Image-Text-to-Text
Safetensors
GGUF
idefics3
vision-language
card-extraction
mobile-optimized
lora
continual-learning
structured-data
conversational
Eval Results (legacy)
Instructions to use sugiv/cardvaultplus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sugiv/cardvaultplus 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 sugiv/cardvaultplus:F16 # Run inference directly in the terminal: llama cli -hf sugiv/cardvaultplus:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sugiv/cardvaultplus:F16 # Run inference directly in the terminal: llama cli -hf sugiv/cardvaultplus:F16
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:F16 # Run inference directly in the terminal: ./llama-cli -hf sugiv/cardvaultplus:F16
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:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf sugiv/cardvaultplus:F16
Use Docker
docker model run hf.co/sugiv/cardvaultplus:F16
- LM Studio
- Jan
- vLLM
How to use sugiv/cardvaultplus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sugiv/cardvaultplus" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sugiv/cardvaultplus", "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/sugiv/cardvaultplus:F16
- Ollama
How to use sugiv/cardvaultplus with Ollama:
ollama run hf.co/sugiv/cardvaultplus:F16
- Unsloth Studio
How to use sugiv/cardvaultplus 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 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 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 to start chatting
- Docker Model Runner
How to use sugiv/cardvaultplus with Docker Model Runner:
docker model run hf.co/sugiv/cardvaultplus:F16
- Lemonade
How to use sugiv/cardvaultplus with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sugiv/cardvaultplus:F16
Run and chat with the model
lemonade run user.cardvaultplus-F16
List all available models
lemonade list
- Atomic Chat
| #!/usr/bin/env python3 | |
| """ | |
| Upload CardVault+ model to HuggingFace Hub | |
| """ | |
| import os | |
| import sys | |
| from huggingface_hub import HfApi, login | |
| def upload_model(): | |
| # Check for token | |
| token = os.getenv("HF_TOKEN") | |
| if not token: | |
| print("Please set HF_TOKEN environment variable") | |
| print("Get your token from: https://huggingface.co/settings/tokens") | |
| return False | |
| try: | |
| # Login | |
| login(token=token) | |
| print("β Successfully logged in to HuggingFace") | |
| # Initialize API | |
| api = HfApi(token=token) | |
| # Check if repo exists, create if not | |
| try: | |
| api.repo_info(repo_id="sugiv/cardvaultplus", repo_type="model") | |
| print("β Repository exists") | |
| except: | |
| print("π Creating new repository...") | |
| api.create_repo(repo_id="sugiv/cardvaultplus", repo_type="model", private=False) | |
| print("β Repository created") | |
| # Upload folder | |
| print("π Uploading model files (4.2GB)...") | |
| print("This may take 10-15 minutes...") | |
| api.upload_folder( | |
| folder_path=".", | |
| repo_id="sugiv/cardvaultplus", | |
| repo_type="model", | |
| commit_message="Upload production CardVault+ SmolVLM model - 4 epochs trained, 0.000133 validation loss" | |
| ) | |
| print("π Successfully uploaded CardVault+ model!") | |
| print("π Available at: https://huggingface.co/sugiv/cardvaultplus") | |
| return True | |
| except Exception as e: | |
| print(f"β Upload failed: {e}") | |
| return False | |
| if __name__ == "__main__": | |
| success = upload_model() | |
| sys.exit(0 if success else 1) | |