Instructions to use unsloth/SmolLM2-360M-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/SmolLM2-360M-Instruct-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("unsloth/SmolLM2-360M-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use unsloth/SmolLM2-360M-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/SmolLM2-360M-Instruct-GGUF",
	filename="SmolLM2-360M-Instruct-F16.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 unsloth/SmolLM2-360M-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf unsloth/SmolLM2-360M-Instruct-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 unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf unsloth/SmolLM2-360M-Instruct-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 unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use unsloth/SmolLM2-360M-Instruct-GGUF with Ollama:
```
ollama run hf.co/unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use unsloth/SmolLM2-360M-Instruct-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 unsloth/SmolLM2-360M-Instruct-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 unsloth/SmolLM2-360M-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/SmolLM2-360M-Instruct-GGUF to start chatting

Docker Model Runner
How to use unsloth/SmolLM2-360M-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use unsloth/SmolLM2-360M-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SmolLM2-360M-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Finetune SmolLM2, Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!

We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing

unsloth/SmolLM2-360M-Instruct-GGUF

For more details on the model, please go to Hugging Face's original model card

✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

Unsloth supports	Free Notebooks	Performance	Memory use
Llama-3.2 (3B)	▶️ Start on Colab	2.4x faster	58% less
Llama-3.2 (11B vision)	▶️ Start on Colab	2.4x faster	58% less
Llama-3.1 (8B)	▶️ Start on Colab	2.4x faster	58% less
Phi-3.5 (mini)	▶️ Start on Colab	2x faster	50% less
Gemma 2 (9B)	▶️ Start on Colab	2.4x faster	58% less
Mistral (7B)	▶️ Start on Colab	2.2x faster	62% less
DPO - Zephyr	▶️ Start on Colab	1.9x faster	19% less

This conversational notebook is useful for ShareGPT ChatML / Vicuna templates.
This text completion notebook is for raw text. This DPO notebook replicates Zephyr.
* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

Special Thanks

A huge thank you to the Hugging Face team for creating and releasing these models.

Model Summary

SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device.

The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback.

The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by Argilla such as Synth-APIGen-v0.1.

SmolLM2

Downloads last month: 743

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

2-bit

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5-bit

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8-bit

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