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This is the first model developed by QTACK and serves as a proof of concept for our distillation approach! Built upon a distillbert-based architecture, Mini-GTE is distilled from GTE and designed for efficiency without sacrificing accuracy at only 66M parameters. As a standalone sentence transformer, it ranks 2nd on the MTEB classic leaderboard in the <100M parameter category and 63rd overall which makes it a strong choice for real-time query encoding, semantic search, and similarity tasks.
Mini-GTE is built on the Sentence Transformers framework. To install the required packages, run:
pip install -U sentence-transformers
Here's a quick example to get you started:
from sentence_transformers import SentenceTransformer
# Download directly from Hugging Face
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape) # Expected: [3, 768]
# Compute the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape) # Expected: [3, 3]
For any questions, suggestions, or issues, please contact the QTACK team directly through our contact page.
distilbert/distilbert-base-uncased