metadata
pipeline_tag: sentence-similarity
language: fra
license: mit
tags:
- trimmed
library_name: sentence-transformers
base_model: perplexity-ai/pplx-embed-v1-0.6b
base_model_relation: quantized
datasets:
- lbourdois/fineweb-2-trimming
pplx-embed-v1-fra-32768
This model is a 20.47% smaller version of perplexity-ai/pplx-embed-v1-0.6b optimized for French language via vocabulary size reduction using the trimming method.
This trimmed model should perform similarly to the original model with only 32,768 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.
Model Statistics
| Metric | Original | Trimmed | Reduction |
|---|---|---|---|
| Vocabulary size | 151,936 tokens | 32,768 tokens | 78.43% |
| Model size | 596,049,920 params | 474,021,888 params | 20.47% |
Mining Dataset Statistics
- Number of texts used for mining: 200,000 texts
- Dataset: lbourdois/fineweb-2-trimming
Usage
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("alphaedge-ai/pplx-embed-v1-fra-32768")
# Run inference with queries and documents
query = "My query in French"
documents = [
"Chunk in French",
"Chunk in French",
"Chunk in French",
]
query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# Compute similarities to determine a ranking
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
Citations
pplx-embed-v1
@misc{eslami2026diffusionpretraineddensecontextualembeddings,
title={Diffusion-Pretrained Dense and Contextual Embeddings},
author={Sedigheh Eslami and Maksim Gaiduk and Markus Krimmel and Louis Milliken and Bo Wang and Denis Bykov},
year={2026},
eprint={2602.11151},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2602.11151},
}
Trimming blog post
@misc{hf_blogpost_trimming,
title={Introduction to Trimming},
author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},
year={2026},
url={https://huggingface.co/blog/lbourdois/introduction-to-trimming},
}