Feature Extraction
Transformers
Safetensors
aether_rami_moe
biology
chemistry
drug-discovery
ai-drug-discovery
computational-biology
esm2
diffsbdd
structure-based-drug-design
mixture-of-experts
Mixture of Experts
16b
flow-matching
kras-g12c
kras-g12d
tuberculosis
malaria
sickle-cell
pharmacopeia
posebusters
crossdocked2020
custom_code
Instructions to use Premchan369/aether-rami-esm2-diffsbdd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Premchan369/aether-rami-esm2-diffsbdd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Premchan369/aether-rami-esm2-diffsbdd", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Premchan369/aether-rami-esm2-diffsbdd", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Today
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Deploy 16B MoE architecture with Foldseek RAG and Autonomous Swarm
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Yesterday
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Release AETHER-RAMI v4.2.0-Omega Model Weights & Preclinical PLM Encoders
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