---
license: cc-by-nc-4.0
language:
- fa
library_name: transformers
tags:
- text-generation-inference
inference: false
pipeline_tag: text-generation
---

# PersianLLaMA: Towards Building First Persian Large Language Model

<img src="https://huggingface.co/ViraIntelligentDataMining/PersianLLaMA-2-13B/resolve/main/persianllama.png" alt="PersianLLaMA" width=400/> 

## 🌟 Introduction
Welcome to the home of PersianLLaMA, the pioneering large language model for the Persian language. With 13 billion parameters, this model is trained on a diverse corpus and designed to excel in multiple NLP tasks, setting a new benchmark for Persian language understanding and generation.

## 🛠 Model Description
PersianLLaMA is not just a model but a comprehensive tool for:
- 📝 **Text Generation**: Crafting coherent and contextually appropriate text.
- 🎯 **Instruct Tuning**: Executing tasks based on detailed instructions, ideal for scenarios where the model needs to adhere to specific guidelines or produce outputs tailored to particular requirements.
- ❓ **Question Answering**: Providing accurate answers to Persian queries.
- 📊 **Text Summarization**: Condensing Persian texts into precise summaries.

This model has been collaboratively developed by a team of experts, including Mohammad Amin Abbasi, Arash Ghafouri, Mahdi Firouzmandi, Hassan Naderi, and Behrouz Minaei Bidgoli.

## 🚀 Quick Start
To integrate PersianLLaMA into your project, follow these steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-huggingface-username/persianllama"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "این متن به فارسی است"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs["input_ids"])
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## 📈 Evaluation and Benchmarks
PersianLLaMA demonstrates superior performance over existing models, with robust evaluation metrics that highlight its capabilities in natural language understanding and generation.


## 📜 Citing PersianLLaMA
If you find PersianLLaMA useful in your research, please consider citing:

```sql
@article{abbasi2023persianllama,
  title={PersianLLaMA: Towards Building First Persian Large Language Model},
  author={Abbasi, Mohammad Amin and others},
  journal={https://arxiv.org/abs/2312.15713},
  year={2023}
}
```


## 📄 License
PersianLLaMA is open-sourced under the CC BY-NC 4.0 license.