Instructions to use matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF", dtype="auto")

llama-cpp-python

How to use matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF",
	filename="SmolLM2-1.7B-Instruct-Matt-Shill-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-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 matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16

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 matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16

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 matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16

Use Docker

docker model run hf.co/matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16

LM Studio
Jan

vLLM

How to use matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-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": "matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16

SGLang

How to use matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-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 "matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-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": "matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-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 "matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-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": "matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF with Ollama:
```
ollama run hf.co/matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16
```

Unsloth Studio

How to use matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-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 matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-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 matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF with Docker Model Runner:
```
docker model run hf.co/matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16
```

Lemonade

How to use matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill-GGUF:F16

Run and chat with the model

lemonade run user.SmolLM2-1.7B-Instruct-Matt-Shill-GGUF-F16

List all available models

lemonade list

Model Card for SmolLM2-1.7B-Instruct-Matt-Shill-GGUF

This model is GGUF quanitzed version of matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill which is a fine-tuned version of HuggingFaceTB/SmolLM2-1.7B-Instruct. It has been trained using TRL and quantized using llama.cpp.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="matthewhaynesonline/SmolLM2-1.7B-Instruct-Matt-Shill", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.13.0
Transformers: 4.48.1
Pytorch: 2.7.0.dev20250125
Datasets: 3.2.0
Tokenizers: 0.21.0

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}