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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sugiv/cardvaultplus-500m",
	filename="GGUF/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 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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sugiv/cardvaultplus-500m: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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sugiv/cardvaultplus-500m: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:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sugiv/cardvaultplus-500m: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:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sugiv/cardvaultplus-500m:Q4_K_M

Use Docker

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

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

Unsloth Studio new

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.cardvaultplus-500m-Q4_K_M

List all available models

lemonade list

CardVault+ SmolVLM-500M Fine-tuned Model

Model Description

CardVault+ is a specialized fine-tuned version of HuggingFaceTB/SmolVLM-500M-Instruct, optimized for credit card information extraction. This model can accurately extract structured data from credit card images in JSON format.

Key Features

Vision-Language Processing: Processes credit card images and extracts text
Structured Output: Returns data in consistent JSON format
High Accuracy: Fine-tuned on 9,612 synthetic credit card images
Production Ready: Optimized for real-world deployment

Training Details

Base Model: HuggingFaceTB/SmolVLM-500M-Instruct
Training Method: LoRA (Low-Rank Adaptation) fine-tuning
Training Data: 9,612 synthetic credit card images
Training Epochs: 4
Final Validation Loss: 0.000056
Training Date: July 22, 2025

Usage

from transformers import AutoProcessor, Idefics3ForConditionalGeneration
from PIL import Image
import torch

# Load model and processor
model = Idefics3ForConditionalGeneration.from_pretrained(
    "sugiv/cardvaultplus-500m",
    torch_dtype=torch.float16,
    trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(
    "sugiv/cardvaultplus-500m", 
    trust_remote_code=True
)

# Process credit card image
image = Image.open("credit_card.jpg")
prompt = '''<|im_start|>user
Extract the card information from this image in the following JSON format:
{
    "card_number": "XXXX XXXX XXXX XXXX",
    "cardholder_name": "FULL NAME", 
    "expiry_date": "MM/YY",
    "cvv": "XXX",
    "card_type": "VISA/MASTERCARD/AMEX/DISCOVER"
}

<image>
<|im_end|>
<|im_start|>assistant
'''

inputs = processor(images=image, text=prompt, return_tensors="pt")
with torch.no_grad():
    generated_ids = model.generate(**inputs, max_new_tokens=200)
output = processor.decode(generated_ids[0], skip_special_tokens=True)

Expected Output Format

{
    "card_number": "4532 1234 5678 9012",
    "cardholder_name": "JOHN DOE",
    "expiry_date": "12/27", 
    "cvv": "123",
    "card_type": "VISA"
}