Instructions to use baseten/DummyGemmaTextModelForEmbedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baseten/DummyGemmaTextModelForEmbedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="baseten/DummyGemmaTextModelForEmbedding")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("baseten/DummyGemmaTextModelForEmbedding") model = AutoModel.from_pretrained("baseten/DummyGemmaTextModelForEmbedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a98f618c3b5575c5257e3aec35ead72e93ceb9fe240a2bff72dfa802ed29f9d
- Size of remote file:
- 1.07 GB
- SHA256:
- 41062c485a26efca0a9ee5bac1d1801afc96317101afc0dd32b6cd1eae196926
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