Instructions to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF")
model = AutoModelForCausalLM.from_pretrained("fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_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 fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

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 fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4
# Run inference directly in the terminal:
./llama-cli -hf fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

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 fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

Use Docker

docker model run hf.co/fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

LM Studio
Jan

vLLM

How to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fabxx48/GPT_OSS_20B_ArduinoExpert_v4_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": "fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

SGLang

How to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF with Ollama:
```
ollama run hf.co/fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4
```

Unsloth Studio

How to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_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 fabxx48/GPT_OSS_20B_ArduinoExpert_v4_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 fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF to start chatting

How to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

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": "fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_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 fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

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 fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

Run Hermes

hermes

OpenClaw new

How to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

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 "fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4" \
  --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 fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF with Docker Model Runner:
```
docker model run hf.co/fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4
```

Lemonade

How to use fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fabxx48/GPT_OSS_20B_ArduinoExpert_v4_GGUF:MXFP4

Run and chat with the model

lemonade run user.GPT_OSS_20B_ArduinoExpert_v4_GGUF-MXFP4

List all available models

lemonade list

Atomic Chat

GPT_OSS_20B_ArduinoExpert_v4_GGUF / README.md

fabxx48

Update README.md

1c83865 verified 8 months ago

preview code

Raw

History Blame Contribute Delete

3.45 kB

	---
	base_model: openai/gpt-oss-20b
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- arduino
	- electronics
	- embedded-systems
	- cpp
	- gguf
	license: apache-2.0
	language:
	- en
	- it
	- fr
	- es
	- de
	---

	# 🤖 GPT_OSS_20B_ArduinoExpert_v0.2 - GGUF

	![Unsloth](https://img.shields.io/badge/made%20with-unsloth-yellow?style=flat&logo=unsloth)
	![Arduino](https://img.shields.io/badge/domain-Arduino%20%26%20Embedded-00979D?style=flat&logo=arduino)
	![GGUF](https://img.shields.io/badge/format-GGUF-blue?style=flat)

	> Languages: 🇺🇸 English \| 🇮🇹 Italiano \| 🇫🇷 Français \| 🇪🇸 Español \| 🇩🇪 Deutsch
	>
	> This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).

	---

	<a name="english-description"></a>
	## 🇬🇧 English Description

	GPT_OSS_20B_ArduinoExpert_v0.2 is a specialized fine-tune designed to assist makers, students, and engineers with Arduino prototyping, embedded C++ programming, and circuit design.

	### 🌍 Multilingual Capabilities
	While fine-tuned primarily on English and Italian technical data, this model inherits the strong multilingual capabilities of the base `gpt-oss-20b`. It can understand and generate technical explanations in French, Spanish, and German, effectively bridging the gap between technical English documentation and your native language.

	### 🚀 Capabilities
	* Arduino/C++ Syntax: Modern libraries, proper memory management, and ISRs.
	* Hardware Wiring: Pinouts for Arduino Uno, Nano, ESP32, and common sensor connections.
	* Debugging: Identifying compilation errors and common hardware pitfalls.

	### ⚠️ Limitations & Safety
	* Voltage Logic: Always verify pin voltages (3.3V vs 5V) with a multimeter.
	* Safety: Do NOT use for mains voltage (110V/220V).
	* Hallucinations: Always check official datasheets.

	---

	<a name="descrizione-italiana"></a>
	## 🇮🇹 Descrizione Italiana

	GPT_OSS_20B_ArduinoExpert_v0.2 è un modello specializzato per Arduino, C++ embedded e progettazione circuitale.

	### 🌍 Supporto Multilingue
	Oltre all'Italiano e all'Inglese, il modello mantiene le capacità multilingue native di `gpt-oss-20b`. Puoi fargli domande in Francese, Spagnolo o Tedesco e ricevere risposte tecniche coerenti e codice commentato correttamente.

	### 🚀 Cosa sa fare
	* Codice C++: Scrive sketch ottimizzati per Arduino e ESP32.
	* Hardware: Spiega come collegare sensori (I2C, SPI) e gestisce i pinout.
	* Debug: Analizza errori di compilazione e suggerisce fix hardware.

	### ⚠️ Avvertenze
	1. Voltaggi: Controlla sempre i voltaggi col multimetro prima di collegare.
	2. Sicurezza: Non usare per progetti ad alta tensione (220V).

	---

	## 📂 Available Model Files / File Disponibili

	\| Filename \| Quantization \| Description \|
	\| :--- \| :--- \| :--- \|
	\| `gpt-oss-20b.MXFP4.gguf` \| MXFP4 \| Balanced performance/size (Recommended) \|

	## 🔧 Training Details

	* Finetuned with: [Unsloth](https://github.com/unslothai/unsloth)
	* Base Model: openai/gpt-oss-20b
	* Format: GGUF

	<br>

	<details>
	<summary><strong>📚 Citation / Citazione (Click to expand)</strong></summary>

	```bibtex
	@misc{unsloth2023,
	title={Unsloth: Faster and Memory Efficient LLM Fine-tuning},
	author={Daniel Han and Unsloth Team},
	year={2023},
	url={[https://github.com/unslothai/unsloth](https://github.com/unslothai/unsloth)}
	}