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Sponsored by
APIMaster
alphaedge-ai / Qwen3-Embedding-eus-32768
Model's Last Updated: May 20 2026
sentence-similarity
Introduction of Qwen3-Embedding-eus-32768
Model Details of Qwen3-Embedding-eus-32768
Qwen3-Embedding-eus-32768
This model is a
20.44% smaller
version of
Qwen/Qwen3-Embedding-0.6B
optimized for
Basque
language via vocabulary size reduction using the
trimming
method.
This trimmed model should perform similarly to the original model with only 32,768 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.
Model Statistics
| Metric | Original | Trimmed | Reduction |
|---|---|---|---|
| Vocabulary size | 151,669 tokens | 32,768 tokens | 78.40% |
| Model size | 595,776,512 params | 474,021,888 params | 20.44% |
Mining Dataset Statistics
- Number of texts used for mining : 200,000 texts
- Dataset : lbourdois/fineweb-2-trimming
Usage
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("alphaedge-ai/Qwen3-Embedding-eus-32768")
# Run inference with queries and documents
query = "My query in Basque"
documents = [
"Chunk in Basque",
"Chunk in Basque",
"Chunk in Basque",
]
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)
Citations
Qwen3 Embedding
@article{qwen3embedding,
title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren},
journal={arXiv preprint arXiv:2506.05176},
year={2025}
}
Trimming blog post
@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},
}
Runs of alphaedge-ai Qwen3-Embedding-eus-32768 on huggingface.co
98
Total runs
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24-hour runs
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3-day runs
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More Information About Qwen3-Embedding-eus-32768 huggingface.co Model
Url of Qwen3-Embedding-eus-32768
Qwen3-Embedding-eus-32768 huggingface.co Url
Provider of Qwen3-Embedding-eus-32768 huggingface.co
Other API from alphaedge-ai
Updated:May 20 2026
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