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 /pyproject.toml

RegalInvader

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

e5235fb verified 3 days ago

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[project]
name = "lowbit"
version = "0.1.0"
description = "Marco-Nano-T ternary distillation trials"
requires-python = ">=3.12,<3.13"
dependencies = [
    "torch",
    "transformers",
    "datasets",
    "safetensors",
    "numpy",
    "zstandard",
    "pyyaml",
    "pyarrow",
    "lm-eval[ifeval,math]",  # IFEval and MATH-500 scoring for RS-full
    "accelerate",  # lm-eval's HF backend (T0.3 eval wrapper)
    "pytest",
    "hypothesis",
    "fasttext-numpy2-wheel",  # language ID (lid.176) for prompt translation and output filters (T1.3, T1.4)
]

[project.optional-dependencies]
# gen: desktop GPU (sm_120), x86-64 — teacher generation and student evals
gen = ["vllm"]
# spark: GB10, aarch64, CUDA 13 — resolved on spark itself (env/spark.lock)
spark = ["torchao", "bitsandbytes"]  # 8-bit AdamW: whichever installs on aarch64 and passes T2.5

[build-system]
requires = ["setuptools>=68"]
build-backend = "setuptools.build_meta"

[tool.setuptools.packages.find]
where = ["src"]

[tool.pytest.ini_options]
testpaths = ["tests"]
markers = ["gpu: needs a CUDA device (run with -m gpu)",
           "slow: reads full datasets from $MT_DATA (run with -m slow)"]
addopts = "-m 'not gpu and not slow'"

	[project]
	name = "lowbit"
	version = "0.1.0"
	description = "Marco-Nano-T ternary distillation trials"
	requires-python = ">=3.12,<3.13"
	dependencies = [
	"torch",
	"transformers",
	"datasets",
	"safetensors",
	"numpy",
	"zstandard",
	"pyyaml",
	"pyarrow",
	"lm-eval[ifeval,math]", # IFEval and MATH-500 scoring for RS-full
	"accelerate", # lm-eval's HF backend (T0.3 eval wrapper)
	"pytest",
	"hypothesis",
	"fasttext-numpy2-wheel", # language ID (lid.176) for prompt translation and output filters (T1.3, T1.4)
	]

	[project.optional-dependencies]
	# gen: desktop GPU (sm_120), x86-64 — teacher generation and student evals
	gen = ["vllm"]
	# spark: GB10, aarch64, CUDA 13 — resolved on spark itself (env/spark.lock)
	spark = ["torchao", "bitsandbytes"] # 8-bit AdamW: whichever installs on aarch64 and passes T2.5

	[build-system]
	requires = ["setuptools>=68"]
	build-backend = "setuptools.build_meta"

	[tool.setuptools.packages.find]
	where = ["src"]

	[tool.pytest.ini_options]
	testpaths = ["tests"]
	markers = ["gpu: needs a CUDA device (run with -m gpu)",
	"slow: reads full datasets from $MT_DATA (run with -m slow)"]
	addopts = "-m 'not gpu and not slow'"