Instructions to use mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF",
	filename="Kurtis-SmolLM2-360M-Instruct-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mrs83/Kurtis-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 mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mrs83/Kurtis-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 mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mrs83/Kurtis-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 mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mrs83/Kurtis-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 mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrs83/Kurtis-SmolLM2-360M-Instruct-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": "mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF:Q4_K_M

Ollama
How to use mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF with Ollama:
```
ollama run hf.co/mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use mrs83/Kurtis-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 mrs83/Kurtis-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 mrs83/Kurtis-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 mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF to start chatting

Docker Model Runner
How to use mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF:Q4_K_M

Run and chat with the model

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

List all available models

lemonade list

Model Card for Model ID

This model has been fine-tuned using Kurtis, an experimental fine-tuning, inference and evaluation tool for Small Language Models.

Model Details

Model Description

Developed by: Massimo R. Scamarcia massimo.scamarcia@gmail.com
Funded by: Massimo R. Scamarcia massimo.scamarcia@gmail.com - (self-funded)
Shared by: Massimo R. Scamarcia massimo.scamarcia@gmail.com
Model type: Transformer decoder
Language(s) (NLP): English
License: MIT
Finetuned from model [optional]: HuggingFaceTB/SmolLM2-360M-Instruct

Model Sources

Repository: https://github.com/mrs83/kurtis

Uses

The model is intended for use in a conversational setting, particularly in mental health and therapeutic support scenarios.

Direct Use

Not suitable for production usage.

Out-of-Scope Use

This model should not be used for:

Making critical mental health decisions or diagnoses.
Replacing professional mental health services.
Applications where responses require regulatory compliance or are highly sensitive.
Generating responses without human supervision, especially in contexts that involve vulnerable individuals.

Bias, Risks, and Limitations

Misuse of this dataset could lead to providing inappropriate or harmful responses, so it should not be deployed without proper safeguards in place.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

How to Get Started with the Model

ollama run hf.co/mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF

Downloads last month: 26

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

Model tree for mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Quantized

(85)

this model

Dataset used to train mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF

Collection including mrs83/Kurtis-SmolLM2-360M-Instruct-GGUF

Kurtis

Collection

Kurtis is a project focused on supervised fine-tuning of small language model (SLM) on mental-health datasets. • 8 items • Updated Jan 26, 2025