​
Loading a Model

To load a pre-trained model, you can use the Model2Vec.from_pretrained method. This method allows you to load models from the Hugging Face Hub or from a local path. 
from model2vec import Model2Vec

model = Model2Vec.from_pretrained("minishlab/potion-base-8M")

​
Creating Mean Embeddings

To create mean embeddings, you can use the encode method of the Model2Vec class. This method allows you to encode a list of sentences into mean embeddings. 
from model2vec import Model2Vec

model = Model2Vec.from_pretrained("minishlab/potion-base-8M")
embeddings = model.encode(["Hello world", "Static embeddings are great!"])

​
Creating Sequence Embeddings

To create sequence embeddings, you can use the encode_as_sequence method of the Model2Vec class. This method allows you to encode a list of sentences into sequence embeddings, which are useful for tasks where you need a single embedding per token. 
from model2vec import Model2Vec
model = Model2Vec.from_pretrained("minishlab/potion-base-8M")
embeddings = model.encode_as_sequence(["Hello world", "Static embeddings are great!"], mode="sequence")