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
Tom Aarsen commited on
Commit ·
2f6ecfd
1
Parent(s): 4ee1aa5
Add "device_map": "auto" to automatically move the model to CUDA if possible
Browse files
README.md
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@@ -72,7 +72,7 @@ model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B")
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# together with setting `padding_side` to "left":
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# model = SentenceTransformer(
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# "Qwen/Qwen3-Embedding-0.6B",
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# model_kwargs={"attn_implementation": "flash_attention_2"},
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# tokenizer_kwargs={"padding_side": "left"},
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# )
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# together with setting `padding_side` to "left":
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# model = SentenceTransformer(
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# "Qwen/Qwen3-Embedding-0.6B",
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# model_kwargs={"attn_implementation": "flash_attention_2", "device_map": "auto"},
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# tokenizer_kwargs={"padding_side": "left"},
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# )
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