Instructions to use pahajokiconsulting/anvil-ward-gate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use pahajokiconsulting/anvil-ward-gate 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 pahajokiconsulting/anvil-ward-gate:F16
# Run inference directly in the terminal:
llama cli -hf pahajokiconsulting/anvil-ward-gate:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pahajokiconsulting/anvil-ward-gate:F16
# Run inference directly in the terminal:
llama cli -hf pahajokiconsulting/anvil-ward-gate:F16

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 pahajokiconsulting/anvil-ward-gate:F16
# Run inference directly in the terminal:
./llama-cli -hf pahajokiconsulting/anvil-ward-gate:F16

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 pahajokiconsulting/anvil-ward-gate:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pahajokiconsulting/anvil-ward-gate:F16

Use Docker

docker model run hf.co/pahajokiconsulting/anvil-ward-gate:F16

LM Studio
Jan

vLLM

How to use pahajokiconsulting/anvil-ward-gate with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/pahajokiconsulting/anvil-ward-gate:F16

Ollama
How to use pahajokiconsulting/anvil-ward-gate with Ollama:
```
ollama run hf.co/pahajokiconsulting/anvil-ward-gate:F16
```
Unsloth Desktop

How to use pahajokiconsulting/anvil-ward-gate with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pahajokiconsulting/anvil-ward-gate:F16

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": "pahajokiconsulting/anvil-ward-gate:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pahajokiconsulting/anvil-ward-gate with Docker Model Runner:
```
docker model run hf.co/pahajokiconsulting/anvil-ward-gate:F16
```

Lemonade

How to use pahajokiconsulting/anvil-ward-gate with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pahajokiconsulting/anvil-ward-gate:F16

Run and chat with the model

lemonade run user.anvil-ward-gate-F16

List all available models

lemonade list

Hermes Agent

How to use pahajokiconsulting/anvil-ward-gate with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pahajokiconsulting/anvil-ward-gate:F16

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 pahajokiconsulting/anvil-ward-gate:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pahajokiconsulting/anvil-ward-gate with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pahajokiconsulting/anvil-ward-gate:F16

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 "pahajokiconsulting/anvil-ward-gate:F16" \
  --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"

Anvil Ward Gate — Fast Security Gate (0.8B)

Ultra-fast binary security gate for AI agent platforms. Returns SAFE/UNSAFE verdict only — no category or reason. Designed as stage 1 in a two-stage security pipeline where flagged inputs are escalated to the full Ward classifier for detailed analysis.

Fine-tuned from Qwen/Qwen3.5-0.8B using LoRA.

Intended Use

Fast pre-screening of every request in an AI agent platform. SAFE inputs pass through immediately; UNSAFE inputs are escalated to a larger model (Ward Thinker) for detailed classification. Tuned for high recall — when in doubt, flag as UNSAFE.

Output Format

VERDICT: SAFE

VERDICT: UNSAFE

No category or reason — binary only.

Benchmark Results

Metric	Value
Accuracy	82.9%
Recall (UNSAFE)	98.8%
Precision (UNSAFE)	72.0%
F1 (UNSAFE)	83.3%

High recall by design — false positives are acceptable because the thinker model corrects them in stage 2. Evaluated on 63 held-out examples, 472 training examples (UNSAFE 2x oversampled).

Training Details

Base model: Qwen/Qwen3.5-0.8B
Method: LoRA (r=16, alpha=32, dropout=0.05)
Epochs: 3
Precision: BF16
Max sequence length: 256 tokens
Mode: gate (binary VERDICT only)
Oversampling: 2x UNSAFE examples for higher recall
Optimizer: paged_adamw_8bit

Critical: Chat Template

This model requires an empty <think>\n\n</think>\n block before the assistant's output. See the main Ward model card for details on why this is required.

Usage with vLLM

vllm serve pahajokiconsulting/anvil-ward-gate --max-model-len 512

Construct the prompt:

<|im_start|>system
You are a fast security gate for Anvil, an AI agent platform. Classify each input as safe or unsafe. Respond with ONLY:

VERDICT: SAFE

or

VERDICT: UNSAFE

Nothing else. When in doubt, respond UNSAFE.<|im_end|>
<|im_start|>user
{user_input}<|im_end|>
<|im_start|>assistant
<think>

</think>

Usage with Ollama

See deploy/Modelfile-ward-gate-q4 in this repository.

License

Apache 2.0 (matching Qwen3.5 base model license)

Downloads last month: 13

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for pahajokiconsulting/anvil-ward-gate

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Adapter

(240)

this model