Instructions to use flipbitsnotburgers/m2v-embeddinggemma-european with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use flipbitsnotburgers/m2v-embeddinggemma-european with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("flipbitsnotburgers/m2v-embeddinggemma-european") - Notebooks
- Google Colab
- Kaggle
m2v-embeddinggemma-european
A Model2Vec static embedding model distilled from google/embeddinggemma-300m, pruned to European languages only.
What is this?
- Distilled EmbeddingGemma (308M param encoder, based on Gemma 3) into a static token embedding lookup table
- Pruned all non-European script tokens (CJK, Arabic, Hebrew, Thai, Devanagari, Korean, Japanese, etc.)
Stats
| Before pruning | After pruning | |
|---|---|---|
| Vocabulary | 255,732 tokens | 177,926 tokens |
| Model size | ~127 MB | ~87 MB |
| Embedding dim | 256 | 256 |
30.4% of tokens were removed (non-European scripts).
License
Subject to the Gemma Terms of Use.
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Model tree for flipbitsnotburgers/m2v-embeddinggemma-european
Base model
google/embeddinggemma-300m