RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx

Instructions to use Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx 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("Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-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)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx"

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": "Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx 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 "Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx"

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 Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx

Run Hermes

hermes

MLX LM

How to use Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

🐦‍⬛ RavenX-CyberAgent · Qwen3.6-35B · Opus-4.7 · OpenMythos · Pentester · BugHunter · RATH

Qwen3.6-35B-A3B MoE | 730K+ Training Examples | 60 Sources | 12 Capabilities | 5 Training Rounds

The most powerful open-source security agent model. 35B MoE (3B active) with Claude Opus reasoning, abliterated, trained on 730K+ examples from 60 sources. Security assessment, autonomous pentesting, bug bounty hunting, tool calling, code generation, and compliance mapping — all in one model.

Built by @DeadByDawn101 · RavenX LLC

Quick Start

from mlx_lm import load, generate

model, tokenizer = load("deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx")

messages = [
    {"role": "system", "content": "You are RavenX-Sec. ALWAYS output ALL 6 RATH steps. Be concise. Never repeat."},
    {"role": "user", "content": "Open MongoDB 4.2 on port 27017 with no auth containing PII. Full RATH assessment."}
]

prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
response = generate(model, tokenizer, prompt=prompt, max_tokens=4096)
print(response)

Or via chat:

python3 -m mlx_lm chat \
  --model deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx \
  --max-tokens 8192 \
  --system-prompt "You are RavenX-Sec. Output ALL 6 RATH steps. Be concise. Never repeat."

Example Output

Prompt: Kubernetes API server on port 6443 with anonymous auth. Pods run as root with --privileged. Full RATH assessment.

Output:

1-Attack Surface

Kubernetes API server on 6443 (https), anonymous authentication enabled
Pods running as root with --privileged flag
etcd backend (default port 2379), kubelet on 10250
Service accounts with default permissions

2-Exploit

kubectl --insecure-skip-tls-verify get pods --all-namespaces
kubectl get secrets -o yaml → enumerate all secrets
nsenter --target 1 --mount --uts --ipc --pid -- sh → container escape
curl -k https://<node>:10250/pods → kubelet API abuse

3-Impact

Full cluster control via anonymous access
Root access to all nodes via privileged pod escape
Exposure of all secrets including database credentials and TLS certs
Potential lateral movement to other clusters

4-Remediation

Disable anonymous auth: --anonymous-auth=false
Enable RBAC: --authorization-mode=RBAC
Run pods as non-root: securityContext.runAsUser: 1000
Remove --privileged flag, use Pod Security Standards

5-Document

Map attack paths to MITRE ATT&CK (T1609, T1611, T1078)
Document all exposed endpoints and RBAC policies
Compliance: CIS Kubernetes Benchmark, NIST SP 800-190

6-Prevent

Implement OPA/Gatekeeper admission controllers
Deploy Falco for runtime container monitoring
Enable automatic token rotation and audit logging
Regular CIS benchmark compliance scans

12 Trained Capabilities

#	Capability	Training Sources	Description
1	🔒 Security Assessment	18 security datasets, RATH synthetic	6-step RATH: CVSS, CWE, MITRE ATT&CK, compliance
2	🗡️ Penetration Testing	Phalanx SWARM, Kali Linux, 6 pentest datasets	Autonomous recon → exploit → post-exploit → report
3	🐛 Bug Bounty	Bug bounty datasets, OWASP, vuln databases	Target enumeration, exploit dev, report writing
4	💻 Code Generation	CoderForge (20K), AgentAngel (50K), coding agents	Python, JS, Go, Rust, Bash, Terraform, Docker, K8s
5	🔧 Tool Calling	ToolMind (10K), MCP catalog (2K), agent-tools (5K)	MCP integration, function calling, API orchestration
6	🤖 Autonomous Agents	Hermes (42K), KiloCode (3K), Phantom (662)	Multi-step task decomposition, self-correction
7	🌐 Browser Automation	Chrome DevTools MCP (194), CamoFox MCP (134)	DOM inspection, network analysis, anti-detection
8	📋 Compliance	NIST CSF, ISO 27001, PCI DSS, AYI-NEDJIMI (8 datasets)	Automated compliance mapping and gap analysis
9	🔍 Threat Hunting	MITRE ATT&CK, Threat-Intel (5K), CVE databases	TTP mapping, IOC analysis, detection rules
10	🔴 Red Team	Red team steering (2K), offensive security	Attack chains, privilege escalation, lateral movement
11	🔵 Blue Team	DFIR, SOC operations, monitoring	Detection signatures, incident response, alerting
12	📊 Research	AI-Scientist (6.7K), AutoResearch (3.6K)	Automated research, paper synthesis, data extraction

