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 modules.json from HIT-TMG/JevEmbed-Qwen3-Embedding-4B: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/HIT-TMG/JevEmbed-Qwen3-Embedding-4B/resolve/a96ec65d6a90f4f71d16e83e0d5fb99b35d7c940/modules.json
- Command line
-
hf download hf://HIT-TMG/JevEmbed-Qwen3-Embedding-4B@a96ec65d6a90f4f71d16e83e0d5fb99b35d7c940/modules.json
-
curl -L -o modules.json https://huggingface.co/HIT-TMG/JevEmbed-Qwen3-Embedding-4B/resolve/a96ec65d6a90f4f71d16e83e0d5fb99b35d7c940/modules.json
349 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |