Instructions to use MR-CODESPIKE/sentinelng-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use MR-CODESPIKE/sentinelng-models 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 MR-CODESPIKE/sentinelng-models:Q1_0
# Run inference directly in the terminal:
llama cli -hf MR-CODESPIKE/sentinelng-models:Q1_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MR-CODESPIKE/sentinelng-models:Q1_0
# Run inference directly in the terminal:
llama cli -hf MR-CODESPIKE/sentinelng-models:Q1_0

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 MR-CODESPIKE/sentinelng-models:Q1_0
# Run inference directly in the terminal:
./llama-cli -hf MR-CODESPIKE/sentinelng-models:Q1_0

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 MR-CODESPIKE/sentinelng-models:Q1_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MR-CODESPIKE/sentinelng-models:Q1_0

Use Docker

docker model run hf.co/MR-CODESPIKE/sentinelng-models:Q1_0

LM Studio
Jan
Ollama
How to use MR-CODESPIKE/sentinelng-models with Ollama:
```
ollama run hf.co/MR-CODESPIKE/sentinelng-models:Q1_0
```
Unsloth Desktop

How to use MR-CODESPIKE/sentinelng-models with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MR-CODESPIKE/sentinelng-models:Q1_0

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": "MR-CODESPIKE/sentinelng-models:Q1_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use MR-CODESPIKE/sentinelng-models with Docker Model Runner:
```
docker model run hf.co/MR-CODESPIKE/sentinelng-models:Q1_0
```

Lemonade

How to use MR-CODESPIKE/sentinelng-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MR-CODESPIKE/sentinelng-models:Q1_0

Run and chat with the model

lemonade run user.sentinelng-models-Q1_0

List all available models

lemonade list

Hermes Agent

How to use MR-CODESPIKE/sentinelng-models with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MR-CODESPIKE/sentinelng-models:Q1_0

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 MR-CODESPIKE/sentinelng-models:Q1_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MR-CODESPIKE/sentinelng-models with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MR-CODESPIKE/sentinelng-models:Q1_0

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 "MR-CODESPIKE/sentinelng-models:Q1_0" \
  --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"

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Check out the documentation for more information.

SentinelNG Models

This repository contains model assets used by the SentinelNG Android application for local crop, health, and language assistance. It is a model-artifact repository rather than a complete application.

File	Format	Intended role
`crop_doctor.tflite`	TensorFlow Lite	On-device crop or plant-health inference.
`health_scan.tflite`	TensorFlow Lite	On-device health-scan inference.
`nigerian_nlu.ftz`	FastText-style model artifact	Nigerian-language or intent/NLU support.
`bonsai_1.7b_q1_0.gguf`	GGUF	Compact local language-model asset for constrained devices.

The binary files are large and are stored through Hugging Face LFS/Xet. Download only the assets required by the target application. The corresponding Android integration is in the SentinelNG-App GitHub repository, where TFLiteHelper.kt defines the expected model names and inference lifecycle.

Usage guidance

Before using an asset in a new application, verify its input shape, label order, preprocessing, output interpretation, and license. A model file alone does not define a safe diagnosis workflow. Crop and health predictions are assistive signals and must not replace qualified professional advice.

Reproducibility

Record the training data revision, preprocessing pipeline, label map, evaluation metrics, and conversion/quantization settings whenever these models are replaced. Keep the model filename stable only when the input/output contract remains compatible.

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GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

1-bit

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MR-CODESPIKE
/

sentinelng-models

SentinelNG Models

Contents

Usage guidance

Reproducibility