Instructions to use fernandotonon/QtMeshEditor-smolvlm-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fernandotonon/QtMeshEditor-smolvlm-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="fernandotonon/QtMeshEditor-smolvlm-gguf",
	filename="SmolVLM-500M-Instruct-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fernandotonon/QtMeshEditor-smolvlm-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 fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0

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 fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0

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 fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0

Use Docker

docker model run hf.co/fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0

LM Studio
Jan

vLLM

How to use fernandotonon/QtMeshEditor-smolvlm-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fernandotonon/QtMeshEditor-smolvlm-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": "fernandotonon/QtMeshEditor-smolvlm-gguf",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0

Ollama
How to use fernandotonon/QtMeshEditor-smolvlm-gguf with Ollama:
```
ollama run hf.co/fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0
```

Unsloth Studio

How to use fernandotonon/QtMeshEditor-smolvlm-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 fernandotonon/QtMeshEditor-smolvlm-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 fernandotonon/QtMeshEditor-smolvlm-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fernandotonon/QtMeshEditor-smolvlm-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use fernandotonon/QtMeshEditor-smolvlm-gguf with Docker Model Runner:
```
docker model run hf.co/fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0
```

Lemonade

How to use fernandotonon/QtMeshEditor-smolvlm-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fernandotonon/QtMeshEditor-smolvlm-gguf:Q8_0

Run and chat with the model

lemonade run user.QtMeshEditor-smolvlm-gguf-Q8_0

List all available models

lemonade list

SmolVLM-500M-Instruct — Q8_0 GGUF

Quantized HuggingFaceTB/SmolVLM-500M-Instruct (Apache-2.0) as a Q8_0 GGUF + vision projector for llama.cpp inference. All credit for the model goes to the Hugging Face TB team.

Packaged for QtMeshEditor's local image-captioning feature (llama.cpp runs in-process; the model downloads on first use and runs offline).

The files QtMeshEditor downloads at runtime live in the shared fernandotonon/QtMeshEditor-models repo under caption/. This repo is the standalone model card + mirror.

Files

file	role
`SmolVLM-500M-Instruct-Q8_0.gguf`	language model, Q8_0 (~437 MB)
`mmproj-SmolVLM-500M-Instruct-Q8_0.gguf`	vision projector (~109 MB)

Use with llama.cpp's multimodal API (load the mmproj alongside the LM, e.g. llama-mtmd-cli -m SmolVLM-500M-Instruct-Q8_0.gguf --mmproj mmproj-SmolVLM-500M-Instruct-Q8_0.gguf).

License

Apache-2.0 (same as upstream). Credit: Hugging Face TB (SmolVLM).

Downloads last month: -

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

8-bit