This model is a 20.47% smaller version of perplexity-ai/pplx-embed-v1-0.6b optimized for Thai 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

python

from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("alphaedge-ai/pplx-embed-v1-tha-32768")
# Run inference with queries and documents
query = "My query in Thai"
documents = [
    "Chunk in Thai",
    "Chunk in Thai",
    "Chunk in Thai",
]
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}, 
}