Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
sentence-similarity
text-embeddings-inference
Instructions to use Qwen/Qwen3-Embedding-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Qwen/Qwen3-Embedding-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-Embedding-8B") 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-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Qwen/Qwen3-Embedding-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Embedding-8B") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Embedding-8B", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse filesAdd comment of max_seq_len to sentence transformers, especially for efficient training:
```python
# Optionally, lower the maximum sequence length for lower memory usage
# model.max_seq_length = 8192
```
README.md
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# tokenizer_kwargs={"padding_side": "left"},
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# )
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# The queries and documents to embed
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queries = [
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"What is the capital of China?",
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journal={arXiv preprint arXiv:2506.05176},
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year={2025}
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}
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```
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# tokenizer_kwargs={"padding_side": "left"},
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# )
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# Optionally, lower the maximum sequence length for lower memory usage
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# model.max_seq_length = 8192
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# The queries and documents to embed
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queries = [
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"What is the capital of China?",
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journal={arXiv preprint arXiv:2506.05176},
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year={2025}
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}
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```
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