potion-multilingual-128M EU AI Act Compliance Profile

potion-multilingual-128M Model Card

This Model2Vec model is pre-trained using Tokenlearn on all languages in the C4 dataset. It is a distilled version of the BAAI/bge-m3 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical.

potion-multilingual-128M is a multilingual model, trained on 101 languages, and is capable of generating embeddings for any text in any language. The model produces 256 dimensional embeddings, and has a theoretically unlimited context length since embeddings are static (pre-computed).

Installation

Install model2vec using pip:

Usage

Using Model2Vec

The Model2Vec library is the fastest and most lightweight way to run Model2Vec models.

Load this model using the from_pretrained method:

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("minishlab/potion-multilingual-128M")

# Compute text embeddings
embeddings = model.encode(["Example sentence"])

How it works

Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on MTEB. This model is pre-trained using Tokenlearn. It's created using the following steps:

• Distillation: first, a model is distilled from a sentence transformer model using Model2Vec.
• Training data creation: the sentence transformer model is used to create training data by creating me

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