Image-to-Text
Safetensors
GGUF
English
idefics3
vision-language
credit-card
ocr
json-extraction
cardvault
smolvlm
fine-tuned
conversational
Instructions to use sugiv/cardvaultplus-500m 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-500m 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-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 serve -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/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
- Llama new
- LM Studio
- Jan
- Ollama
How to use sugiv/cardvaultplus-500m with Ollama:
ollama run hf.co/sugiv/cardvaultplus-500m:Q4_K_M
- Unsloth Desktop
- 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
- Atomic Chat
File size: 486 Bytes
c7e86c3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"do_convert_rgb": true,
"do_image_splitting": true,
"do_normalize": true,
"do_pad": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Idefics3ImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_image_size": {
"longest_edge": 512
},
"processor_class": "Idefics3Processor",
"resample": 1,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 2048
}
}
|