potion retrieval 32M EU AI Act Compliance Profile

potion-retrieval-32M Model Card

This Model2Vec model is optmized for retrieval tasks. It is a finetune of potion-base-32M. It's finetuned using a modified version of the training approach described in this blogpost. 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.

Installation

Install model2vec using pip:

Usage

Load this model using the from_pretrained method:

from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("minishlab/potion-retrieval-32M")
# 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 mean output embeddings on a large corpus.
• Training: the distilled model is trained on the training data using Tokenlearn.
• Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying SIF weighting.

The results for this model can be found on the [Mo

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Tags

Library

Base Model