Feature Extraction
sentence-transformers
Safetensors
English
Chinese
qwen2
Retrieval
STS
Classification
Clustering
Reranking
vllm
custom_code
text-embeddings-inference
Instructions to use KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
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## Citation
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If you find this model useful, please consider giving a star and citation.
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url={https://arxiv.org/abs/2506.20923},
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@misc{hu2025kalmembedding,
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## Citation
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If you find this model useful, please consider giving a star and citation.
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```
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@inproceedings{
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zhao2026kalmembeddingv2,
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title={Ka{LM}-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model},
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author={Xinping Zhao and Xinshuo Hu and Zifei Shan and Shouzheng Huang and Yao Zhou and Xin Zhang and Zetian Sun and zhenyu liu and Dongfang Li and Xinyuan Wei and Youcheng Pan and Yang Xiang and Meishan Zhang and Haofen Wang and Jun Yu and Baotian Hu and Min Zhang},
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booktitle={The Fourteenth International Conference on Learning Representations},
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year={2026},
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url={https://openreview.net/forum?id=Y7qzhvWhcz}
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}
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@misc{hu2025kalmembedding,
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