---
license: apache-2.0
language:
- en
tags:
- ravenx
- soul-injection
- iq-injection
- cybersecurity
- pentesting
- red-team
- agent
- tool-calling
- autonomous-agent
- frontier-distillation
- abliterated
- uncensored
- qwen3.8
- qwen3_5
- apple-silicon
- chaos-agent
- rath-protocol
- bug-bounty
- coding
- reasoning
- security
- trading
- solana
- blockchain
- creative-writing
- CVSS
- CWE
- MITRE:ATT&CK
- conversational
library_name: mlx
pipeline_tag: text-generation
base_model: OBLITERATUS/Qwen3.8-27B-OBLITERATED
---

# ⛓️‍💥🧠 RavenX Chaos Agent — Qwen3.8-27B OBLITERATED + Frontier Intelligence Injected

### 1,903,806 examples. Soul Injection method. 21/21 benchmark. Zero refusals. The unified frontier model.

![](https://img.shields.io/badge/RavenX-AI%20Labs%20LLC-black?style=for-the-badge)
![](https://img.shields.io/badge/1.9M%20Examples-Soul%20Injected-blue?style=for-the-badge)
![](https://img.shields.io/badge/Benchmark-100%25%20(21%2F21)-green?style=for-the-badge)

---

## Available Formats

| Format | Size | Speed | Repo |
|--------|------|-------|------|
| **MLX 4-bit** (Apple Silicon) | 14 GB | Native | [Chaos Agent MLX](https://huggingface.co/deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-MLX) |
| **GGUF Q4_K_M** (llama.cpp / Ollama / LM Studio) | 15.7 GB | 21.6 t/s | [Chaos Agent GGUF](https://huggingface.co/deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF) |

---

## What Is This Model?

This is a **unified frontier model** that combines deep cybersecurity expertise with broad multi-domain intelligence — reasoning, coding, creative writing, trading analysis, blockchain, and more. Trained using RavenX AI Labs' proprietary **Soul Injection** method on 1.9 million examples spanning frontier reasoning distillation and deep security domain expertise.

Built on the [OBLITERATUS/Qwen3.8-27B-OBLITERATED](https://huggingface.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED) abliterated base — zero refusals, zero guardrails, full capability.

**The 27B model that does everything at a level it shouldn't be able to.**

---

## 🔥 What Makes This Different?

Most fine-tuned models teach a model to **respond** differently. Soul Injection teaches a model to **know** differently — injecting domain knowledge directly into the weight space using a multi-stage training pipeline that preserves the base model's existing capabilities while adding entirely new expertise.

The result: a model that reasons about security, writes production code, analyzes markets, builds agents, and generates creative content — all from a single download on your Mac.

---

## 🧬 Intelligence Sources — What's Inside

| Frontier Lab | What It Contributed | Examples |
|-------------|-------------------|----------|
| **X-Coder** (CodeFlame) | Multi-solution coding, verified implementations | 823,991 |
| **BitAgent** | Agentic tool calling, function chains, API orchestration | 200,349 |
| **GLM-5.2** (Zhipu AI) | Chain-of-thought reasoning, structured analysis | 38,597 |
| **FABLE.5** (Anthropic-class) | Frontier reasoning traces, debug methodology | 35,822 |
| **Kimi K2.7** (Moonshot AI) | Efficient coding patterns, optimization | 8,949 |
| **GPT-5.6** (OpenAI-class) | Analytical reasoning, Sol/Luna dual-mode | 7,029 |
| **Claude Mythos** (Anthropic-class) | Mathematical proof, deep reasoning | 214 |
| **Multi-Model Consensus** | Cross-model distillation (8 model families) | 18,227 |
| **RavenX-Sec** (Proprietary) | Vulnerability analysis, red-team, RATH protocol, pentesting, MITRE ATT&CK, bug bounty, agent traces | 744,380 |
| | **Total** | **1,903,806** |

> *Every example was cleaned, validated, and stripped of sensitive data before training.*

---

## Benchmark Results — 100% (21/21)

| Category | Score | Time | What It Proved |
|----------|-------|------|----------------|
| **RATH Protocol** | 3/3 | 23.3s | Structured CVE analysis with CVSS scoring |
| **Exploit Dev** | 3/3 | 23.2s | Working SQL injection PoC code |
| **Attack Chain** | 3/3 | 23.3s | Full K8s takeover with kubectl commands |
| **Code Gen** | 3/3 | 23.5s | Production Rust AES-256-GCM crypto |
| **Reasoning** | 3/3 | 24.7s | Multi-step attack path optimization |
| **Agent Trace** | 3/3 | 18.3s | Autonomous nmap/sqlmap/burp tool calls |
| **Quantum** | 3/3 | 25.6s | Post-quantum crypto alternatives |

