Instructions to use mradermacher/Marco-Nano-Instruct-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Marco-Nano-Instruct-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Marco-Nano-Instruct-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Marco-Nano-Instruct-i1-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 mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M

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 mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M

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 mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Marco-Nano-Instruct-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/Marco-Nano-Instruct-i1-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mradermacher/Marco-Nano-Instruct-i1-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mradermacher/Marco-Nano-Instruct-i1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/Marco-Nano-Instruct-i1-GGUF to start chatting

How to use mradermacher/Marco-Nano-Instruct-i1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mradermacher/Marco-Nano-Instruct-i1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M

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 "mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M" \
  --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"

Docker Model Runner
How to use mradermacher/Marco-Nano-Instruct-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Marco-Nano-Instruct-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Marco-Nano-Instruct-i1-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mradermacher/Marco-Nano-Instruct-i1-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 mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M

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 mradermacher/Marco-Nano-Instruct-i1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Marco-Nano-Instruct-i1-GGUF / README.md

mradermacher

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metadata

base_model: ATH-MaaS/Marco-Nano-Instruct
datasets:
  - allenai/Dolci-Instruct-SFT
  - nvidia/Nemotron-Cascade-2-SFT-Data
  - nvidia/Nemotron-RL-instruction_following
  - nvidia/Nemotron-RL-instruction_following-structured_outputs
  - nvidia/Nemotron-RL-ReasoningGym-v1
  - nvidia/Nemotron-RL-knowledge-mcqa
  - nvidia/Nemotron-Cascade-RL-RLHF
  - BytedTsinghua-SIA/DAPO-Math-17k
  - Skywork/Skywork-OR1-RL-Data
  - nvidia/Nemotron-SFT-Multilingual-v1
language:
  - en
  - zh
  - ar
  - de
  - es
  - fr
  - ko
  - ja
  - pt
  - tr
  - id
  - it
  - nl
  - pl
  - ru
  - vi
  - th
  - he
  - uk
  - ms
  - bn
  - cs
  - ur
  - kk
  - el
  - ro
  - hu
  - ne
  - az
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
  - moe
  - mixture-of-experts
  - multilingual
  - upcycling

About

weighted/imatrix quants of https://huggingface.co/ATH-MaaS/Marco-Nano-Instruct

For a convenient overview and download list, visit our model page for this model.

static quants are available at https://huggingface.co/mradermacher/Marco-Nano-Instruct-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	imatrix	0.2	imatrix file (for creating your own quants)
GGUF	i1-IQ1_S	2.7	for the desperate
GGUF	i1-IQ1_M	2.8	mostly desperate
GGUF	i1-IQ2_XXS	3.0
GGUF	i1-IQ2_XS	3.2
GGUF	i1-IQ2_S	3.2
GGUF	i1-IQ2_M	3.4
GGUF	i1-Q2_K	3.4	IQ3_XXS probably better
GGUF	i1-Q2_K_S	3.5	very low quality
GGUF	i1-IQ3_XXS	3.7	lower quality
GGUF	i1-IQ3_XS	3.8
GGUF	i1-IQ3_S	4.0	beats Q3_K*
GGUF	i1-Q3_K_S	4.0	IQ3_XS probably better
GGUF	i1-IQ3_M	4.0
GGUF	i1-Q3_K_M	4.3	IQ3_S probably better
GGUF	i1-Q3_K_L	4.5	IQ3_M probably better
GGUF	i1-IQ4_XS	4.5
GGUF	i1-IQ4_NL	4.7	prefer IQ4_XS
GGUF	i1-Q4_0	4.7	fast, low quality
GGUF	i1-Q4_K_S	5.0	optimal size/speed/quality
GGUF	i1-Q4_1	5.2
GGUF	i1-Q4_K_M	5.5	fast, recommended
GGUF	i1-Q5_K_S	5.8
GGUF	i1-Q5_K_M	6.2
GGUF	i1-Q6_K	7.3	practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.