Instructions to use tiny-random/embeddinggemma-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiny-random/embeddinggemma-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tiny-random/embeddinggemma-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("tiny-random/embeddinggemma-2") model = AutoModel.from_pretrained("tiny-random/embeddinggemma-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from tiny-random/embeddinggemma-2: direct link, hf CLI and curl.
- Browser
- Download file 12 MB
-
https://huggingface.co/tiny-random/embeddinggemma-2/resolve/main/model.safetensors
- Command line
-
hf download hf://tiny-random/embeddinggemma-2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/tiny-random/embeddinggemma-2/resolve/main/model.safetensors
12 MB
- Xet hash:
- af69dd6637fef626f09bc34fce7fb55cd9fa91fb8cc95c5befb1b07ec8cf0d57
- Size of remote file:
- 12 MB
- SHA256:
- aea7d54f9a3b5ad9241a54b0902fe8db48f5e849202a6b30f2e72757c43dd87e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.