Instructions to use assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF",
	filename="unoq_optimized.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF
# Run inference directly in the terminal:
llama-cli -hf assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF
# Run inference directly in the terminal:
llama-cli -hf assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF

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 assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF
# Run inference directly in the terminal:
./llama-cli -hf assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF

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 assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF

Use Docker

docker model run hf.co/assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF

LM Studio
Jan
Ollama
How to use assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF with Ollama:
```
ollama run hf.co/assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF
```

Unsloth Studio new

How to use assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-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 assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-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 assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF to start chatting

Docker Model Runner
How to use assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF with Docker Model Runner:
```
docker model run hf.co/assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF
```

Lemonade

How to use assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF

Run and chat with the model

lemonade run user.SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF

Quantized and optimized for the Arduino Uno Q using an NVIDIA DGX Spark.

Optimization Specs

Architecture: ARM64 / Adreno 702 GPU
Quantization: Q4_0 with --pure alignment for Adreno kernels.
Base Model: SmolLM2-135M-Instruct

⚙️ Inference Engine

This model is designed to run with the optimized llama-cli binary found in the official Uno Q Edge AI repository: 👉 GitHub: Arduino-UnoQ-Optimized-Llama-CLI

Usage

bash ./llama-cli -m unoq_optimized.gguf -ngl 99 -t 4 -c 2048

Downloads last month: 121

GGUF

Model size

0.1B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for assix-research/SmolLM2-135M-Instruct-ArduinoUnoQ-GGUF

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Quantized

(98)

this model