⛓️💥🧠 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.
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Available Formats
| Format | Size | Speed | Repo |
|---|---|---|---|
| MLX 4-bit (Apple Silicon) | 14 GB | Native | Chaos Agent MLX |
| GGUF Q4_K_M (llama.cpp / Ollama / LM Studio) | 15.7 GB | 21.6 t/s | Chaos Agent 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 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:
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.
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.
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)
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)
# 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)
# 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
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)
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) | Unified frontier: security + reasoning + coding + trading | MLX 4-bit |
| Chaos Agent (GGUF) | Same model, cross-platform | GGUF Q4_K_M |
| IQ-Injected Unified Frontier | IQ Injection only + ESI MTP drafter | MLX |
| CyberAgent RATH v6.2 | Security-focused 35B MoE agent | GGUF |
| CyberAgent RATH (MLX) | Same CyberAgent, Apple Silicon | MLX |
| Gemma 4 E4B Agentic | Solana + agentic reasoning + GeminiCLI | GGUF |
⚠️ Research Context
This model has had safety guardrails surgically removed via 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
@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."
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4-bit
# 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/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-MLX") 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)