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
Update docker-compose.yaml
Browse files- docker-compose.yaml +2 -1
docker-compose.yaml
CHANGED
|
@@ -3,7 +3,8 @@ services:
|
|
| 3 |
container_name: Qwen3-Embedding-0.6B
|
| 4 |
restart: no
|
| 5 |
#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
|
| 6 |
-
image: dengcao/vllm-openai:v0.9.2rc2
|
|
|
|
| 7 |
ipc: host
|
| 8 |
volumes:
|
| 9 |
- ./models:/models
|
|
|
|
| 3 |
container_name: Qwen3-Embedding-0.6B
|
| 4 |
restart: no
|
| 5 |
#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
|
| 6 |
+
#image: dengcao/vllm-openai:v0.9.2rc2
|
| 7 |
+
image: dengcao/vllm-openai:v0.9.2
|
| 8 |
ipc: host
|
| 9 |
volumes:
|
| 10 |
- ./models:/models
|