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
Download config_sentence_transformers.json from HIT-TMG/JevEmbed-Qwen3-Embedding-4B: direct link, hf CLI and curl.
- Browser
- Download file 375 Bytes
-
https://huggingface.co/HIT-TMG/JevEmbed-Qwen3-Embedding-4B/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://HIT-TMG/JevEmbed-Qwen3-Embedding-4B/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/HIT-TMG/JevEmbed-Qwen3-Embedding-4B/resolve/main/config_sentence_transformers.json
375 Bytes
| { | |
| "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" | |
| } | |
| } |