Update README.md

README.md

@@ -121,25 +121,12 @@ Load this model using the `from_pretrained` method:
 from model2vec import StaticModel
 
 # Load a pretrained Model2Vec model
-model = StaticModel.from_pretrained("multilingual-e5-small-distill")
+model = StaticModel.from_pretrained("cnmoro/multilingual-e5-small-distilled-64d")
 
 # Compute text embeddings
 embeddings = model.encode(["Example sentence"])
 ```
 
-Alternatively, you can distill your own model using the `distill` method:
-```python
-from model2vec.distill import distill
-
-# Choose a Sentence Transformer model
-model_name = "BAAI/bge-base-en-v1.5"
-
-# Distill the model
-m2v_model = distill(model_name=model_name, pca_dims=256)
-
-# Save the model
-m2v_model.save_pretrained("m2v_model")
-```
 
 ## How it works