RichardErkhov/ViraIntelligentDataMining_-_PersianLLaMA-13B-gguf

Quantization made by Richard Erkhov.

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PersianLLaMA-13B - GGUF

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

Name	Quant method	Size
PersianLLaMA-13B.Q2_K.gguf	Q2_K	4.68GB
PersianLLaMA-13B.IQ3_XS.gguf	IQ3_XS	5.17GB
PersianLLaMA-13B.IQ3_S.gguf	IQ3_S	5.45GB
PersianLLaMA-13B.Q3_K_S.gguf	Q3_K_S	5.45GB
PersianLLaMA-13B.IQ3_M.gguf	IQ3_M	5.75GB
PersianLLaMA-13B.Q3_K.gguf	Q3_K	6.08GB
PersianLLaMA-13B.Q3_K_M.gguf	Q3_K_M	6.08GB
PersianLLaMA-13B.Q3_K_L.gguf	Q3_K_L	6.63GB
PersianLLaMA-13B.IQ4_XS.gguf	IQ4_XS	6.73GB
PersianLLaMA-13B.Q4_0.gguf	Q4_0	7.06GB
PersianLLaMA-13B.IQ4_NL.gguf	IQ4_NL	7.1GB
PersianLLaMA-13B.Q4_K_S.gguf	Q4_K_S	7.11GB
PersianLLaMA-13B.Q4_K.gguf	Q4_K	7.52GB
PersianLLaMA-13B.Q4_K_M.gguf	Q4_K_M	7.52GB
PersianLLaMA-13B.Q4_1.gguf	Q4_1	7.81GB
PersianLLaMA-13B.Q5_0.gguf	Q5_0	8.57GB
PersianLLaMA-13B.Q5_K_S.gguf	Q5_K_S	8.57GB
PersianLLaMA-13B.Q5_K.gguf	Q5_K	8.81GB
PersianLLaMA-13B.Q5_K_M.gguf	Q5_K_M	8.81GB
PersianLLaMA-13B.Q5_1.gguf	Q5_1	9.33GB
PersianLLaMA-13B.Q6_K.gguf	Q6_K	10.18GB
PersianLLaMA-13B.Q8_0.gguf	Q8_0	13.18GB

Original model description:

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

🌟 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.