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", 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
Readme - Fix Paper Link
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@@ -28,7 +28,7 @@ Paper: [arXiv:2407.20267](https://arxiv.org/abs/2407.20267)
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This repository provides PyTorch source code associated with our publication, "A Large Encoder-Decoder Family of Foundation Models for Chemical Language".
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Paper: [Arxiv Link](https://github.com/IBM/materials/blob/main/smi-ted/paper/
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We provide the model weights in two formats:
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This repository provides PyTorch source code associated with our publication, "A Large Encoder-Decoder Family of Foundation Models for Chemical Language".
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Paper: [Arxiv Link](https://github.com/IBM/materials/blob/main/smi-ted/paper/smi-ted_preprint.pdf)
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We provide the model weights in two formats:
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