Image-Text-to-Text
GGUF
Spanish
English
llama.cpp
vision-language-model
multimodal
cybersecurity
spanish
latam
experimental
conversational
Instructions to use jsantillana/vectrayx-vision-1b-qwen-experimental 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 jsantillana/vectrayx-vision-1b-qwen-experimental 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 jsantillana/vectrayx-vision-1b-qwen-experimental # Run inference directly in the terminal: llama cli -hf jsantillana/vectrayx-vision-1b-qwen-experimental
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jsantillana/vectrayx-vision-1b-qwen-experimental # Run inference directly in the terminal: llama cli -hf jsantillana/vectrayx-vision-1b-qwen-experimental
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 jsantillana/vectrayx-vision-1b-qwen-experimental # Run inference directly in the terminal: ./llama-cli -hf jsantillana/vectrayx-vision-1b-qwen-experimental
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 jsantillana/vectrayx-vision-1b-qwen-experimental # Run inference directly in the terminal: ./build/bin/llama-cli -hf jsantillana/vectrayx-vision-1b-qwen-experimental
Use Docker
docker model run hf.co/jsantillana/vectrayx-vision-1b-qwen-experimental
- LM Studio
- Jan
- vLLM
How to use jsantillana/vectrayx-vision-1b-qwen-experimental with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jsantillana/vectrayx-vision-1b-qwen-experimental" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jsantillana/vectrayx-vision-1b-qwen-experimental", "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/jsantillana/vectrayx-vision-1b-qwen-experimental
- Ollama
How to use jsantillana/vectrayx-vision-1b-qwen-experimental with Ollama:
ollama run hf.co/jsantillana/vectrayx-vision-1b-qwen-experimental
- Unsloth Desktop
- Docker Model Runner
How to use jsantillana/vectrayx-vision-1b-qwen-experimental with Docker Model Runner:
docker model run hf.co/jsantillana/vectrayx-vision-1b-qwen-experimental
- Lemonade
How to use jsantillana/vectrayx-vision-1b-qwen-experimental with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jsantillana/vectrayx-vision-1b-qwen-experimental
Run and chat with the model
lemonade run user.vectrayx-vision-1b-qwen-experimental-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
