Instructions to use RegalInvader/Marco-Nano-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use RegalInvader/Marco-Nano-Instruct-GGUF 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 RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
# Run inference directly in the terminal:
llama cli -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
# Run inference directly in the terminal:
llama cli -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0

Use Docker

docker model run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0

Llama new
LM Studio
Jan

vLLM

How to use RegalInvader/Marco-Nano-Instruct-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0

Ollama
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Ollama:
```
ollama run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
```
Unsloth Desktop

How to use RegalInvader/Marco-Nano-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_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": "RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
```

Lemonade

How to use RegalInvader/Marco-Nano-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0

Run and chat with the model

lemonade run user.Marco-Nano-Instruct-GGUF-TQ1_0

List all available models

lemonade list

Hermes Agent

How to use RegalInvader/Marco-Nano-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_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 RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use RegalInvader/Marco-Nano-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_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 "RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_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"

Marco-Nano-Instruct-GGUF / code /scripts /run_rs.py

RegalInvader

Release: Marco-Nano-Instruct 1-, 2- and 3-bit GGUF for phones

e5235fb verified 4 days ago

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#!/usr/bin/env python3
"""Run RS-fast / RS-full (spec §7) through the marco model wrapper; see src/lowbit/eval/rs.py."""
from __future__ import annotations

import argparse
import json

from lowbit.eval.rs import run_suite


def main() -> None:
    p = argparse.ArgumentParser()
    p.add_argument("--suite", required=True)
    p.add_argument("--out", required=True)
    p.add_argument("--model-args", required=True, help="lm-eval model_args for the chosen backend")
    p.add_argument("--batch-size", default="16", help="an int, or auto (vLLM schedules everything itself)")
    p.add_argument("--model", default="marco", choices=["marco", "vllm"])
    p.add_argument("--runs", nargs="*", help="run names from the suite (default: all)")
    p.add_argument("--tasks", nargs="*", help="tasks within those runs (default: all)")
    p.add_argument("--limit", type=int, default=None, help="items per task (dry runs only)")
    p.add_argument("--device", default="cuda")
    a = p.parse_args()
    bs = a.batch_size if a.batch_size == "auto" else int(a.batch_size)
    s = run_suite(a.suite, a.out, a.model_args, bs, a.limit, a.device, a.model, a.runs, a.tasks)
    print(json.dumps({k: s[k] for k in ("scores", "timings", "total_s")}, indent=1))


if __name__ == "__main__":
    main()

	#!/usr/bin/env python3
	"""Run RS-fast / RS-full (spec §7) through the marco model wrapper; see src/lowbit/eval/rs.py."""
	from __future__ import annotations

	import argparse
	import json

	from lowbit.eval.rs import run_suite


	def main() -> None:
	p = argparse.ArgumentParser()
	p.add_argument("--suite", required=True)
	p.add_argument("--out", required=True)
	p.add_argument("--model-args", required=True, help="lm-eval model_args for the chosen backend")
	p.add_argument("--batch-size", default="16", help="an int, or auto (vLLM schedules everything itself)")
	p.add_argument("--model", default="marco", choices=["marco", "vllm"])
	p.add_argument("--runs", nargs="*", help="run names from the suite (default: all)")
	p.add_argument("--tasks", nargs="*", help="tasks within those runs (default: all)")
	p.add_argument("--limit", type=int, default=None, help="items per task (dry runs only)")
	p.add_argument("--device", default="cuda")
	a = p.parse_args()
	bs = a.batch_size if a.batch_size == "auto" else int(a.batch_size)
	s = run_suite(a.suite, a.out, a.model_args, bs, a.limit, a.device, a.model, a.runs, a.tasks)
	print(json.dumps({k: s[k] for k in ("scores", "timings", "total_s")}, indent=1))


	if __name__ == "__main__":
	main()