Instructions to use AXERA-TECH/Qwen3-Embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXERA-TECH/Qwen3-Embedding-0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AXERA-TECH/Qwen3-Embedding-0.6B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AXERA-TECH/Qwen3-Embedding-0.6B", device_map="auto") - sentence-transformers
How to use AXERA-TECH/Qwen3-Embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AXERA-TECH/Qwen3-Embedding-0.6B") 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:
- 9a22b082aa402c095d4ea94570130df994be99f9294f7e64290736fc708b66c7
- Size of remote file:
- 311 MB
- SHA256:
- a55b140d86852835bd18d8200222a9f302340730f0670eb7e23a4895e5489033
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