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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use worthdoing/SmolLM2-1.7B-Instruct-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 worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf worthdoing/SmolLM2-1.7B-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 worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf worthdoing/SmolLM2-1.7B-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 worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use worthdoing/SmolLM2-1.7B-Instruct-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M

Ollama
How to use worthdoing/SmolLM2-1.7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use worthdoing/SmolLM2-1.7B-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 worthdoing/SmolLM2-1.7B-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 worthdoing/SmolLM2-1.7B-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 worthdoing/SmolLM2-1.7B-Instruct-GGUF to start chatting

Docker Model Runner
How to use worthdoing/SmolLM2-1.7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use worthdoing/SmolLM2-1.7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull worthdoing/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SmolLM2-1.7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

spb-wd commited on Apr 17

Commit

794b0d8

verified ·

1 Parent(s): 040ff4e

Upload README.md with huggingface_hub

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README.md +13 -0

README.md CHANGED Viewed

@@ -21,6 +21,19 @@ pipeline_tag: text-generation
 </p>
 <p align="center"><strong>Author: Simon-Pierre Boucher</strong></p>
 # SmolLM2-1.7B-Instruct - GGUF Quantized by worthdoing
 > Quantized for local Mac inference (Apple Silicon / Metal) by **worthdoing**

 </p>
 <p align="center"><strong>Author: Simon-Pierre Boucher</strong></p>
+<p align="center">
+  <img src="https://img.shields.io/badge/Format-GGUF-blue?style=for-the-badge" alt="GGUF"/>
+  <img src="https://img.shields.io/badge/Params-1.7B-orange?style=for-the-badge" alt="Parameters"/>
+  <img src="https://img.shields.io/badge/Platform-Apple_Silicon-black?style=for-the-badge&logo=apple" alt="Apple Silicon"/>
+  <img src="https://img.shields.io/badge/License-Apache_2.0-green?style=for-the-badge" alt="License"/>
+  <img src="https://img.shields.io/badge/Quantized_by-worthdoing-purple?style=for-the-badge" alt="worthdoing"/>
+</p>
+<p align="center">
+  <img src="https://img.shields.io/badge/Q4__K__M-0.9_GB-brightgreen?style=flat-square" alt="Q4_K_M"/>
+  <img src="https://img.shields.io/badge/Q5__K__M-1.1_GB-yellow?style=flat-square" alt="Q5_K_M"/>
+  <img src="https://img.shields.io/badge/Q8__0-1.6_GB-red?style=flat-square" alt="Q8_0"/>
+</p>
 # SmolLM2-1.7B-Instruct - GGUF Quantized by worthdoing
 > Quantized for local Mac inference (Apple Silicon / Metal) by **worthdoing**