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| Metric | Original | Trimmed | Reduction |
|---|---|---|---|
| Vocabulary size | 250,002 tokens | 16,384 tokens | 93.45% |
| Model size | 278,043,648 params | 98,625,024 params | 64.53% |

1from sentence_transformers import SentenceTransformer
2# Download from the 🤗 Hub
3model = SentenceTransformer("alphaedge-ai/granite-embedding-278m-spa-16384")
4# Run inference with queries and documents
5query = "My query in Spanish"
6documents = [
7 "Chunk in Spanish",
8 "Chunk in Spanish",
9 "Chunk in Spanish",
10]
11query_embeddings = model.encode_query(query)
12document_embeddings = model.encode_document(documents)
13print(query_embeddings.shape, document_embeddings.shape)
14# Compute similarities to determine a ranking
15similarities = model.similarity(query_embeddings, document_embeddings)
16print(similarities)@misc{awasthy2025graniteembeddingmodels,
title={Granite Embedding Models},
author={Parul Awasthy and Aashka Trivedi and Yulong Li and Mihaela Bornea and David Cox and Abraham Daniels and Martin Franz and Gabe Goodhart and Bhavani Iyer and Vishwajeet Kumar and Luis Lastras and Scott McCarley and Rudra Murthy and Vignesh P and Sara Rosenthal and Salim Roukos and Jaydeep Sen and Sukriti Sharma and Avirup Sil and Kate Soule and Arafat Sultan and Radu Florian},
year={2025},
eprint={2502.20204},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2502.20204},
}@misc{hf_blogpost_trimming,
title={Introduction to Trimming},
author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},
year={2026},
url={https://huggingface.co/blog/lbourdois/introduction-to-trimming},
}