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
- Xet hash:
- 6aec39639a0a2d1ca966356b8c2b8426a484f80ff80731f44fa8482040713bdf
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.