Feature Extraction
Transformers
PyTorch
Safetensors
Diffusers
SMI-TED
chemistry
foundation models
AI4Science
materials
molecules
transformer
Instructions to use ibm-research/materials.smi-ted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibm-research/materials.smi-ted with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ibm-research/materials.smi-ted")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ibm-research/materials.smi-ted", dtype="auto") - Diffusers
How to use ibm-research/materials.smi-ted 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-ted", 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
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requirements.txt
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rdkit==2024.3.5
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transformers
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torch-optimizer
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datasets
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scikit-learn
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scipy>=1.12.0
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numpy==1.26.4
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pandas==1.4.0
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tqdm>=4.66.4
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rdkit>=2024.3.5
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