Instructions to use minishlab/potion-multilingual-128M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/potion-multilingual-128M with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/potion-multilingual-128M") - sentence-transformers
How to use minishlab/potion-multilingual-128M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/potion-multilingual-128M") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| - dataset: | |
| id: mteb/BRIGHT | |
| task_id: BrightBiologyLongRetrieval_default_long | |
| revision: c26703e6600d97c579ee2985f16cf307db13ed85 | |
| value: 15.372 | |
| date: '2026-03-31' | |
| notes: Obtained using MTEB v2.10.12 | |
| source: | |
| url: https://github.com/embeddings-benchmark/mteb/ | |
| name: Obtained using MTEB v2.10.12 | |
| user: mteb | |
| - dataset: | |
| id: mteb/BRIGHT | |
| task_id: BrightBiologyLongRetrieval | |
| revision: c26703e6600d97c579ee2985f16cf307db13ed85 | |
| value: 15.372 | |
| date: '2026-03-31' | |
| notes: Obtained using MTEB v2.10.12 | |
| source: | |
| url: https://github.com/embeddings-benchmark/mteb/ | |
| name: Obtained using MTEB v2.10.12 | |
| user: mteb | |