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 /tests /test_eval_wrapper.py

RegalInvader

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

e5235fb verified 3 days ago

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"""T0.3 wrapper: the ternary switch replaces routed experts by their fake-quant, and nothing else."""
import torch
from transformers import Qwen3MoeConfig, Qwen3MoeForCausalLM

from lowbit.eval.wrapper import routed_expert_params, ternarise_experts
from lowbit.quant.ternary import QuantSpec, fake_quant


def tiny_moe():
    torch.manual_seed(0)
    cfg = Qwen3MoeConfig(vocab_size=256, hidden_size=128, intermediate_size=256, moe_intermediate_size=128,
                         num_hidden_layers=2, num_attention_heads=2, num_key_value_heads=1, head_dim=64,
                         num_experts=4, num_experts_per_tok=2, max_position_embeddings=64)
    m = Qwen3MoeForCausalLM(cfg)
    with torch.no_grad():
        for p in m.parameters():
            p.normal_(0, 0.02)
    return m


def test_ternarise_experts_only_touches_experts():
    m = tiny_moe()
    before = {n: p.detach().clone() for n, p in m.named_parameters()}
    expert_names = {n for n, _ in routed_expert_params(m)}
    assert expert_names, "no routed-expert weights found"
    spec = QuantSpec()
    n = ternarise_experts(m, spec)
    assert n == sum(before[k].numel() for k in expert_names)
    for name, p in m.named_parameters():
        if name in expert_names:
            assert torch.equal(p, fake_quant(before[name].float(), spec).to(p.dtype))
            g = p.reshape(-1, 128)  # each g128 group holds at most {-s, 0, +s}
            assert all(len(torch.unique(row.abs())) <= 2 for row in g[:16])
        else:
            assert torch.equal(p, before[name]), name

	"""T0.3 wrapper: the ternary switch replaces routed experts by their fake-quant, and nothing else."""
	import torch
	from transformers import Qwen3MoeConfig, Qwen3MoeForCausalLM

	from lowbit.eval.wrapper import routed_expert_params, ternarise_experts
	from lowbit.quant.ternary import QuantSpec, fake_quant


	def tiny_moe():
	torch.manual_seed(0)
	cfg = Qwen3MoeConfig(vocab_size=256, hidden_size=128, intermediate_size=256, moe_intermediate_size=128,
	num_hidden_layers=2, num_attention_heads=2, num_key_value_heads=1, head_dim=64,
	num_experts=4, num_experts_per_tok=2, max_position_embeddings=64)
	m = Qwen3MoeForCausalLM(cfg)
	with torch.no_grad():
	for p in m.parameters():
	p.normal_(0, 0.02)
	return m


	def test_ternarise_experts_only_touches_experts():
	m = tiny_moe()
	before = {n: p.detach().clone() for n, p in m.named_parameters()}
	expert_names = {n for n, _ in routed_expert_params(m)}
	assert expert_names, "no routed-expert weights found"
	spec = QuantSpec()
	n = ternarise_experts(m, spec)
	assert n == sum(before[k].numel() for k in expert_names)
	for name, p in m.named_parameters():
	if name in expert_names:
	assert torch.equal(p, fake_quant(before[name].float(), spec).to(p.dtype))
	g = p.reshape(-1, 128) # each g128 group holds at most {-s, 0, +s}
	assert all(len(torch.unique(row.abs())) <= 2 for row in g[:16])
	else:
	assert torch.equal(p, before[name]), name