Feature Extraction
sentence-transformers
ONNX
Safetensors
multilingual
bidirectional_pplx_qwen3
sentence-similarity
mteb
custom_code
text-embeddings-inference
Instructions to use LHC88/pplx-embed-v1-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LHC88/pplx-embed-v1-4B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LHC88/pplx-embed-v1-4B", trust_remote_code=True) 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 LHC88/pplx-embed-v1-4B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/LHC88/pplx-embed-v1-4B/resolve/main/tokenizer.json
- Command line
-
hf download hf://LHC88/pplx-embed-v1-4B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/LHC88/pplx-embed-v1-4B/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- a415a74fa7d93decadecb0fe0d6b1e51493ac309668c9ed3d3b6fb8fd55e42df
- Size of remote file:
- 11.4 MB
- SHA256:
- c6fb5c5bbba5fa5f8332edfb6d8aa67bd7fb3d75365b1765f108201698eaebf5
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