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--- |
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license: cc-by-nc-4.0 |
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language: |
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- fa |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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inference: false |
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pipeline_tag: text-generation |
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--- |
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# PersianLLaMA: Towards Building First Persian Large Language Model |
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<img src="https://huggingface.co/ViraIntelligentDataMining/PersianLLaMA-2-13B/resolve/main/persianllama.png" alt="PersianLLaMA" width=400/> |
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## 🌟 Introduction |
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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. |
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## 🛠 Model Description |
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PersianLLaMA is not just a model but a comprehensive tool for: |
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- 📝 **Text Generation**: Crafting coherent and contextually appropriate text. |
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- 🎯 **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. |
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- ❓ **Question Answering**: Providing accurate answers to Persian queries. |
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- 📊 **Text Summarization**: Condensing Persian texts into precise summaries. |
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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. |
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## 🚀 Quick Start |
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To integrate PersianLLaMA into your project, follow these steps: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "your-huggingface-username/persianllama" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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prompt = "این متن به فارسی است" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(inputs["input_ids"]) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## 📈 Evaluation and Benchmarks |
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PersianLLaMA demonstrates superior performance over existing models, with robust evaluation metrics that highlight its capabilities in natural language understanding and generation. |
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## 📜 Citing PersianLLaMA |
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If you find PersianLLaMA useful in your research, please consider citing: |
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```sql |
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@article{abbasi2023persianllama, |
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title={PersianLLaMA: Towards Building First Persian Large Language Model}, |
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author={Abbasi, Mohammad Amin and others}, |
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journal={https://arxiv.org/abs/2312.15713}, |
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year={2023} |
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} |
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``` |
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## 📄 License |
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PersianLLaMA is open-sourced under the CC BY-NC 4.0 license. |
