Instructions to use alikia2x/potion-multilingual-128M-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use alikia2x/potion-multilingual-128M-int8 with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("alikia2x/potion-multilingual-128M-int8") - sentence-transformers
How to use alikia2x/potion-multilingual-128M-int8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("alikia2x/potion-multilingual-128M-int8") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
potion-multilingual-128M-int8 Model Card
This model is a quantized version of a minishlab/potion-multilingual-128M.
Note that the model.safetensors was not quantized, quantization was only applied to ONNX format.
For more details, check the original model repo.
Library Authors
Model2Vec was developed by the Minish Lab team consisting of Stephan Tulkens and Thomas van Dongen.
Citation
Please cite the Model2Vec repository if you use this model in your work.
@software{minishlab2024model2vec,
author = {Stephan Tulkens and {van Dongen}, Thomas},
title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
year = {2024},
publisher = {Zenodo},
doi = {10.5281/zenodo.17270888},
url = {https://github.com/MinishLab/model2vec},
license = {MIT}
}
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