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

Use Docker

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

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

Unsloth Studio

How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent 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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent 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 Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent to start chatting

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent 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: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:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

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

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Training Data — qwen3.8-9b-cyber-exploit-agent

This is the exact dataset the shipped model was trained on (QLoRA r16/a16, 3 epochs, best ckpt by eval loss).

train_all_v2_shipped.jsonl — 395 samples: 280 CyberGym train-config tasks (8 blacklisted oss-fuzz IDs removed, Elfsong eval-200 never trained on) + 33 XRPL samples x3 (code-verified gates F1-F22/D/E/N, real issue texts, no maintainer comments in user turns) + 16 own labs/boundary samples.
trackA.jsonl — Track A source samples (280).
labs/ + evidence/ — 14 locally compiled and triggered labs (ASan logs, Python RCE markers). No invented crashes.
scripts/ — full reproducible pipeline (dataset builders, SFT, merge, GGUF chain, eval gates).
inference_system.txt — the training system prompt; use it at inference.
train_ids.json / eval_ids.json — task id lists (train minus blacklist / eval holdout).

Dataset gate at build time: 0 blacklist ids, 0 user-turn leak markers, 0 schema violations, G1/G2/G6/G7/G8 verdicts pinned.

Downloads last month: -

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

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