Sentence Similarity
sentence-transformers
Safetensors
English
olmo3
feature-extraction
mteb
olmo
retrieval
Instructions to use petegordon/olmo3-embed-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use petegordon/olmo3-embed-7b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("petegordon/olmo3-embed-7b") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from petegordon/olmo3-embed-7b: direct link, hf CLI and curl.
- Browser
- Download file 7.14 MB
-
https://huggingface.co/petegordon/olmo3-embed-7b/resolve/main/tokenizer.json
- Command line
-
hf download hf://petegordon/olmo3-embed-7b/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/petegordon/olmo3-embed-7b/resolve/main/tokenizer.json
7.14 MB
File too large to display, you can check the raw version instead.