavenX LLC

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

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

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."

Downloads last month: -

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for deadbydawn101/RavenX-CyberAgent-v6.2-Experimental-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Adapter

lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled

Adapter

huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated

Quantized

(10)

this model