Sentence Similarity
Transformers
Safetensors
embedding_gemma2
feature-extraction
embeddinggemma
quantization
post-training-quantization
int4
4-bit precision
text-embeddings
semantic-search
retrieval
low-memory
cpu
8-bit precision
Instructions to use ewin-reg/embeddinggemma-2-0.2B-Quantized-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ewin-reg/embeddinggemma-2-0.2B-Quantized-text with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("ewin-reg/embeddinggemma-2-0.2B-Quantized-text") model = AutoModel.from_pretrained("ewin-reg/embeddinggemma-2-0.2B-Quantized-text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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