Instructions to use deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

How to use deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit"

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-Security-RATH-128k-mlx-4bit

Run Hermes

hermes

MLX LM

How to use deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

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