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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ibm-research/materials.smi-ted", device_map="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
Download smi-ted-Light_40.pt from ibm-research/materials.smi-ted: direct link, hf CLI and curl.
- Browser
- Download file 1.16 GB
-
https://huggingface.co/ibm-research/materials.smi-ted/resolve/main/smi-ted-Light_40.pt
- Command line
-
hf download hf://ibm-research/materials.smi-ted/smi-ted-Light_40.pt
-
curl -L -o smi-ted-Light_40.pt https://huggingface.co/ibm-research/materials.smi-ted/resolve/main/smi-ted-Light_40.pt
1.16 GB
- Xet hash:
- 81aecf4ac81d56351b1ec9eb41d1b930e4ee4545aaeee383be8ead27ce97ab00
- Size of remote file:
- 1.16 GB
- SHA256:
- baf252dbc081a00c68d2fd6ed8b08a0db0fa15244cfea442d49f0619a3a65375
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