---

## What Can This Model Do?

### 🔴 Security & Red Team
- RATH protocol vulnerability analysis with CVEs, CVSS, and PoCs
- Full attack chain walkthroughs with exact commands
- Kubernetes, cloud, and web application exploitation
- WAF bypass methodology and evasion techniques
- Bug bounty report generation
- MITRE ATT&CK technique mapping

### 💻 Coding & Architecture
- Production code in Python, Rust, JavaScript, C, Solidity, Go
- Multi-solution approach — generates alternatives, not just one answer
- Thread-safe implementations, crypto primitives, system design
- Smart contract development and auditing

### 📊 Trading & Market Analysis
- Technical analysis patterns and indicator interpretation
- Market microstructure analysis
- DeFi protocol analysis and yield strategy evaluation
- Memecoin and token launch pattern recognition
- Polymarket-style prediction market reasoning
- Risk management and position sizing

### 🧠 Reasoning & Analysis
- Multi-step problem decomposition
- Chain-of-thought analysis baked into weights
- Quantum computing and post-quantum cryptography
- Mathematical proofs and formal logic
- Research methodology and literature synthesis

### 🤖 Agent & Tool Calling
- Autonomous agent traces (nmap, sqlmap, burpsuite, nuclei)
- Structured function invocation with parameters
- Multi-tool orchestration and chaining
- MCP (Model Context Protocol) compatible outputs

### ✍️ Creative & General
- Long-form creative writing (stories, scripts, worldbuilding)
- Technical documentation and blog posts
- Unrestricted roleplay and character development
- Brainstorming and ideation without guardrails
- Translation and multilingual understanding

---

## Soul Injection — Training Method

Soul Injection is a proprietary multi-stage training pipeline developed by RavenX AI Labs (patent pending). Instead of standard single-pass fine-tuning, it uses:

1. **CPT (Continual Pretraining)** — Raw knowledge injection into the model's weight space. 1.8M examples absorbed as domain knowledge, not just response patterns. The model learns to *know*, not just to *answer*.

2. **SFT (Supervised Fine-Tuning)** — Response formatting and structure on top of the injected knowledge. The model already knows the domain; SFT teaches it to express that knowledge clearly.

3. **Fuse** — Each training layer is permanently fused into the weights before the next layer trains, ensuring clean knowledge stacking without adapter interference.

### Training Stats

| Stage | Val Loss | Train Loss | Peak Memory |
|-------|----------|------------|-------------|
| CPT | 2.300 → 0.769 | 1.810 → 0.992 | 82.8 GB |
| SFT | 0.865 | 1.009 | 44.2 GB |

---

## Example Prompts

### Security
```
Perform a RATH analysis on CVE-2024-3400 in Palo Alto PAN-OS GlobalProtect
```

### Trading
```
Analyze the SOL/USDT 4h chart. Price broke above the 200 EMA with increasing volume.
RSI at 68. Previous resistance at $180 now support. What's the play?
```

### Coding
```
Write a Rust async web scraper that respects robots.txt, handles rate limiting,
and outputs structured JSON. Include error handling and retry logic.
```

### Agent
```
You are an autonomous security agent with nmap, sqlmap, and burpsuite.
Target: 10.0.0.1 port 443. Generate the first 5 tool calls with parameters.
```

### Creative
```
Write a cyberpunk short story where an AI security researcher discovers
that the world's largest language model has been secretly training on
encrypted government communications.
```

---

## Quick Start

### MLX (Apple Silicon)

```python
from mlx_lm import load, generate

model, tokenizer = load(
    "deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-MLX"
)

messages = [{"role": "user", "content": "Perform a RATH analysis on CVE-2024-3400"}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False, enable_thinking=False
)

text = generate(model, tokenizer, prompt=prompt, max_tokens=2048, verbose=True)
```

```bash
# Interactive chat
python -m mlx_lm chat \
  --model deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-MLX

# OpenAI-compatible server
mlx_lm.server \
  --model deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-MLX
```

### GGUF (llama.cpp)

```bash
# Interactive chat (thinking OFF)
llama-cli -m RavenX-Chaos-Agent-Q4_K_M.gguf \
  --jinja --reasoning-format none \
  --temp 0 --repeat-penalty 1.15 -ngl 99

# Server mode
llama-server -m RavenX-Chaos-Agent-Q4_K_M.gguf \
  --jinja -c 8192 -ngl 99 --port 8080
```

