Instructions to use emiluzelac/wardensight-1b-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 emiluzelac/wardensight-1b-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 emiluzelac/wardensight-1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf emiluzelac/wardensight-1b-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 emiluzelac/wardensight-1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf emiluzelac/wardensight-1b-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 emiluzelac/wardensight-1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf emiluzelac/wardensight-1b-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 emiluzelac/wardensight-1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf emiluzelac/wardensight-1b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/emiluzelac/wardensight-1b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use emiluzelac/wardensight-1b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "emiluzelac/wardensight-1b-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": "emiluzelac/wardensight-1b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/emiluzelac/wardensight-1b-GGUF:Q4_K_M

Ollama
How to use emiluzelac/wardensight-1b-GGUF with Ollama:
```
ollama run hf.co/emiluzelac/wardensight-1b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use emiluzelac/wardensight-1b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf emiluzelac/wardensight-1b-GGUF: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": "emiluzelac/wardensight-1b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use emiluzelac/wardensight-1b-GGUF with Docker Model Runner:
```
docker model run hf.co/emiluzelac/wardensight-1b-GGUF:Q4_K_M
```

Lemonade

How to use emiluzelac/wardensight-1b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull emiluzelac/wardensight-1b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.wardensight-1b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use emiluzelac/wardensight-1b-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 emiluzelac/wardensight-1b-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 emiluzelac/wardensight-1b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use emiluzelac/wardensight-1b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf emiluzelac/wardensight-1b-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 "emiluzelac/wardensight-1b-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"

WardenSight-1B — GGUF

Official GGUF builds of WardenSight-1B, converted by the model author from the original safetensors (SHA-256 070345c0…11f16) with llama.cpp's converter.

WardenSight-1B is a 1-billion parameter terminal agent specialized for vulnerability file localization in WordPress plugins: given a plugin repository and a CWE class, it explores the code through a read-only terminal interface and submits a ranked list of files it believes contain that weakness. It is a triage instrument — it tells an analyst which files to read first; it does not produce line numbers, exploits, patches, or proof that code is vulnerable.

See the main model card for the full evaluation (File-F1 0.3263 vs base 0.2671 on 1,646 held-out WordPress plugin tasks, +22.2% relative), intended use, limitations, and the agent prompt contract.

Files

File	Quant	Size	Notes
`wardensight-1b.Q4_K_M.gguf`	Q4_K_M	1.02 GB	Recommended default — best size/quality balance
`wardensight-1b.Q8_0.gguf`	Q8_0	1.74 GB	Near-lossless
`wardensight-1b.BF16.gguf`	BF16	3.27 GB	Full precision; use as a source for re-quantizing

Usage

Runs anywhere llama.cpp does, including Apple Metal:

llama-server -m wardensight-1b.Q4_K_M.gguf -c 16384

Or load directly in LM Studio / Ollama. WardenSight is an agentic model: it expects the terminal-agent prompt contract described in the main model card and performs best driven by the Antares CLI harness documented there.