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 tokenizer.json from HIT-TMG/JevEmbed-Qwen3-Embedding-4B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/HIT-TMG/JevEmbed-Qwen3-Embedding-4B/resolve/main/tokenizer.json
- Command line
-
hf download hf://HIT-TMG/JevEmbed-Qwen3-Embedding-4B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/HIT-TMG/JevEmbed-Qwen3-Embedding-4B/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 1032144f741f729755bdbe5f8214eb2d0a78997e5d069fd79fd060bf3b0f585b
- Size of remote file:
- 11.4 MB
- SHA256:
- b7e24abbf8db66065c500459b3f6b876165878c4d45486d8a54466da3b8e0f81
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