llama_poetry_fa

Model Card for llama_poetry_fa

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Model Name: DivAIn (دیوان) – A Persian Poetry-Driven Llama-Based Language Model Model URL: https://huggingface.co/8lianno/llama_poetry_fa

Model Summary

llama_poetry_fa is a Persian poetry generation model fine-tuned from a Llama 3.1-based checkpoint. It aims to produce stylistically coherent, culturally relevant, and metrically sound verses in response to a user’s prompt. The model focuses on generating output that aligns with classical Persian poetic traditions, such as maintaining consistent rhyme schemes and thematic unity.

Model Details

Model Description

• Developed by: 8lianno
• Model type: Causal Language Model (Llama-based, instruction-tuned)
• Language: Persian (fa)
• License: MIT (Dedicated Model For Matrix AI)
• Finetuned from: meta-llama/Llama-3.1-8B-Instruct

This model has been trained to understand Persian poetic structure, using additional tokens [شروع_شعر], [پایان_شعر], and [مصرع] to guide and maintain poem formatting. It can respond to various thematic prompts—whether about nature, love, historical figures, or abstract concepts—and produce a poem that attempts to follow Persian poetry norms.

Model Sources

• Repository: https://huggingface.co/8lianno/llama_poetry_fa
• Paper [optional]: No paper at this time.

Uses

Direct Use

This model is intended to generate Persian poetry in response to user prompts. It can be used as a creative writing aid, a tool for literary exploration, or as a source of inspiration for authors, poets, and students studying Persian literature.

Example:

• Prompt: "درباره بهار شعری بسرایید"

• Response:

  • عشق چون بهار به جان می افروزد
  • هر دم به گلستان دل می روزند
  • در شب سیاه، چون شمع می تابد
  • هر دم به عشق، جان می روزند

• Prompt: "درباره دریا شعری بسرایید"

• Response:

  • دریا آینه‌ی آسمان است
  • در سینه‌ی او، راز نهان است
  • هر موج او به ساحل می‌رسد
  • صد قصه‌ی عشق و غم و جان است

• Prompt: "درباره عشق بنویسید"

• Response:

  • امید چراغ راه بود
  • در ظلمت شب تاب بود
  • هر کس که با امید همره بود
  • صاحب گنج بی‌بند و بست بود

• Prompt: "آسمان چرا می گرید؟"

• Response:

  • آسمان ز عشق میگرید
  • از غم خورشید میگرید
  • هر کس که به دل عاشق شد
  • صد بهار در دل میگرید

Downstream Use

While primarily designed for poetry generation, the model can be integrated into:

• Persian literature chatbots
• Educational applications for Persian poetry studies
• Cultural or literary heritage projects

Out-of-Scope Use

The model is not designed for:

• Factual Question-Answering (it may produce poetic but factually incorrect answers)
• Non-Persian text generation
• High-stakes decision making

Bias, Risks, and Limitations

The model’s content is drawn from its training data and may reflect certain cultural or poetic biases. It might:

• Present thematically repetitive motifs or styles from classical Persian poetry.
• Struggle with modern or colloquial Persian forms.
• Occasionally produce incoherent or repetitive verses.

Users should critically evaluate the generated text, especially if used in public-facing contexts.

Recommendations

• Review generated poems for cultural appropriateness.
• Verify factual accuracy independently.
• Suggested for Indirect and Poetic answers.
• For sensitive or controversial topics, consider human moderation.

How to Get Started with the Model

Poetry Generator Code

pip install -U transformers>=4.30.0
pip install -U accelerate
pip install bitsandbytes==0.42.0

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

class PoetryGenerator:
    def __init__(self, model_path, token):
        self.token = token
        self.device = "cuda" if torch.cuda.is_available() else "cpu"

        # Configure quantization settings
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=torch.float16,
            bnb_4bit_use_double_quant=True
        )

        # Load tokenizer from the base model used during fine-tuning
        self.tokenizer = AutoTokenizer.from_pretrained(
            "meta-llama/Llama-3.1-8B-Instruct",
            token=token,
            trust_remote_code=True
        )
        self.tokenizer.pad_token = self.tokenizer.eos_token

        # Add the special tokens that were used during training
        special_tokens = {
            "additional_special_tokens": [
                "[شروع_شعر]",
                "[پایان_شعر]",
                "[مصرع]"
            ]
        }
        self.tokenizer.add_special_tokens(special_tokens)

        # Load the base model
        base_model = AutoModelForCausalLM.from_pretrained(
            "meta-llama/Llama-3.1-8B-Instruct",
            token=token,
            device_map="auto",
            trust_remote_code=True,
            torch_dtype=torch.float16,
            quantization_config=bnb_config
        )

        # Resize token embeddings to match tokenizer
        base_model.resize_token_embeddings(len(self.tokenizer))

        # Load the fine-tuned model from Hugging Face Hub
        self.model = PeftModel.from_pretrained(
            base_model,
            model_path,
            token=token,
            device_map="auto"
        )

        self.model.eval()

    def generate_poem(self, prompt):
        formatted_prompt = f"""سوال: {prompt}
لطفا یک شعر فارسی در پاسخ به این سوال بسرایید که دارای وزن و قافیه مناسب باشد.

شعر:"""

        inputs = self.tokenizer(formatted_prompt, return_tensors="pt", padding=True)
        inputs = {k: v.to(self.device) for k, v in inputs.items()}

        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_length=512,
                num_return_sequences=1,
                temperature=0.7,
                top_p=0.9,
                do_sample=True,
                pad_token_id=self.tokenizer.pad_token_id,
                eos_token_id=self.tokenizer.eos_token_id
            )

        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

def main():
    # Use the Hugging Face Hub model path instead of a local path
    generator = PoetryGenerator(
        model_path="8lianno/llama_poetry_fa",
        token="<YOUR_HF_TOKEN>"
    )

    prompts = [
        "درباره بهار شعری بسرایید",
        "شعری درباره عشق بنویسید",
        "درباره دریا شعری بسرایید"
    ]

    print("=== Persian Poetry Generation ===\n")
    for i, prompt in enumerate(prompts, 1):
        print(f"\nPrompt {i}: {prompt}")
        print("\nGenerated Poetry:")
        try:
            poem = generator.generate_poem(prompt)
            print(poem)
            print("\n" + "="*50)
        except Exception as e:
            print(f"Error generating poem: {str(e)}")
            print(f"Error type: {type(e)}")

if __name__ == "__main__":
    main()