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:
- f6aa6b6d77b66c0b5905517df9e5261c32195192839dd05e71e37d6b89933074
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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