Instructions to use facebook/esm2_t30_150M_UR50D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/esm2_t30_150M_UR50D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="facebook/esm2_t30_150M_UR50D")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t30_150M_UR50D") model = AutoModelForMaskedLM.from_pretrained("facebook/esm2_t30_150M_UR50D") - Inference
- Notebooks
- Google Colab
- Kaggle
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#5 opened 3 days ago
by
vigneshwar234
[AUTOMATED] Model Memory Requirements
#3 opened over 2 years ago
by
model-sizer-bot