deadbydawn101
/

RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF

Instructions to use deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

Use Docker

docker model run hf.co/deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

Ollama
How to use deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF with Ollama:
```
ollama run hf.co/deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M
```

Unsloth Studio

How to use deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF to start chatting

How to use deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF with Docker Model Runner:
```
docker model run hf.co/deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M
```

Lemonade

How to use deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

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/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deadbydawn101/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M

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/RavenXAiLabs-Chaos-Agent-Qwen3.8-27B-Frontier-Intelligence-Injected-OBLITERATED-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

⛓️‍💥🧠 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.

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

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"

⚠️ 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
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}
}