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VatsaDev
/
sakura-vl

Image-Text-to-Text
GGUF
English
vision
1_bit
conversational
Model card Files Files and versions
xet
Community

Instructions to use VatsaDev/sakura-vl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • llama-cpp-python

    How to use VatsaDev/sakura-vl with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="VatsaDev/sakura-vl",
    	filename="1b_text_alpha.gguf",
    )
    
    llm.create_chat_completion(
    	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"
    					}
    				}
    			]
    		}
    	]
    )
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use VatsaDev/sakura-vl with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf VatsaDev/sakura-vl
    # Run inference directly in the terminal:
    llama-cli -hf VatsaDev/sakura-vl
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf VatsaDev/sakura-vl
    # Run inference directly in the terminal:
    llama-cli -hf VatsaDev/sakura-vl
    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 VatsaDev/sakura-vl
    # Run inference directly in the terminal:
    ./llama-cli -hf VatsaDev/sakura-vl
    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 VatsaDev/sakura-vl
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf VatsaDev/sakura-vl
    Use Docker
    docker model run hf.co/VatsaDev/sakura-vl
  • LM Studio
  • Jan
  • vLLM

    How to use VatsaDev/sakura-vl with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "VatsaDev/sakura-vl"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "VatsaDev/sakura-vl",
    		"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/VatsaDev/sakura-vl
  • Ollama

    How to use VatsaDev/sakura-vl with Ollama:

    ollama run hf.co/VatsaDev/sakura-vl
  • Unsloth Studio new

    How to use VatsaDev/sakura-vl 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 VatsaDev/sakura-vl 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 VatsaDev/sakura-vl to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for VatsaDev/sakura-vl to start chatting
  • Pi new

    How to use VatsaDev/sakura-vl with Pi:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama-server -hf VatsaDev/sakura-vl
    Configure the model in Pi
    # Install Pi:
    npm install -g @mariozechner/pi-coding-agent
    # Add to ~/.pi/agent/models.json:
    {
      "providers": {
        "llama-cpp": {
          "baseUrl": "http://localhost:8080/v1",
          "api": "openai-completions",
          "apiKey": "none",
          "models": [
            {
              "id": "sakura-vl"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • Docker Model Runner

    How to use VatsaDev/sakura-vl with Docker Model Runner:

    docker model run hf.co/VatsaDev/sakura-vl
  • Lemonade

    How to use VatsaDev/sakura-vl with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull VatsaDev/sakura-vl
    Run and chat with the model
    lemonade run user.sakura-vl-{{QUANT_TAG}}
    List all available models
    lemonade list
sakura-vl
484 MB
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  • 1 contributor
History: 7 commits
VatsaDev's picture
VatsaDev
Rename 4b_vis.gguf to 4b_vis_alpha.gguf
93c8df3 verified 23 days ago
  • .gitattributes
    1.73 kB
    Rename 4b_vis.gguf to 4b_vis_alpha.gguf 23 days ago
  • 1b_text_alpha.gguf
    248 MB
    xet
    Rename 1b_text.gguf to 1b_text_alpha.gguf 23 days ago
  • 4b_vis_alpha.gguf
    236 MB
    xet
    Rename 4b_vis.gguf to 4b_vis_alpha.gguf 23 days ago
  • README.md
    353 Bytes
    Update README.md 23 days ago