Instructions to use aarabil/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aarabil/embeddinggemma-300m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aarabil/embeddinggemma-300m")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aarabil/embeddinggemma-300m") model = AutoModel.from_pretrained("aarabil/embeddinggemma-300m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54a250b3b7c81482286dfd8f776cbfdfd7e2889cf6c1674679395abd967cdd60
- Size of remote file:
- 33.4 MB
- SHA256:
- 6852f8d561078cc0cebe70ca03c5bfdd0d60a45f9d2e0e1e4cc05b68e9ec329e
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