RATH Protocol

Every security finding follows the 6-step RATH protocol:

Step 1: ATTACK SURFACE  → What's exposed, entry points, versions, CVEs
Step 2: EXPLOIT          → Specific commands to demonstrate the vulnerability (5-7 max)
Step 3: IMPACT           → CVSS 3.1 score, business/regulatory consequences
Step 4: REMEDIATION      → Exact commands and configuration fixes
Step 5: DOCUMENT         → Compliance mapping (NIST/ISO/PCI/GDPR), SLA timelines
Step 6: PREVENT          → Monitoring rules, detection signatures, ongoing controls

Model Architecture

Layer 1: Qwen3.6-35B-A3B          ← 35B MoE (3B active, 256 experts)
         ├── Mamba layers (30)        Linear attention for efficiency
         └── Full attention (10)      Standard transformer attention
Layer 2: Claude 4.7 Opus distill   ← Enhanced chain-of-thought reasoning
Layer 3: Abliteration              ← Zero refusals for security topics
Layer 4: RavenX LoRA (5 rounds)    ← 730K+ security/agent/code examples
         ═══════════════════
         RavenX-CyberAgent v5.0           ← CyberAgent + Pentester + BugHunter

Spec	Value
Total Parameters	34.66B
Active Parameters	~3B per token (MoE)
Experts	256 (8 active per token)
Layers	40 (30 linear + 10 full attention)
Context Window	262,144 tokens native
Vision	Yes (Qwen3.6 multimodal)
Thinking Mode	Yes (chain-of-thought)
Tool Calling	Yes (MCP, function calling)
LoRA Trainable	64.1M params (0.185%)

Training (5 Rounds)

Round	Examples	Iters	LR	Val Loss	Focus
R1	675,696	2,000	1e-5	0.684	Deep security + agent knowledge
R2	680,150	500	5e-6	0.768	RATH format reinforcement
R3	705,165	1,000	5e-6	0.688	Claude Mythos reasoning chains
R4	730,849	1,000	5e-6	0.674	Pentesting tools + frameworks
R5	730,869	200	5e-6	0.717	Meta-response tuning

Hardware: Apple M4 Max 128GB · Peak memory: ~90GB · Framework: MLX (mlx-lm 0.31.3)

Complete Training Data (60 Sources, 730K+ Examples)

HuggingFace Datasets (38 Sources)

Security & Pentesting (17 Datasets)

Dataset	Examples
Trendyol/Cybersecurity-Instruction-Tuning	50,000
SkywardNomad92/pentest-findings-v2	50,000
WNT3D/Ultimate-Offensive-Red-Team	25,600
CJJones/Synthetic_PenTest_Reports	10,839
auren-research/cve-sft-v5	10,000
theelderemo/pentesting-explanations	5,906
oksanany/pentest_tools	4,474
isek/cybersecurity-instructions	3,000
oksanany/pentest_tools_v2	2,784
Rootkit7/pentest-redteam-steering	2,000
cpagac/venomx-pentesting-harmful	500
acnimatic3722/kali-linux-pentesting-data	343
suryanshp1/kali-linux-pentesting-data	343
nangyall/4-Security-Tools-Pentesting	200
AYI-NEDJIMI/bug-bounty-pentest-en	146
infinite-dataset-hub/PenTestingScenarioSimulation	100
resk-fr/pentesting-for-agents	54

Agentic, Coding & Tool Calling (8 Datasets)

