ViraIntelligentDataMining_-_PersianLLaMA-13B-gguf

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PersianLLaMA-13B - GGUF

• Model creator: https://huggingface.co/ViraIntelligentDataMining/
• Original model: https://huggingface.co/ViraIntelligentDataMining/PersianLLaMA-13B/

Original model description:

license: cc-by-nc-4.0 language:

• fa library_name: transformers tags:
• text-generation-inference inference: false pipeline_tag: text-generation

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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 Persian Wikipedia 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, Behrouz Minaei Bidgoli.

🚀 Quick Start

To integrate PersianLLaMA into your project, follow these steps:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ViraIntelligentDataMining/PersianLLaMA-13B"
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:

@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.