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

Libraries

How to use NGARiAI/ngari-tool with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NGARiAI/ngari-tool")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NGARiAI/ngari-tool")
model = AutoModelForCausalLM.from_pretrained("NGARiAI/ngari-tool", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

vLLM

How to use NGARiAI/ngari-tool with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

How to use NGARiAI/ngari-tool with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NGARiAI/ngari-tool" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NGARiAI/ngari-tool",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NGARiAI/ngari-tool" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NGARiAI/ngari-tool",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 / README.md

garryjohnsoniii

Add widget example prompts + point datasets link to dataset-type repo

e7f0a3c verified about 1 month ago

preview code

Raw History Blame Contribute Delete

3.99 kB

	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- ngari
	- sovereign-ai
	- edge-ai
	- qwen2.5
	- lora
	- gguf
	- ollama
	- function-calling
	- tool-use
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	datasets:
	- NGARiAI/ngari-datasets
	widget:
	- text: "What's the weather in Nairobi right now?"
	example_title: "Tool call: weather"
	- text: "Add 'review Q4 budget' to my calendar for tomorrow at 10am."
	example_title: "Tool call: calendar"
	- text: "Search the web for sovereign AI infrastructure news."
	example_title: "Tool call: web search"
	---

	# NGARi Tool — Tool-Calling 1.5B

	A 1.5B tool-calling model: Qwen2.5-1.5B-Instruct + tool-format LoRA, scoring 100% tool detection / 100% tool-name accuracy / 100% params validity on the NGARi tool-format eval.

	This is the production tool-mode model in the NGARi Sovereign Business Operating System — the fast, lightweight router that lets on-device agents trigger tools, format function calls, and structure JSON without ever touching the cloud.

	## Why it exists — the NGARi model pipeline

	Small models naturally struggle with the rigid syntax of function calling. NGARi solves this with a teacher→student pipeline:

	```
	Teacher (27B-class, e.g. qwen3.8-27B)
	│ generates multi-turn tool-calling scenarios:
	│ mock JSON schemas, user queries, correct API calls
	▼
	ngari-tool (1.5B)
	│ fine-tuned on perfectly structured examples
	▼
	Result: 100% tool-format accuracy at edge speed (runs on 8GB RAM)
	```

	The companion QA/safety model is [NGARiAI/ngari-ft-distilled](https://huggingface.co/NGARiAI/ngari-ft-distilled).

	## 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` (v1; 27B-class teacher planned for v2) \|
	\| License \| Apache 2.0 (NGARi-authored artifacts) \|
	\| Hardware validated \| aarch64 / NVIDIA Jetson AGX Orin, 8GB RAM, air-gap verified \|

	> 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

	### Tool format — `ngari-tool-stable_tool_eval.json`

	```json
	{
	"model": "ngari-tool:stable",
	"num_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 \|
	\| `ngari-tool-q4_K_M.gguf` / `-f16.gguf` \| GGUF — use with Ollama / llama.cpp \|

	## Usage

	```bash
	# Ollama (GGUF)
	ollama create ngari-tool -f Modelfile
	ollama run ngari-tool "What's the weather in Nairobi?"
	```

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

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

	## Companion repos

	- [NGARiAI/ngari-ft-distilled](https://huggingface.co/NGARiAI/ngari-ft-distilled) — QA + content-safety sibling
	- [NGARiAI/ngari-datasets](https://huggingface.co/datasets/NGARiAI/ngari-datasets) — the training data (incl. tool-format set)
	- [NGARiAI/ns-bos-kernel](https://github.com/NGARiAI/ns-bos-kernel) — the Apache 2.0 sovereign kernel

	## Sovereign AI

	Trained and verified on user-owned edge hardware with zero cloud dependency. Verified air-gap (monitored via `/proc/net/dev`). "AI You Own. Completely."