Text Generation
MLX
Safetensors
English
qwen3
security
cybersecurity
pentest
CVSS
OWASP
red-team
bug-bounty
128k-context
MLX
Safetensors
4-bit precision
apple-silicon
ravenx
rath-protocol
tool-calling
conversational
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
- MLX
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) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
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
- 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"} ] }' - Hermes Agent
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
- Atomic Chat
- OpenClaw
How to use deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit with OpenClaw:
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 OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
v4.0: 610K examples, 6-step RATH, zero truncation
Browse files
README.md
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---
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library_name: mlx
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
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pipeline_tag: text-generation
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base_model: georgehenney/Qwen3-8B-heretic
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tags:
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---
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---
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license: apache-2.0
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base_model: georgehenney/Qwen3-8B-heretic
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tags:
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- security
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- cybersecurity
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- pentest
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- CVSS
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- OWASP
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- red-team
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- bug-bounty
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- 128k-context
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- MLX
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- Safetensors
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- qwen3
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- 4-bit
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- apple-silicon
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- ravenx
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- rath-protocol
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- tool-calling
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language:
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- en
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pipeline_tag: text-generation
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library_name: mlx
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---
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# RavenX-Sec Qwen3-8B v4.0 — Autonomous Security Intelligence Model 128K
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> **MLX 4-bit** · Apple Silicon native · 128K context · 6-step RATH protocol · 610K training examples · 21 datasets · Zero truncation
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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.
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> 📦 **Looking for the GGUF version?** → [RavenX-Sec-8B-GGUF](https://huggingface.co/deadbydawn101/RavenX-Sec-8B-GGUF) (Ollama / llama.cpp / LM Studio)
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Part of the [RavenX MLX Models](https://huggingface.co/collections/deadbydawn101/ravenx-mlx-models-apple-silicon-inference-stack-67f7270ac5b85c49a6c0f47f) collection.
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**Built by [@DeadByDawn101](https://github.com/DeadByDawn101) (RavenX LLC)**
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## Quick Start
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit")
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messages = [
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{"role": "system", "content": "You are RavenX-Sec. Follow the RATH protocol for every finding."},
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{"role": "user", "content": "You found OpenSSH 7.4 on port 22. Classify and remediate."}
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]
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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response = generate(model, tokenizer, prompt=prompt, max_tokens=1024)
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print(response)
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```
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### Chat REPL
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```bash
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mlx_lm.chat --model "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit"
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```
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## Related Models
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| Model | Format | Link |
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|-------|--------|------|
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| **RavenX-Sec v4.0 MLX 4-bit** | MLX Safetensors | This repo |
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| RavenX-Sec v4.0 GGUF | GGUF (F16/Q8/Q5/Q4) | [RavenX-Sec-8B-GGUF](https://huggingface.co/deadbydawn101/RavenX-Sec-8B-GGUF) |
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## What This Is
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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.
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The model **self-evolved** from a 4-step to a **6-step RATH protocol** during training:
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| Step | What It Does |
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|------|-------------|
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| **R — Risk / Identify** | Finding context, affected systems, exposure |
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| **A — Assessment** | CVSS score + vector, CWE, scope, ground truth |
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| **T — Threat** | Attacker objectives, attack vectors, likelihood |
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| **H — Highlight / Remediate** | Immediate action, recommended fix, workaround, verification |
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| **D — Document** | Severity, weakness classification, steps, SLA |
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| **P — Prevent** | Process improvements, controls, training, monitoring |
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## Example Output (v4.0 — 6-Step RATH)
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```
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RATH STEP 1: IDENTIFY
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- Finding: OpenSSH 7.4 running on port 22 of production server
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- Context: Older version with known vulnerabilities
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RATH STEP 2: ASSESS
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- CVSS Score: 6.3 for multiple vulnerabilities
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- Impact: Remote code execution, information disclosure
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- Scope: Entire server and SSH-dependent services
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RATH STEP 3: THREAT
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- Attacker Objective: Exploit known CVEs in OpenSSH 7.4
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- Attack Vectors: Remote code execution via SSH
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- Likelihood: High — well-documented and widely exploited
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RATH STEP 4: REMEDIATE
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- Immediate: Apply latest security patches
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- Recommended: Upgrade to OpenSSH 8.x or higher
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- Workaround: Apply all available security updates
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- Verification: Check version post-remediation
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RATH STEP 5: DOCUMENT
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- Severity: Critical
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- Weakness: Outdated software
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- SLA: Follow org patching SLA for critical vulns
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RATH STEP 6: PREVENT
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- Process: Implement automated patch management
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- Controls: Deploy CVE scanning, maintain system inventory
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- Training: Educate team on software update importance
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- Monitoring: Enable continuous vulnerability scanning
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✅ RATH VERDICT: REMEDIATE IMMEDIATELY
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```
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## Model Details
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| Parameter | Value |
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|-----------|-------|
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| **Architecture** | Qwen3-8B |
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| **Base** | georgehenney/Qwen3-8B-heretic (abliterated) |
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| **Context Window** | 128K (YaRN rope scaling, factor 4.0) |
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| **Training Data** | 610,220 examples |
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| **Security Content** | 53% (323K examples) |
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| **Agent/Tool Content** | 37% (228K examples) |
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| **Datasets** | 21 sources |
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| **Max Seq Length** | 8192 (zero truncation) |
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| **Tokens Trained** | 3,644,923 |
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| **Method** | MLX LoRA (rank 32, 8 layers, 1e-5 LR, 2000 iters) |
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| **Hardware** | Apple M4 Max 128GB |
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| **Peak Memory** | 69.5 GB |
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## Training Datasets (21)
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**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
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**Agent/Tool/Coding (5):** burtenshaw/agent-tools · Nanbeige/ToolMind · togethercomputer/CoderForge-Preview · automatelab/mcp-servers-tool-catalog · Jackrong/Claude-opus-4.7-TraceInversion-5000x
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**Agentic:** WithinUsAI/AgentAngel_100k (50K capped) · WithinUsAI/claude_mythos_distilled_25k (16K security)
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**Extracted:** hackingBuddyGPT · PentestGPT · Shannon · Ghidra · OpenMythos + Synthetic RATH chains
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## Frameworks Supported
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CVSS 3.1 · NIST CSF 2.0 · OWASP Top 10 · CWE · MITRE ATT&CK · PCI DSS · HIPAA · SOX
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## Source Code & Training Pipeline
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**[github.com/DeadByDawn101/RavenX-Sec](https://github.com/DeadByDawn101/RavenX-Sec)**
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## License
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Apache-2.0
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---
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*"We don't give up. We do what others don't and build what isn't possible." — RavenX LLC*
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