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 sentence_bert_config.json from HIT-TMG/JevEmbed-Qwen3-Embedding-4B: direct link, hf CLI and curl.
- Browser
- Download file 59 Bytes
-
https://huggingface.co/HIT-TMG/JevEmbed-Qwen3-Embedding-4B/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://HIT-TMG/JevEmbed-Qwen3-Embedding-4B/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/HIT-TMG/JevEmbed-Qwen3-Embedding-4B/resolve/main/sentence_bert_config.json
59 Bytes
| { | |
| "max_seq_length": 1024, | |
| "do_lower_case": false | |
| } | |