Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
sentence-similarity
text-embeddings-inference
Instructions to use Qwen/Qwen3-Embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Qwen/Qwen3-Embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/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] - Transformers
How to use Qwen/Qwen3-Embedding-0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Qwen/Qwen3-Embedding-0.6B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Embedding-0.6B") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Embedding-0.6B") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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by heyitsys - opened
README.md
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@@ -31,7 +31,7 @@ The Qwen3 Embedding model series is the latest proprietary model of the Qwen fam
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- Model Type: Text Embedding
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- Supported Languages: 100+ Languages
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- Number of
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- Context Length: 32k
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- Embedding Dimension: Up to 1024, supports user-defined output dimensions ranging from 32 to 1024
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- Model Type: Text Embedding
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- Supported Languages: 100+ Languages
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- Number of Parameters: 0.6B
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- Context Length: 32k
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- Embedding Dimension: Up to 1024, supports user-defined output dimensions ranging from 32 to 1024
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