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 Settings
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-8B-Security-RATH-128k-mlx-4bit / README.md

deadbydawn101

v4.0: 610K examples, 6-step RATH, zero truncation

72d9f8e verified 9 days ago

preview code

raw

history blame contribute delete

6.07 kB

	---
	license: apache-2.0
	base_model: georgehenney/Qwen3-8B-heretic
	tags:
	- security
	- cybersecurity
	- pentest
	- CVSS
	- OWASP
	- red-team
	- bug-bounty
	- 128k-context
	- MLX
	- Safetensors
	- qwen3
	- 4-bit
	- apple-silicon
	- ravenx
	- rath-protocol
	- tool-calling
	language:
	- en
	pipeline_tag: text-generation
	library_name: mlx
	---

	# 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](https://huggingface.co/deadbydawn101/RavenX-Sec-8B-GGUF) (Ollama / llama.cpp / LM Studio)

	Part of the [RavenX MLX Models](https://huggingface.co/collections/deadbydawn101/ravenx-mlx-models-apple-silicon-inference-stack-67f7270ac5b85c49a6c0f47f) collection.

	Built by [@DeadByDawn101](https://github.com/DeadByDawn101) (RavenX LLC)

	## Quick Start

	```python
	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
	```bash
	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](https://huggingface.co/deadbydawn101/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

	## 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