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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| Num. of parameters | ≈1B |
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| Training tokens | ≈1T |
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|Loss function |MLM + In-Context loss|
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## Licence
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The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
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| Num. of parameters | ≈1B |
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| Training tokens | ≈1T |
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|Loss function |MLM + In-Context loss|
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|Multi-layer loss | yes |
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## Licence
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The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
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