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Bhavin1905/
Gemma-model-fine-tuned-for-advertisement-generation

Name: Gemma-model-fine-tuned-for-advertisement-generation
Availability: InStock

text-generation-inference

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Gemma Fine-tuned for advertisement generation

This is a fine-tuned language model specifically trained for generating creative advertisement scripts and compelling call-to-actions. The model is part of the PromoGen project - an AI-powered advertisement generator.

Model Description

Model Type: Fine-tuned Transformer Language Model
Language: English
Use Case: Advertisement script generation, marketing copy, call-to-action creation
Base Model: Google's Gemma for CasualLLM
Fine-tuning Dataset: Custom advertisement and marketing copy dataset

Model Performance

Model Size: ~4.8GB
Generation Time: 30-60 seconds (GPU) / 2-5 minutes (CPU)
Memory Usage: ~6GB during inference
Recommended Hardware: NVIDIA GPU with 8GB+ VRAM

Usage

Quick Start

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Bhavin1905/Promogen-finetuned-model")
model = AutoModelForCausalLM.from_pretrained("Bhavin1905/Promogen-finetuned-model")

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Generate advertisement
def generate_ad(company_name, product_name, description):
    prompt = f"Company: {company_name}\nProduct: {product_name}\nDescription: {description}\nAdvertisement:"
    
    inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
    
    with torch.no_grad():
        outputs = model.generate(
            inputs,
            max_new_tokens=100,
            temperature=0.8,
            top_p=0.85,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text.split("Advertisement:")[-1].strip()

# Example usage
ad_script = generate_ad(
    company_name="TechCorp",
    product_name="SmartWatch Pro",
    description="Advanced fitness tracking with heart rate monitoring"
)
print(ad_script)

Integration with PromoGen Application

This model is designed to work with the PromoGen Flask web application. To use it:

Clone the PromoGen repository:

bash
1git clone https://github.com/cptghostriley/PromoGen.git 2cd PromoGen
Download this model:

bash
1from huggingface_hub import snapshot_download 2snapshot_download( 3 repo_id='Bhavin1905/Promogen-finetuned-model', 4 local_dir='./promogen_final_model' 5)
Run the application:

bash
1pip install -r requirements.txt 2python app.py

Model Details

Training Configuration

Training Method: Fine-tuning on advertisement dataset
Optimization: AdamW optimizer
Learning Rate: [Training details]
Batch Size: [Training details]
Training Steps: [Training details]

Model Files

config.json: Model configuration
generation_config.json: Generation parameters
model-00001-of-00002.safetensors: Model weights (part 1)
model-00002-of-00002.safetensors: Model weights (part 2)
model.safetensors.index.json: Model index
special_tokens_map.json: Special tokens mapping
tokenizer.json: Tokenizer configuration
tokenizer.model: Tokenizer model file
tokenizer_config.json: Tokenizer configuration

Hardware Requirements

Minimum Requirements

RAM: 8GB+ (16GB recommended)
Storage: 10GB+ free space
CPU: Multi-core processor

Recommended for Best Performance

GPU: NVIDIA GPU with 8GB+ VRAM (RTX 3070/4060 or better)
RAM: 16GB+
Storage: SSD with 20GB+ free space

Limitations and Bias

The model is trained specifically for advertisement generation
Performance may vary for other text generation tasks
Generated content should be reviewed for appropriateness
May reflect biases present in training data

Citation

If you use this model in your research or applications, please cite:

bibtex

@misc{promogen2024,
  title={PromoGen: AI-Powered Advertisement Generator},
  author={Ashwin Mahawar, Bhavin, and Team PromoGen},
  year={2024},
  url={https://github.com/cptghostriley/PromoGen}
}

Contact

For questions, issues, or collaboration:

GitHub Repository: PromoGen
Issues: GitHub Issues

Note: This model is part of the PromoGen project and is designed for educational and research purposes. Please ensure compliance with applicable laws and regulations when using for commercial purposes.

Bhavin1905/Gemma-model-fine-tuned-for-advertisement-generation