Instructions to use modularStarEncoder/ModularStarEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modularStarEncoder/ModularStarEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="modularStarEncoder/ModularStarEncoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("modularStarEncoder/ModularStarEncoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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Finally, our implementation integrates FlashAttention V2 for faster inference.
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- **Paper:** [
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- **Languages:** 600+ Programming languages
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Finally, our implementation integrates FlashAttention V2 for faster inference.
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- **Paper:** [One Model to Train them All: Hierarchical Self-Distillation for Enhanced Early Layer Embeddings](https://arxiv.org/abs/2503.03008)
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- **Languages:** 600+ Programming languages
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