Instructions to use HIT-TMG/JevEmbed-Qwen3-Embedding-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HIT-TMG/JevEmbed-Qwen3-Embedding-4B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HIT-TMG/JevEmbed-Qwen3-Embedding-4B") 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] - Notebooks
- Google Colab
- Kaggle
File size: 375 Bytes
a96ec65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"prompts": {
"query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:",
"document": ""
},
"default_prompt_name": null,
"similarity_fn_name": "cosine",
"model_type": "SentenceTransformer",
"__version__": {
"sentence_transformers": "5.3.0",
"transformers": "4.51.0",
"pytorch": "2.8.0+cu129"
}
} |