Instructions to use kylebrodeur/microfactory-node-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kylebrodeur/microfactory-node-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kylebrodeur/microfactory-node-gguf",
	filename="microfactory-node-v2.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kylebrodeur/microfactory-node-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 kylebrodeur/microfactory-node-gguf:Q4_0
# Run inference directly in the terminal:
llama cli -hf kylebrodeur/microfactory-node-gguf:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kylebrodeur/microfactory-node-gguf:Q4_0
# Run inference directly in the terminal:
llama cli -hf kylebrodeur/microfactory-node-gguf:Q4_0

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 kylebrodeur/microfactory-node-gguf:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf kylebrodeur/microfactory-node-gguf:Q4_0

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 kylebrodeur/microfactory-node-gguf:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kylebrodeur/microfactory-node-gguf:Q4_0

Use Docker

docker model run hf.co/kylebrodeur/microfactory-node-gguf:Q4_0

LM Studio
Jan
Ollama
How to use kylebrodeur/microfactory-node-gguf with Ollama:
```
ollama run hf.co/kylebrodeur/microfactory-node-gguf:Q4_0
```

Unsloth Studio

How to use kylebrodeur/microfactory-node-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 kylebrodeur/microfactory-node-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 kylebrodeur/microfactory-node-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kylebrodeur/microfactory-node-gguf to start chatting

How to use kylebrodeur/microfactory-node-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kylebrodeur/microfactory-node-gguf:Q4_0

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": "kylebrodeur/microfactory-node-gguf:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kylebrodeur/microfactory-node-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 kylebrodeur/microfactory-node-gguf:Q4_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 kylebrodeur/microfactory-node-gguf:Q4_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use kylebrodeur/microfactory-node-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kylebrodeur/microfactory-node-gguf:Q4_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 "kylebrodeur/microfactory-node-gguf:Q4_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"

Docker Model Runner
How to use kylebrodeur/microfactory-node-gguf with Docker Model Runner:
```
docker model run hf.co/kylebrodeur/microfactory-node-gguf:Q4_0
```

Lemonade

How to use kylebrodeur/microfactory-node-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kylebrodeur/microfactory-node-gguf:Q4_0

Run and chat with the model

lemonade run user.microfactory-node-gguf-Q4_0

List all available models

lemonade list

microfactory-node-gguf / README.md

kylebrodeur

docs: add q4_0 + ollama.com tags + cross-links

b61bd10 verified 19 days ago

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	---
	license: gemma
	base_model: google/gemma-4-e4b-it
	tags:
	- gguf
	- llama-cpp
	- ollama
	- 3d-printing
	- chief-engineer
	- microfactory
	language:
	- en
	---

	# Microfactory Node — Chief Engineer (GGUF)

	Quantized GGUFs of three LoRA-fine-tuned variants of
	[`google/gemma-4-e4b-it`](https://huggingface.co/google/gemma-4-e4b-it), trained
	on real 3D-printer outcomes to predict where a print will fail and propose
	settings before the nozzle moves.

	Both distribution paths point at the same blobs:
	- `ollama.com/kylebrodeur` — public Ollama registry, one-command pulls
	- `huggingface.co/kylebrodeur/microfactory-node-gguf` (this repo) — canonical GGUFs + `template`/`system`/`params` config

	\| File \| Quant \| Size \| `ollama run …` (registry tag) \| Source adapter \|
	\|------\|-------\|------\|-------------------------------\|----------------\|
	\| `microfactory-node-v3-qat.gguf` \| q4_k_m \| 5.1 GB \| [`kylebrodeur/microfactory-node-v3-qat`](https://ollama.com/kylebrodeur/microfactory-node-v3-qat) (recommended) \| [`microfactory-node-lora-v3-qat`](https://huggingface.co/kylebrodeur/microfactory-node-lora-v3-qat) \|
	\| `microfactory-node-v3-qat-q4_0.gguf` \| q4_0 \| 4.9 GB \| [`kylebrodeur/microfactory-node-v3-qat:q4_0`](https://ollama.com/kylebrodeur/microfactory-node-v3-qat:q4_0) \| [`microfactory-node-lora-v3-qat`](https://huggingface.co/kylebrodeur/microfactory-node-lora-v3-qat) \|
	\| `microfactory-node-v2.gguf` \| q4_k_m \| 5.1 GB \| [`kylebrodeur/microfactory-node-v2`](https://ollama.com/kylebrodeur/microfactory-node-v2) \| [`microfactory-node-lora-v2`](https://huggingface.co/kylebrodeur/microfactory-node-lora-v2) \|
	\| `microfactory-node.gguf` \| q4_k_m \| 5.1 GB \| [`kylebrodeur/microfactory-node`](https://ollama.com/kylebrodeur/microfactory-node) \| [`microfactory-node-lora`](https://huggingface.co/kylebrodeur/microfactory-node-lora) \|

	> The QAT model was trained with simulated 4-bit quantization, so it retains
	> more quality after quantization than the standard v2. Use `q4_k_m` for
	> balanced quality/size, or `q4_0` (the quant Google's QAT was trained for)
	> for the highest fidelity reconstruction of the QAT model.

	## Run with Ollama (public registry — easiest)

	```bash
	# recommended
	ollama run kylebrodeur/microfactory-node-v3-qat

	# QAT-native quant
	ollama run kylebrodeur/microfactory-node-v3-qat:q4_0

	# other variants
	ollama run kylebrodeur/microfactory-node-v2
	ollama run kylebrodeur/microfactory-node
	```

	## Run with Ollama (this HF repo — no download step)

	Ollama can pull GGUFs directly from HF — the `template`, `system`, and `params`
	files in this repo configure the Gemma 4 chat template, the Chief Engineer
	system prompt, and tuned sampling automatically:

	```bash
	ollama run hf.co/kylebrodeur/microfactory-node-gguf:microfactory-node-v3-qat.gguf
	ollama run hf.co/kylebrodeur/microfactory-node-gguf:microfactory-node-v3-qat-q4_0.gguf
	ollama run hf.co/kylebrodeur/microfactory-node-gguf:microfactory-node-v2.gguf
	ollama run hf.co/kylebrodeur/microfactory-node-gguf:microfactory-node.gguf
	```

	See the [HF × Ollama docs](https://huggingface.co/docs/hub/en/ollama) for the
	`hf.co/...` URI form and how Ollama discovers the auxiliary config files.

	## Run with llama.cpp

	```bash
	hf download kylebrodeur/microfactory-node-gguf microfactory-node-v3-qat.gguf --local-dir .
	llama-cli -m microfactory-node-v3-qat.gguf -p "PLA overhang at 22C, 45% humidity"
	```

	## Use the live demo

	The Hugging Face Space [`build-small-hackathon/microfactory-lab`](https://huggingface.co/spaces/build-small-hackathon/microfactory-lab)
	runs the full Chief Engineer UI against these adapters (ZeroGPU + a Modal-hosted
	OpenAI-compatible endpoint as fallback). Source repo:
	[`kylebrodeur/microfactory-lab`](https://github.com/kylebrodeur/microfactory-lab).

	The full conversion + publishing pipeline (LoRA → Modal merge → llama.cpp
	quantize → HF Hub → ollama.com) is documented in
	[`learn/finetune/OLLAMA_PUBLISHING.md`](https://github.com/kylebrodeur/microfactory-lab/blob/main/chief-engineer/learn/finetune/OLLAMA_PUBLISHING.md).