Instructions to use jc-builds/SmolLM2-360M-Instruct-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jc-builds/SmolLM2-360M-Instruct-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jc-builds/SmolLM2-360M-Instruct-Q4_K_M-GGUF",
	filename="SmolLM2-360M-Instruct.Q4_K_M.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 jc-builds/SmolLM2-360M-Instruct-Q4_K_M-GGUF with llama.cpp:

Install from brew

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

Use Docker

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

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

Unsloth Studio

How to use jc-builds/SmolLM2-360M-Instruct-Q4_K_M-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 jc-builds/SmolLM2-360M-Instruct-Q4_K_M-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 jc-builds/SmolLM2-360M-Instruct-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jc-builds/SmolLM2-360M-Instruct-Q4_K_M-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use jc-builds/SmolLM2-360M-Instruct-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/jc-builds/SmolLM2-360M-Instruct-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use jc-builds/SmolLM2-360M-Instruct-Q4_K_M-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

SmolLM2-360M-Instruct Q4_K_M GGUF

This is a Q4_K_M quantized GGUF conversion of HuggingFaceTB/SmolLM2-360M-Instruct optimized for on-device inference with llama.cpp.

Model Details

Property	Value
Original Model	SmolLM2-360M-Instruct
Parameters	360 million
Quantization	Q4_K_M (4-bit, medium quality)
File Size	~258 MB
Context Window	8,192 tokens
Architecture	LLaMA-style transformer
Training Data	4 trillion tokens

Intended Use

This model is optimized for:

Mobile/Edge Deployment: Runs efficiently on all iOS devices
llama.cpp Integration: Compatible with llama.cpp and its bindings
On-Device AI: Private, offline inference without cloud dependencies

Capabilities

Fast Responses: Very quick inference
Text Rewriting: Good at summarization and rewriting
Instruction Following: Solid instruction following for its size
Low Resource Usage: Minimal RAM and storage requirements
Trained on 4T Tokens: Quality upgrade from 135M version

Usage with llama.cpp

./llama-cli -m SmolLM2-360M-Instruct.Q4_K_M.gguf -p "Your prompt here" -n 512

License

This model inherits the Apache 2.0 license from the original SmolLM2 model.

Attribution

Original Model: SmolLM2-360M-Instruct by Hugging Face
Quantization: jc-builds

Downloads last month: 63

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for jc-builds/SmolLM2-360M-Instruct-Q4_K_M-GGUF

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Quantized

(89)

this model