Instructions to use C10X/Qwen3-Embedding-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use C10X/Qwen3-Embedding-Turbo with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("C10X/Qwen3-Embedding-Turbo") - sentence-transformers
How to use C10X/Qwen3-Embedding-Turbo with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("C10X/Qwen3-Embedding-Turbo") 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:
- 3b58b5aa81a09e6cec19a9986a9a10d434199f4a0c89d4720e27a00e5e6bc15c
- Size of remote file:
- 77.6 MB
- SHA256:
- 98a3e8e799ee296939952312795f13f519321d4d829fc91ef6996190636fdb49
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.