Instructions to use NGARiAI/ngari-tool with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NGARiAI/ngari-tool 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 NGARiAI/ngari-tool:F16
# Run inference directly in the terminal:
llama cli -hf NGARiAI/ngari-tool:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NGARiAI/ngari-tool:F16
# Run inference directly in the terminal:
llama cli -hf NGARiAI/ngari-tool: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 NGARiAI/ngari-tool:F16
# Run inference directly in the terminal:
./llama-cli -hf NGARiAI/ngari-tool: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 NGARiAI/ngari-tool:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NGARiAI/ngari-tool:F16

Use Docker

docker model run hf.co/NGARiAI/ngari-tool:F16

LM Studio
Jan
Ollama
How to use NGARiAI/ngari-tool with Ollama:
```
ollama run hf.co/NGARiAI/ngari-tool:F16
```
Unsloth Desktop

How to use NGARiAI/ngari-tool with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NGARiAI/ngari-tool: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": "NGARiAI/ngari-tool:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NGARiAI/ngari-tool with Docker Model Runner:
```
docker model run hf.co/NGARiAI/ngari-tool:F16
```

Lemonade

How to use NGARiAI/ngari-tool with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NGARiAI/ngari-tool:F16

Run and chat with the model

lemonade run user.ngari-tool-F16

List all available models

lemonade list

Hermes Agent

How to use NGARiAI/ngari-tool with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NGARiAI/ngari-tool: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 NGARiAI/ngari-tool:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NGARiAI/ngari-tool with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NGARiAI/ngari-tool: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 "NGARiAI/ngari-tool: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"

NGARi Tool — Tool-Calling 1.5B

1.5B tool-calling model: Qwen2.5-1.5B-Instruct + tool-format LoRA. 100% tool detection / name / params validity on the NGARi tool-format eval (20 examples). Serves as the NGARi agent tool-mode model in production.

Provenance (verified Aug 3, 2026)

Attribute	Value
Base model	`Qwen/Qwen2.5-1.5B-Instruct` (Apache 2.0) — pinned in `adapter_config.json`
LoRA	rank 32, alpha 64, dropout 0.05, all linear projections
Synthetic data teacher	`qwen3:8b`
License	Apache 2.0 (NGARi-authored artifacts)
Hardware validated	aarch64 / NVIDIA Jetson AGX Orin, 8GB RAM, air-gap verified

Note: Google Gemma models were served only on NGARi hardware and were never used in NGARi training. All training used the Apache-2.0 Qwen2.5 lineage.

Evaluation

ngari-tool-stable_tool_eval.json

{
  "model": "ngari-tool:stable",
  "num_examples": 20,
  "total_examples": 20,
  "tool_detection_rate": 1.0,
  "tool_name_accuracy": 1.0,
  "params_validity_rate": 1.0,
  "tool_detected": 20,
  "name_correct": 20,
  "params_valid": 20,
  "avg_latency_sec": 3.17,
  "total_time_sec": 63.4
}

Files

File	Purpose
`model-*.safetensors` (+ config)	Merged full model — use with Transformers
`adapter_model.safetensors`	PEFT LoRA adapter — apply on the base
`-q4_K_M.gguf` / `-f16.gguf`	GGUF — use with Ollama / llama.cpp

Usage

# Ollama (GGUF)
ollama create ngariai/ngari-tool:q4_K_M -f Modelfile

# Transformers (merged)
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ngariai/ngari-tool")

# PEFT adapter (apply on base)
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
adapter = PeftModel.from_pretrained(base, "ngariai/ngari-tool")

Sovereign AI

Trained and verified on user-owned edge hardware with zero cloud dependency. Part of the NGARi Sovereign Business Operating System (NS-BOS) — see https://github.com/ngariai/ns-bos-kernel for the Apache 2.0 kernel.

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Safetensors

Model size

2B params

Tensor type

F32

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Model tree for NGARiAI/ngari-tool

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

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