lbourdois/fineweb-2-trimming
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How to use alphaedge-ai/granite-embedding-107m-nld-16384 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("alphaedge-ai/granite-embedding-107m-nld-16384")
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]This model is a 83.84% smaller version of ibm-granite/granite-embedding-107m-multilingual optimized for Dutch language via vocabulary size reduction using the trimming method.
This trimmed model should perform similarly to the original model with only 16,384 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.
| Metric | Original | Trimmed | Reduction |
|---|---|---|---|
| Vocabulary size | 250,002 tokens | 16,384 tokens | 93.45% |
| Model size | 106,994,304 params | 17,284,992 params | 83.84% |
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("alphaedge-ai/granite-embedding-107m-nld-16384")
# Run inference with queries and documents
query = "My query in Dutch"
documents = [
"Chunk in Dutch",
"Chunk in Dutch",
"Chunk in Dutch",
]
query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# Compute similarities to determine a ranking
similarities = model.similarity(query_embeddings, document_embeddings)
print(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},
}