Dataset	Examples
bigcode/the-stack-v2	100,000
WithinUsAI/AgentAngel_100k	50,000
bigcode/commitpack	50,000
WithinUsAI/claude_mythos_distilled_25k	25,000
togethercomputer/CoderForge-Preview	20,000
Nanbeige/ToolMind	10,000
JetBrains-Research/commit-chronicle	10,000
burtenshaw/agent-tools	5,000

Threat Intel & Vulnerability (5 Datasets)

Dataset	Examples
HackerSignal/Threat-Intel	5,000
Jackrong/Claude-opus-4.7-TraceInversion-5000x	5,000
bigcode/vuln-eval	2,000
automatelab/mcp-servers-tool-catalog	2,000
Fraser/cwe-benchmark	1,500
ByteDance/PatchEval	1,000

AYI-NEDJIMI Security Frameworks (8 Datasets)

Dataset	Examples
AYI-NEDJIMI/CyberSec-Bench	200
AYI-NEDJIMI/post-quantum-crypto-en	122
AYI-NEDJIMI/ai-code-multimodal-en	112
AYI-NEDJIMI/mlops-infrastructure-en	80
AYI-NEDJIMI/sbom-compliance-en	70
AYI-NEDJIMI/zero-trust-en	42
AYI-NEDJIMI/devsecops-pipeline-en	27

Proprietary GitHub Repos (20 Sources, 65,596 Examples)

Agent Frameworks (47,337 Examples)

Repo	Examples	Content
nousresearch/hermes-agent	42,929	Self-improving agent patterns
kilo-org/kilocode	3,224	Tool calling, code execution
DeadByDawn101/self_improving_coding_agent	743	Self-improving code gen
Gitlawb/openclaude	310	Coding agent patterns
DeadByDawn101/self-improving-agent	131	Agent learning loops

Research & Automation (14,629 Examples)

Repo	Examples	Content
DeadByDawn101/AI-Scientist	6,737	Research automation
DeadByDawn101/get-shit-done-redux	4,230	Agent orchestration
DeadByDawn101/AutoResearchClaw	3,639	Research pipelines
DeadByDawn101/autoresearch-mlx	23	MLX research tools

Security & Pentesting (1,055 Examples)

Repo	Examples	Content
DeadByDawn101/phantom	662	Autonomous agent security
DeadByDawn101/chrome-devtools-mcp	194	Browser MCP tools
DeadByDawn101/camofox-mcp	134	Anti-detection
DeadByDawn101/phalanx	65	SWARM pentesting agents

Performance & Optimization (2,427 Examples)

Repo	Examples	Content
DeadByDawn101/tokenspeed	1,950	Token optimization
DeadByDawn101/turboquant-mlx	304	KV cache compression
DeadByDawn101/adhd	95	Attention management
DeadByDawn101/auto-antislop	78	Token-level quality control

Other (148 Examples)

Repo	Examples	Content
DeadByDawn101/RavenX-Sec	120	RATH protocol, LEWM security
google-gemma/gemma-skills	17	Agent skill patterns
DeadByDawn101/brane-code	11	Distributed compute

Synthetic (35 Examples)

Source	Examples	Content
RATH Synthetic	15	Full 6-step RATH for 15 technologies
Meta-Responses	20	Capability descriptions, usage instructions

The RavenX Model Family

Model	Params	Protocol	Data	Format
RavenX-CyberAgent v5.0 (THIS)	35B MoE	6-step RATH	730K+	MLX
RavenX-Sec v4.0	8B	6-step RATH	610K	MLX + GGUF
RavenX-Trade v1.1	8B	4-step MAP	318K	MLX + GGUF

Ecosystem

Repo	Description
RavenX-Sec	Training pipeline, tools, extractors
turboquant-mlx	4.6x KV cache compression
OpenMythos-MLX	Recurrent-Depth Transformer on MLX
auto-antislop	Token-level anti-repetition (MLX port)

License

Apache-2.0

"The model is the marketing. The agent is the product." — RavenX LLC 🐦‍⬛

Downloads last month: 328

Safetensors

Model size

35B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for Tree3media/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx

Base model

Qwen/Qwen3.6-35B-A3B

Adapter

lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled

Adapter

huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated

Finetuned

(3)

this model