Feature Extraction
sentence-transformers
Safetensors
Transformers
sentence-similarity
text-embeddings-inference
Instructions to use dengcao/Qwen3-Embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dengcao/Qwen3-Embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dengcao/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 dengcao/Qwen3-Embedding-0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dengcao/Qwen3-Embedding-0.6B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dengcao/Qwen3-Embedding-0.6B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| services: | |
| Qwen3-Embedding-0.6B: | |
| container_name: Qwen3-Embedding-0.6B | |
| restart: no | |
| #image: dengcao/vllm-openai:v0.9.2-dev #采用vllm最新的开发版制作的镜像,经测试正常,可放心使用 | |
| #image: dengcao/vllm-openai:v0.9.2rc2 | |
| image: dengcao/vllm-openai:v0.9.2 | |
| ipc: host | |
| volumes: | |
| - ./models:/models | |
| command: ["--model", "/models/Qwen3-Embedding-0.6B", "--served-model-name", "Qwen3-Embedding-0.6B", "--gpu-memory-utilization", "0.90"] | |
| ports: | |
| - 8007:8000 | |
| deploy: | |
| resources: | |
| reservations: | |
| devices: | |
| - driver: nvidia | |
| count: all | |
| capabilities: [gpu] | |