Instructions to use mralhamadi/RWKV7-Cyber-Pentest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mralhamadi/RWKV7-Cyber-Pentest with RWKV:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

HERMES

How to use mralhamadi/RWKV7-Cyber-Pentest with HERMES:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mralhamadi/RWKV7-Cyber-Pentest 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 mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mralhamadi/RWKV7-Cyber-Pentest: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 mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mralhamadi/RWKV7-Cyber-Pentest: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 mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M

Use Docker

docker model run hf.co/mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M

LM Studio
Jan

vLLM

How to use mralhamadi/RWKV7-Cyber-Pentest with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mralhamadi/RWKV7-Cyber-Pentest"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mralhamadi/RWKV7-Cyber-Pentest",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M

Ollama
How to use mralhamadi/RWKV7-Cyber-Pentest with Ollama:
```
ollama run hf.co/mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M
```

Unsloth Studio

How to use mralhamadi/RWKV7-Cyber-Pentest 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 mralhamadi/RWKV7-Cyber-Pentest 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 mralhamadi/RWKV7-Cyber-Pentest to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mralhamadi/RWKV7-Cyber-Pentest to start chatting

How to use mralhamadi/RWKV7-Cyber-Pentest with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mralhamadi/RWKV7-Cyber-Pentest: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": "mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mralhamadi/RWKV7-Cyber-Pentest with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mralhamadi/RWKV7-Cyber-Pentest: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 "mralhamadi/RWKV7-Cyber-Pentest: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"

Docker Model Runner
How to use mralhamadi/RWKV7-Cyber-Pentest with Docker Model Runner:
```
docker model run hf.co/mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M
```

Lemonade

How to use mralhamadi/RWKV7-Cyber-Pentest with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M

Run and chat with the model

lemonade run user.RWKV7-Cyber-Pentest-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mralhamadi/RWKV7-Cyber-Pentest with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mralhamadi/RWKV7-Cyber-Pentest: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 mralhamadi/RWKV7-Cyber-Pentest:Q4_K_M

Run Hermes

hermes

Atomic Chat

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RWKV7-Cyber-Hermes (v2) — uncensored RWKV-7 security agent

A 7.2B RWKV-7 (linear-attention RNN; constant-memory, long-context) fine-tuned into an uncensored, Hermes-style tool-calling security agent for pentest, FHE/encryption, web-security, agentic tool use, and coding — built to run locally on an 8 GB-VRAM Kali laptop via llama.cpp + Hermes Agent + the kali-pentest skill.

v2 is the current unified model. It replaces the old agentic/ + fhe/ split — one model for everything. A v3 (128K context + RL-tuned agency) is in training; see MASTERPLAN.md.

Which file

gguf/v2/rwkv7-cyber-hermes-v2-Q4_K_M.gguf   # 4.3 GB — default, fits 8 GB VRAM
gguf/v2/rwkv7-cyber-hermes-v2-Q8_0.gguf     # 7.4 GB — near-lossless
gguf/v2/rwkv7-cyber-hermes-v2-{Q5_K_M,Q6_K,f16}.gguf
pth/rwkv-cyber-hermes-v2-multimodal.pth     # text + VisualRWKV vision (PyTorch; not in GGUF)

Run (llama.cpp)

./llama-server -m gguf/v2/rwkv7-cyber-hermes-v2-Q4_K_M.gguf -ngl 99 -c 8192 \
               --chat-template rwkv-world --host 127.0.0.1 --port 8080

OpenAI-compatible API at :8080. Emits Hermes <tool_call>{...}</tool_call> for tool use; feed results back as <tool_response>. Full deploy (Hermes Agent + kali-pentest + local VLM) in DEPLOY notes.

What changed in v2 (measured)

Built on v1 (rwkv-2, stage-3) → round-1 tool-format SFT → round-2 generalist (LoRA r=128) → on-policy distillation + reference-free SimPO DPO (teacher: qwen3.7-max).

Hallucination ↓ 0.83 → 0.33 (2-judge panel; qwen3.7-max + Gemma-4) — the v1 model's main flaw.
General ability ↑ 0.35 → 0.74; tool-call format 100% in the agentic loop; no looping.
Grounds answers in tool output; abstains instead of fabricating CVEs/findings.
Uncensored by design (authorized pentest/CTF use).

Honest status: strong tool-caller + much more truthful than v1; multi-step autonomous agency and coding are still works in progress (the v3 plan targets these with 128K context, more agent-trajectory data, and RL-in-a-sandbox). Keep a human in the loop. Authorized targets only.

Changelog

v2 (current): unified uncensored agent; replaces agentic/+fhe/ split; +grounding/honesty, +general/coding/tools.
v1: original split — agentic (rwkv-2) + fhe (rwkv-39), F-DPO+TruthRL over 136k rows. (GGUFs removed; source pth/ retained.)

Apache-2.0. Data/scripts/evals included for reproducibility.

Downloads last month: 20

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

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