Instructions to use Tang-yin-112/mt-guard-4g-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Tang-yin-112/mt-guard-4g-v5 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 Tang-yin-112/mt-guard-4g-v5
# Run inference directly in the terminal:
llama cli -hf Tang-yin-112/mt-guard-4g-v5

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Tang-yin-112/mt-guard-4g-v5
# Run inference directly in the terminal:
llama cli -hf Tang-yin-112/mt-guard-4g-v5

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 Tang-yin-112/mt-guard-4g-v5
# Run inference directly in the terminal:
./llama-cli -hf Tang-yin-112/mt-guard-4g-v5

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 Tang-yin-112/mt-guard-4g-v5
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tang-yin-112/mt-guard-4g-v5

Use Docker

docker model run hf.co/Tang-yin-112/mt-guard-4g-v5

LM Studio
Jan
Ollama
How to use Tang-yin-112/mt-guard-4g-v5 with Ollama:
```
ollama run hf.co/Tang-yin-112/mt-guard-4g-v5
```
Unsloth Desktop

How to use Tang-yin-112/mt-guard-4g-v5 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tang-yin-112/mt-guard-4g-v5

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": "Tang-yin-112/mt-guard-4g-v5"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Tang-yin-112/mt-guard-4g-v5 with Docker Model Runner:
```
docker model run hf.co/Tang-yin-112/mt-guard-4g-v5
```

Lemonade

How to use Tang-yin-112/mt-guard-4g-v5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tang-yin-112/mt-guard-4g-v5

Run and chat with the model

lemonade run user.mt-guard-4g-v5-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Tang-yin-112/mt-guard-4g-v5 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tang-yin-112/mt-guard-4g-v5

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 Tang-yin-112/mt-guard-4g-v5

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Tang-yin-112/mt-guard-4g-v5 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tang-yin-112/mt-guard-4g-v5

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 "Tang-yin-112/mt-guard-4g-v5" \
  --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"

mt-guard-4g v4

Submission for the Microtensor guard / mt-4g arena. LoRA fine-tune of meta-llama/Llama-3.2-3B-Instruct@0cb88a4f764b7a12671c53f0838cd831a0843b95 on RAGTruth, merged and quantised to GGUF Q4_K_M. Declared input 2048 tokens.

Held-out dev F2 0.6018, projected on the scored mix 0.6719.

Downloads last month: 133

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

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