Instructions to use jc-builds/smolvlm2-500m-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jc-builds/smolvlm2-500m-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jc-builds/smolvlm2-500m-gguf",
	filename="SmolVLM2-500M-Video-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 jc-builds/smolvlm2-500m-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/smolvlm2-500m-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf jc-builds/smolvlm2-500m-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jc-builds/smolvlm2-500m-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf jc-builds/smolvlm2-500m-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 jc-builds/smolvlm2-500m-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf jc-builds/smolvlm2-500m-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 jc-builds/smolvlm2-500m-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jc-builds/smolvlm2-500m-gguf:Q8_0

Use Docker

docker model run hf.co/jc-builds/smolvlm2-500m-gguf:Q8_0

LM Studio
Jan

vLLM

How to use jc-builds/smolvlm2-500m-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jc-builds/smolvlm2-500m-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": "jc-builds/smolvlm2-500m-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/jc-builds/smolvlm2-500m-gguf:Q8_0

Ollama
How to use jc-builds/smolvlm2-500m-gguf with Ollama:
```
ollama run hf.co/jc-builds/smolvlm2-500m-gguf:Q8_0
```

Unsloth Studio

How to use jc-builds/smolvlm2-500m-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/smolvlm2-500m-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/smolvlm2-500m-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/smolvlm2-500m-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use jc-builds/smolvlm2-500m-gguf with Docker Model Runner:
```
docker model run hf.co/jc-builds/smolvlm2-500m-gguf:Q8_0
```

Lemonade

How to use jc-builds/smolvlm2-500m-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jc-builds/smolvlm2-500m-gguf:Q8_0

Run and chat with the model

lemonade run user.smolvlm2-500m-gguf-Q8_0

List all available models

lemonade list

SmolVLM2-500M GGUF

This is a GGUF conversion of HuggingFaceTB/SmolVLM2-500M-Video-Instruct - a compact Vision-Language Model optimized for on-device inference with llama.cpp.

Model Details

Property	Value
Original Model	SmolVLM2-500M-Video-Instruct
Parameters	500 million
Quantization	Q8_0
Model Size	~437 MB
Vision Encoder Size	~199 MB (F16)
Context Window	8,192 tokens
Architecture	SmolVLM2 with SigLIP vision encoder

Files

SmolVLM2-500M-Video-Instruct-Q8_0.gguf - Main language model
mmproj-SmolVLM2-500M-Video-Instruct-f16.gguf - Vision encoder (mmproj)

Intended Use

This model is optimized for:

Mobile/Edge Deployment: Runs efficiently on all iOS devices
llama.cpp Integration: Compatible with llama.cpp vision features
On-Device AI: Private, offline image understanding with minimal resources

Capabilities

Image Captioning: Describe images accurately
Visual Q&A: Answer questions about images
Document Extraction: Extract text from photos
Scene Understanding: Analyze visual content
Fast Inference: 15-20 tokens/sec on iPhone 15 Pro

Usage with llama.cpp

./llama-llava-cli -m SmolVLM2-500M-Video-Instruct-Q8_0.gguf \
  --mmproj mmproj-SmolVLM2-500M-Video-Instruct-f16.gguf \
  --image your_image.jpg \
  -p "Describe this image"