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ibm-research
/
materials.smi_ssed

Feature Extraction
Transformers
Safetensors
PyTorch
Diffusers
chemistry
foundation models
AI4Science
materials
molecules
transformer
Model card Files Files and versions
xet
Community

Instructions to use ibm-research/materials.smi_ssed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ibm-research/materials.smi_ssed with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="ibm-research/materials.smi_ssed")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("ibm-research/materials.smi_ssed", device_map="auto")
  • Diffusers

    How to use ibm-research/materials.smi_ssed with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("ibm-research/materials.smi_ssed", dtype=torch.bfloat16, device_map="cuda")
    
    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipe(prompt).images[0]
  • Notebooks
  • Google Colab
  • Kaggle
materials.smi_ssed / smi_ssed
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  • inference
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  • notebooks
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  • training
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  • .gitignore
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  • README.md
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  • requirements.txt
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