Instructions to use minishlab/potion-science-32M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/potion-science-32M with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/potion-science-32M") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use minishlab/potion-science-32M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/potion-science-32M") 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
Download config.json from minishlab/potion-science-32M: direct link, hf CLI and curl.
- Browser
- Download file 203 Bytes
-
https://huggingface.co/minishlab/potion-science-32M/resolve/main/config.json
- Command line
-
hf download hf://minishlab/potion-science-32M/config.json
-
curl -L -o config.json https://huggingface.co/minishlab/potion-science-32M/resolve/main/config.json
203 Bytes
| {"model_type": "model2vec", "architectures": ["StaticModel"], "tokenizer_name": "baai/bge-base-en-v1.5", "apply_pca": 256, "apply_zipf": true, "hidden_dim": 256, "seq_length": 1000000, "normalize": true} |