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

Libraries

How to use sugiv/cardvaultplus-500m-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sugiv/cardvaultplus-500m-gguf",
	filename="cardvault-500m-f16.gguf",
)

llm.create_chat_completion(
	messages = "\"cats.jpg\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sugiv/cardvaultplus-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 sugiv/cardvaultplus-500m-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sugiv/cardvaultplus-500m-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 sugiv/cardvaultplus-500m-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sugiv/cardvaultplus-500m-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 sugiv/cardvaultplus-500m-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sugiv/cardvaultplus-500m-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 sugiv/cardvaultplus-500m-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sugiv/cardvaultplus-500m-gguf:Q4_K_M

Use Docker

docker model run hf.co/sugiv/cardvaultplus-500m-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use sugiv/cardvaultplus-500m-gguf with Ollama:
```
ollama run hf.co/sugiv/cardvaultplus-500m-gguf:Q4_K_M
```

Unsloth Studio new

How to use sugiv/cardvaultplus-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 sugiv/cardvaultplus-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 sugiv/cardvaultplus-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 sugiv/cardvaultplus-500m-gguf to start chatting

Docker Model Runner
How to use sugiv/cardvaultplus-500m-gguf with Docker Model Runner:
```
docker model run hf.co/sugiv/cardvaultplus-500m-gguf:Q4_K_M
```

Lemonade

How to use sugiv/cardvaultplus-500m-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sugiv/cardvaultplus-500m-gguf:Q4_K_M

Run and chat with the model

lemonade run user.cardvaultplus-500m-gguf-Q4_K_M

List all available models

lemonade list

CardVault+ SmolVLM-500M GGUF Models

Available Models

Model File	Size	Quantization	Use Case
`cardvault-500m-f16.gguf`	783MB	F16 (Base)	Maximum quality
`cardvault-500m-mmproj-f16.gguf`	191MB	F16 (Vision)	REQUIRED
`cardvault-500m-q8_0.gguf`	417MB	Q8_0	Near-perfect quality
`cardvault-500m-q6_k.gguf`	399MB	Q6_K	Balanced
`cardvault-500m-q5_k_m.gguf`	311MB	Q5_K_M	Recommended
`cardvault-500m-q4_k_m.gguf`	290MB	Q4_K_M	Maximum compression

Usage

# Download llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make

# Run inference (Q5_K_M recommended)
./main \
  --model cardvault-500m-q5_k_m.gguf \
  --mmproj cardvault-500m-mmproj-f16.gguf \
  --image credit_card.jpg \
  --prompt "Extract card information in JSON format"

⚠️ Two-Component Architecture: Both text model + mmproj required!