Sentence Similarity
sentence-transformers
Safetensors
qwen3
multilingual
model-compression
layer-pruning
vocab-pruning
progressive-distillation
qwen3-0.6b
text-embeddings-inference
Instructions to use gomyk/qwen3-student-qwen3_compressed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gomyk/qwen3-student-qwen3_compressed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gomyk/qwen3-student-qwen3_compressed") 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
- Xet hash:
- bfd98f87dc9b7805e63ec74d81b93ca9b9e0d2053155a2f950bc871a66d22369
- Size of remote file:
- 49.4 MB
- SHA256:
- 7a3d826b5325d181806dfdfec778d00420d831931f7e04e657e35bd8b0805e0e
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