Sentence Similarity
sentence-transformers
Safetensors
Russian
English
bert
embeddings
vllm
inference-optimized
inference
text-embeddings-inference
Instructions to use WpythonW/rubert-tiny2-vllm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use WpythonW/rubert-tiny2-vllm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("WpythonW/rubert-tiny2-vllm") sentences = [ "Это счастливый человек", "Это счастливая собака", "Это очень счастливый человек", "Сегодня солнечный день" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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## Original Model
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For standard PyTorch/Transformers usage, see the original model: [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)
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This vLLM version is optimized for deployment scenarios requiring:
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- High throughput batch processing
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- Low latency inference
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- OpenAI API compatibility
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- Production-grade serving infrastructure
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## Original Model
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For standard PyTorch/Transformers usage, see the original model: [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)
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