Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by a factor up to 50 and making the models up to 500 times faster, with a small drop in performance. Our best model is the most performant static embedding model in the world. See our results here, or dive in to see how it works.
Install the lightweight base package with:
You can start using Model2Vec by loading one of our flagship models from the HuggingFace hub. These models are pre-trained and ready to use. The following code snippet shows how to load a model and make embeddings, which you can use for any task, such as text classification, retrieval, clustering, or building a RAG system:
from model2vec import StaticModel
# Load a model from the HuggingFace hub (in this case the potion-base-8M model)
model = StaticModel.from_pretrained("minishlab/potion-base-8M")
# Make embeddings
embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."])
# Make sequences of token embeddings
token_embeddings = model.encode_as_sequence(["It's dangerous to go alone!", "It's a secret to everybody."])
Instead of using one of our models, you can also distill your own Model2Vec model from a Sentence Transformer model. First, install the distillation extras with:
pip install model2vec[distill]
Then, you can distill a model in ~30 seconds on a CPU with the following code snippet:
from model2vec.distill import distill
# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
# Save the model
m2v_model.save_pretrained("m2v_model")
After distillation, you can also fine-tune your own classification models on top of the distilled model, or on a pre-trained model. First, make sure you install the training extras with:
pip install model2vec[train]
Then, you can fine-tune a model as follows:
import numpy as np
from datasets import load_dataset
from model2vec.train import StaticModelForClassification
# Initialize a classifier from a pre-trained model
classifier = StaticModelForClassification.from_pretrained(model_name="minishlab/potion-base-32M")
# Load a dataset. Note: both single and multi-label classification datasets are supported
ds = load_dataset("setfit/subj")
# Train the classifier on text (X) and labels (y)
classifier.fit(ds["train"]["text"], ds["train"]["label"])
# Evaluate the classifier
classification_report = classifier.evaluate(ds["test"]["text"], ds["test"]["label"])
For advanced usage, please refer to our usage documentation.
23/05/2025: We released potion-multilingual-128M, a multilingual model trained on 101 languages. It is the best performing static embedding model for multilingual tasks, and is capable of generating embeddings for any text in any language. The results can be found in our results section.
01/05/2025: We released backend support for BPE and Unigram tokenizers, along with quantization and dimensionality reduction. New Model2Vec models are now 50% of the original models, and can be quantized to int8 to be 25% of the size, without loss of performance.
12/02/2025: We released Model2Vec training, allowing you to fine-tune your own classification models on top of Model2Vec models. Find out more in our training documentation and results.
30/01/2025: We released two new models: potion-base-32M and potion-retrieval-32M. potion-base-32M is our most performant model to date, using a larger vocabulary and higher dimensions. potion-retrieval-32M is a finetune of potion-base-32M that is optimized for retrieval tasks, and is the best performing static retrieval model currently available.
30/10/2024: We released three new models: potion-base-8M, potion-base-4M, and potion-base-2M. These models are trained using Tokenlearn. Find out more in our blog post. NOTE: for users of any of our old English M2V models, we recommend switching to these new models as they perform better on all tasks.
numpy.from_pretrained and push_to_hub. Our own models can be found here.Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Like BPEmb, it can create subword embeddings, but with much better performance. Distillation doesn't need any data, just a vocabulary and a model.
The core idea is to forward pass a vocabulary through a sentence transformer model, creating static embeddings for the indiviudal tokens. After this, there are a number of post-processing steps we do that results in our best models. For a more extensive deepdive, please refer to the following resources:
Our official documentation can be found here. This includes:
We provide a number of models that can be used out of the box. These models are available on the HuggingFace hub and can be loaded using the from_pretrained method. The models are listed below.
We have performed extensive experiments to evaluate the performance of Model2Vec models. The results are documented in the results folder. The results are presented in the following sections:
MIT
If you use Model2Vec in your research, please cite the following:
@article{minishlab2024model2vec,
author = {Tulkens, Stephan and {van Dongen}, Thomas},
title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
year = {2024},
url = {https://github.com/MinishLab/model2vec}
}
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