---
license: apache-2.0
language:
- fa
- en
base_model:
- heydariAI/persian-embeddings
pipeline_tag: feature-extraction
---
# Persian-English Text Embedding Model (GGUF)

This is a GGUF conversion of the original heydariAI/persian-embeddings from [heydariAI/persian-embeddings](https://huggingface.co/heydariAI/persian-embeddings).
The GGUF version is optimized for efficient inference with frameworks like `llama.cpp` and integration with Ollama.

## Model Details
- **Original Model**: heydariAI/persian-embeddings [heydariAI/persian-embeddings](https://huggingface.co/heydariAI/persian-embeddings)
- **Model Type**: Text Embedding
- **Languages**: Persian (Farsi) and English
- **Conversion**: Converted to GGUF format for efficient CPU/GPU inference

## Intended Uses
As the original repository mentions, this model is designed for:
- Semantic similarity search
- Retrieval-Augmented Generation (RAG) systems
- Text clustering and classification
- Feature extraction for Persian and English texts

## Ollama Integration

This model is available on Ollama Hub as `alishendi/persian-embeddings`.
[Ollama Model Page](https://ollama.com/alishendi/persian-embeddings)
Here's how to use it:

### Installation and Usage

1. First, install Ollama:
```bash
curl -fsSL https://ollama.com/install.sh | sh
```

2. Pull the model:
```bash
ollama pull alishendi/persian-embeddings
```

3. Generate embeddings vector for a sample text:
```bash
curl http://localhost:11434/api/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "model": "alishendi/persian-embeddings",
    "prompt": "برای این متن، بردار بازنمایی تولید کن."
  }'
  ```

### Python Example
Here's how to use the model with Ollama in Python:
```python
# pip install ollama

import ollama
import numpy as np

# Generate embedding
response = ollama.embeddings(
    model="alishendi/persian-embeddings",
    prompt="برای این متن، بردار بازنمایی تولید کن."
)

embedding = np.array(response["embedding"])
print(f"Embedding shape: {embedding.shape}")
print(f"First 10 values: {embedding[:10]}")
```
Also you can download the model on LM Studio and generate the embeddings via its API

## Performance
The GGUF format provides efficient inference on both CPU and GPU hardware. Performance will vary based on your hardware configuration.

## Acknowledgements
This model is based on the original work from [heydariAI/persian-embeddings](https://huggingface.co/heydariAI/persian-embeddings). Please consider citing the original authors if you use this model in your research.