Sentence Similarity
sentence-transformers
Safetensors
English
German
Chinese
qwen3
embeddings
retrieval
legal
text-embeddings-inference
Instructions to use litillabs/litil-embed-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use litillabs/litil-embed-0.6b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("litillabs/litil-embed-0.6b") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from litillabs/litil-embed-0.6b: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/litillabs/litil-embed-0.6b/resolve/main/tokenizer.json
- Command line
-
hf download hf://litillabs/litil-embed-0.6b/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/litillabs/litil-embed-0.6b/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- d0d28bae711384ef80959e0a068334709e267c3f981777edd3d35255059d71a1
- Size of remote file:
- 11.4 MB
- SHA256:
- bb84622d64d698db218178a235ec333098167d397b038f2481f8af547b345c76
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