Sentence Similarity
sentence-transformers
Safetensors
English
Chinese
qwen2
mteb
retriever
text-embeddings-inference
custom_code
Instructions to use Kingsoft-LLM/QZhou-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Kingsoft-LLM/QZhou-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Kingsoft-LLM/QZhou-Embedding", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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**<span style="font-size: 18px; color:red">Latest Updates</span>(Updated on March 10, 2026):**<br>
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We have uploaded a new model script file **modeling_qzhou_v2.py** to support running in the updated transformers version (5.0.0) environment. Model parameters and inference results remain consistent with the original version. To use it, simply rename the file to **modeling_qzhou.py** and replace the original file.
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The new version requires the following runtime dependencies:
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- torch: 2.9.1
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<img src="assets/image-1.png" width="800" height="300"></img>
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**<span style="font-size: 18px; color:red">Latest Updates</span> (Updated on March 10, 2026):**<br>
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We have uploaded a new model script file **modeling_qzhou_v2.py** to support running in the updated transformers version (5.0.0) environment. Model parameters and inference results remain consistent with the original version. To use it, simply rename the file to **modeling_qzhou.py** and replace the original file.
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The new version requires the following runtime dependencies:
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- torch: 2.9.1
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