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alphaedge-ai
/
pplx-embed-v1-fas-32768

Sentence Similarity
sentence-transformers
Safetensors
Persian
bidirectional_pplx_qwen3
trimmed
custom_code
text-embeddings-inference
🇪🇺 Region: EU
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pplx-embed-v1-fas-32768
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---
pipeline_tag: sentence-similarity
language: fas
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-fas-32768
This model is a **20.47% smaller** version of [perplexity-ai/pplx-embed-v1-0.6b](https://huggingface.co/perplexity-ai/pplx-embed-v1-0.6b) optimized for **Persian** language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/lbourdois/introduction-to-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%** |

![image](https://raw.githubusercontent.com/lbourdois/blog/refs/heads/master/assets/images/Trimming/pplx-embed-v1-32768.png)

## Mining Dataset Statistics
- **Number of texts used for mining**: 200,000 texts  
- **Dataset**: [lbourdois/fineweb-2-trimming](https://huggingface.co/datasets/lbourdois/fineweb-2-trimming)

## Usage

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