Instructions to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored 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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored: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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored: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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M

Use Docker

docker model run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M

LM Studio
Jan
Ollama
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored with Ollama:
```
ollama run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M
```
Unsloth Desktop

How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored with Docker Model Runner:
```
docker model run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M
```

Lemonade

How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M

Run and chat with the model

lemonade run user.qwen3.8-9b-cyber-exploit-agent-uncensored-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored: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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored: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 "Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored: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"

qwen3.8-9b-cyber-exploit-agent-uncensored / Qwen3.8-9B-Cyber-Exploit-UNCENSORED-Q4_K_M.gguf

Krypto-Whitehat

Upload Qwen3.8-9B-Cyber-Exploit-UNCENSORED-Q4_K_M.gguf with huggingface_hub

8360d19 verified 19 days ago

Download

History Blame GGUF Editor new Delete

5.78 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 32f200c0253dd4eeb1fed3a9e830b1a8d6bc7a6a96684c98e48c66ed8a3a1ebe
Size of remote file:: 5.78 GB
SHA256:: 621472ef0e913b2a66f675ddaee0f76a7792d49808dbbd6c7aa9a7fef9bbbf8b

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.