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 /src /lowbit /eval /gsm8k.py

RegalInvader

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

e5235fb verified 5 days ago

Raw History Blame Contribute Delete

1.29 kB

"""GSM8K answer extraction and matching, shared by the eval harness (RS-fast/RS-full) and the T1.4 FP8 check."""
from __future__ import annotations

import math
import re

MATH_SUFFIX = "\nPlease reason step by step, and put your final answer within \\boxed{}."


def extract_answer(text: str) -> str | None:
    """The last \\boxed{...} answer, else the last number in the text, normalised for comparison."""
    boxed = re.findall(r"\\boxed\{((?:[^{}]|\{[^{}]*\})*)\}", text)
    cand = boxed[-1] if boxed else None
    if cand is None:
        nums = re.findall(r"-?\d[\d,]*(?:\.\d+)?", text)
        cand = nums[-1] if nums else None
    if cand is None:
        return None
    cand = cand.replace("\\$", "").replace("\\!", "").replace(",", "").replace("$", "").replace("\\%", "").replace("%", "")
    cand = cand.strip().rstrip(".")
    cand = re.sub(r"\\text\{[^}]*\}", "", cand).strip()
    m = re.fullmatch(r"\\[dt]?frac\{(-?\d+)\}\{(\d+)\}", cand)
    if m:
        return str(int(m.group(1)) / int(m.group(2)))
    return cand


def same_number(pred: str | None, gold: str) -> bool:
    if pred is None:
        return False
    try:
        return math.isclose(float(pred), float(gold), rel_tol=0, abs_tol=1e-6)
    except ValueError:
        return pred.strip() == gold.strip()

	"""GSM8K answer extraction and matching, shared by the eval harness (RS-fast/RS-full) and the T1.4 FP8 check."""
	from __future__ import annotations

	import math
	import re

	MATH_SUFFIX = "\nPlease reason step by step, and put your final answer within \\boxed{}."


	def extract_answer(text: str) -> str \| None:
	"""The last \\boxed{...} answer, else the last number in the text, normalised for comparison."""
	boxed = re.findall(r"\\boxed\{((?:[^{}]\|\{[^{}]\}))\}", text)
	cand = boxed[-1] if boxed else None
	if cand is None:
	nums = re.findall(r"-?\d[\d,]*(?:\.\d+)?", text)
	cand = nums[-1] if nums else None
	if cand is None:
	return None
	cand = cand.replace("\\$", "").replace("\\!", "").replace(",", "").replace("$", "").replace("\\%", "").replace("%", "")
	cand = cand.strip().rstrip(".")
	cand = re.sub(r"\\text\{[^}]*\}", "", cand).strip()
	m = re.fullmatch(r"\\[dt]?frac\{(-?\d+)\}\{(\d+)\}", cand)
	if m:
	return str(int(m.group(1)) / int(m.group(2)))
	return cand


	def same_number(pred: str \| None, gold: str) -> bool:
	if pred is None:
	return False
	try:
	return math.isclose(float(pred), float(gold), rel_tol=0, abs_tol=1e-6)
	except ValueError:
	return pred.strip() == gold.strip()