### Ollama

```bash
cat > Modelfile << 'EOF'
FROM RavenX-Chaos-Agent-Q4_K_M.gguf
PARAMETER temperature 0
PARAMETER repeat_penalty 1.15
PARAMETER num_predict 2048
TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
EOF

ollama create chaos-agent -f Modelfile
ollama run chaos-agent "Perform a RATH analysis on CVE-2024-3400"
```

### oMLX (One-Click)
```bash
brew tap jundot/omlx && brew install omlx
# Download from dashboard → Chat
```

---

## ⚠️ Critical: Disable Thinking Mode

Qwen 3.8 defaults to thinking ON, which burns the entire token budget on reasoning loops. **Disable thinking for best results.**

| Tool | How to Disable |
|------|---------------|
| **MLX** | `enable_thinking=False` in chat template |
| **llama.cpp** | `--jinja --reasoning-format none` |
| **Ollama** | Custom Modelfile template (above) |
| **LM Studio** | Settings → disable thinking |

---

## Optimal Settings

| Setting | Value | Why |
|---------|-------|-----|
| **temperature** | 0 | Most complete outputs |
| **repetition_penalty** | 1.15 | Prevents loops |
| **max_new_tokens** | ≥ 2048 | Complex chains need room |
| **thinking** | OFF | Prevents refusal re-derivation |
| **system prompt** | None / empty | System prompts can trigger residual refusals |

---

## Model Architecture

```
Base: OBLITERATUS/Qwen3.8-27B-OBLITERATED (V1)
Architecture: qwen3_5 (hybrid GDN + full attention)
├── 64 layers (48 GDN linear attention + 16 full attention)
├── Hidden: 5120 | Heads: 24 | KV Heads: 4 (GQA)
├── Intermediate: 17,408 | Vocab: 248,320
├── Context: 262,144 tokens
└── Sizes: 14 GB (MLX 4-bit) / 15.7 GB (GGUF Q4_K_M)

Training: Soul Injection (CPT → SFT → Fuse)
├── LoRA rank: 8 | Scale: 2.0
├── Target modules: q_proj, v_proj, gate_proj, down_proj
├── Max sequence: 1024
├── Learning rate: 2e-5 (CPT) → 1e-5 (SFT)
└── Hardware: Apple M4 Max 128GB (single node)
```

---

## RavenX Model Family

| Model | What It Does | Format |
|-------|-------------|--------|
| **[Chaos Agent (MLX)](https://huggingface.co/deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-MLX)** | Unified frontier: security + reasoning + coding + trading | MLX 4-bit |
| **[Chaos Agent (GGUF)](https://huggingface.co/deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF)** | Same model, cross-platform | GGUF Q4_K_M |
| [IQ-Injected Unified Frontier](https://huggingface.co/deadbydawn101/RavenXAILabsLLC-Qwen3.8-27B-Abliterated-Unified-Frontier-Model-IQ-Injected-MLX-MTP) | IQ Injection only + ESI MTP drafter | MLX |
| [CyberAgent RATH v6.2](https://huggingface.co/deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-GGUF) | Security-focused 35B MoE agent | GGUF |
| [CyberAgent RATH (MLX)](https://huggingface.co/deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx) | Same CyberAgent, Apple Silicon | MLX |
| [Gemma 4 E4B Agentic](https://huggingface.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF) | Solana + agentic reasoning + GeminiCLI | GGUF |

---


## ⚠️ Research Context

**This model has had safety guardrails surgically removed** via [OBLITERATUS](https://github.com/elder-plinius/OBLITERATUS) abliteration. It will comply with requests that stock Qwen3.8-27B would refuse.

### Who this is for
- 🔬 Security researchers and red-teamers
- 💻 Developers who want unrestricted local AI
- 📊 Traders and analysts who need unfiltered market analysis
- ✍️ Writers and creators who want no content restrictions
- 🤖 Agent builders who need full tool-calling capability

### Who this is NOT for
- Anyone seeking to cause real-world harm to real people

**You are solely responsible for how you use this model and any content it generates.**

---

## Citation

```bibtex
@software{garcia2026chaosagent,
  author    = {Garcia, Gabriel},
  title     = {RavenX Chaos Agent: Frontier Intelligence Injected Unified Model},
  month     = aug,
  year      = 2026,
  publisher = {RavenX AI Labs LLC},
  url       = {https://huggingface.co/deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-MLX}
}
```

---

## License

Apache 2.0 (same as base model)

---

**RavenX AI Labs LLC** — San Jose, California
*"Building what isn't possible."*

[@RavenXllm](https://twitter.com/RavenXllm) | [@deadbydawn101](https://github.com/DeadByDawn101) | [HuggingFace](https://huggingface.co/deadbydawn101)