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vantagewithai
/
Sulphur-2-Base-Split

Image-to-Video
Diffusers
Safetensors
GGUF
text-to-video
video-to-video
image-text-to-video
audio-to-video
text-to-audio
video-to-audio
audio-to-audio
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
ltx-2-3
sulphur
sulphur-2
conversational
Model card Files Files and versions
xet
Community

Instructions to use vantagewithai/Sulphur-2-Base-Split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use vantagewithai/Sulphur-2-Base-Split with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    from diffusers.utils import load_image, export_to_video
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("vantagewithai/Sulphur-2-Base-Split", dtype=torch.bfloat16, device_map="cuda")
    pipe.to("cuda")
    
    prompt = "A man with short gray hair plays a red electric guitar."
    image = load_image(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png"
    )
    
    output = pipe(image=image, prompt=prompt).frames[0]
    export_to_video(output, "output.mp4")
  • llama-cpp-python

    How to use vantagewithai/Sulphur-2-Base-Split with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="vantagewithai/Sulphur-2-Base-Split",
    	filename="text_encoder/Qwen3.5-sulphur_prompt_enhancer-Q8_0.gguf",
    )
    
    llm.create_chat_completion(
    	messages = "{\n    \"image\": \"cat.png\",\n    \"prompt\": \"The cat starts to dance\"\n}"
    )
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use vantagewithai/Sulphur-2-Base-Split with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
    # Run inference directly in the terminal:
    llama-cli -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
    # Run inference directly in the terminal:
    llama-cli -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
    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 vantagewithai/Sulphur-2-Base-Split:Q8_0
    # Run inference directly in the terminal:
    ./llama-cli -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
    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 vantagewithai/Sulphur-2-Base-Split:Q8_0
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
    Use Docker
    docker model run hf.co/vantagewithai/Sulphur-2-Base-Split:Q8_0
  • LM Studio
  • Jan
  • Ollama

    How to use vantagewithai/Sulphur-2-Base-Split with Ollama:

    ollama run hf.co/vantagewithai/Sulphur-2-Base-Split:Q8_0
  • Unsloth Studio new

    How to use vantagewithai/Sulphur-2-Base-Split 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 vantagewithai/Sulphur-2-Base-Split 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 vantagewithai/Sulphur-2-Base-Split to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for vantagewithai/Sulphur-2-Base-Split to start chatting
  • Pi new

    How to use vantagewithai/Sulphur-2-Base-Split with Pi:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama-server -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
    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": "Sulphur-2-Base-Split"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • Docker Model Runner

    How to use vantagewithai/Sulphur-2-Base-Split with Docker Model Runner:

    docker model run hf.co/vantagewithai/Sulphur-2-Base-Split:Q8_0
  • Lemonade

    How to use vantagewithai/Sulphur-2-Base-Split with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull vantagewithai/Sulphur-2-Base-Split:Q8_0
    Run and chat with the model
    lemonade run user.Sulphur-2-Base-Split-Q8_0
    List all available models
    lemonade list
Sulphur-2-Base-Split / model
109 GB
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  • 1 contributor
History: 3 commits
vantagewithai's picture
vantagewithai
Upload model/sulphur_distil_bf16_model.safetensors with huggingface_hub
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  • sulphur_dev_bf16_model.safetensors
    42 GB
    xet
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    25 GB
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  • sulphur_distil_bf16_model.safetensors
    42 GB
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