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---
license: mit
language:
- en
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
pipeline_tag: text-generation
tags:
- Text-Generation
- Copy-Writing
- Push-Notification
- ad
- AD-Writing
---
# 🪄 Copywriting LLM
Generate short, high-converting push notifications and ad copies.
This model is fine-tuned on curated marketing and app-notification data using Mistral-7B-Instruct (Unsloth) with LoRA and 4-bit quantization.
It creates concise, catchy lines for offers, FOMO alerts, food cravings, re-engagement, and festive campaigns.
# Model Details
Property Value
Base Model unsloth/mistral-7b-instruct-v0.3
Fine-Tuning LoRA (r = 16, α = 16, dropout = 0.0)
Quantization 4-bit (QLoRA NF4)
Dataset 3 000 handcrafted marketing prompts & responses
Task Causal Language Modeling for short-form copywriting
Context Length 2048 tokens
# Usage
#
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained("Kavyaah/copywriting-llm")
model = AutoModelForCausalLM.from_pretrained("Kavyaah/copywriting-llm", torch_dtype="auto")
model.eval()
# Function to generate push notification
def generate_copy(brand, offer, tone="fun", max_new_tokens=40):
prompt = f"""You are an expert marketing copywriter.
Write a short, catchy push notification in a {tone} tone.
It should promote {brand}'s offer: "{offer}".
Keep it under 20 words, engaging, and persuasive."""
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=0.9,
top_p=0.9,
do_sample=True
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Example
print(generate_copy("Zomato", "Flat 60% off on dinner combos this weekend!"))
#
Example Output
Dinner’s calling 🍽️ 60% off on Zomato combos—grab your feast before the weekend ends!
# Evaluation
Metric Result
Human rated copy quality 8.5 / 10
Tone accuracy (fun & playful) 93 %
Avg token length 18 words
# Intended Use
Generating push notifications, app banners, and micro-ad copies
Creative assistants for marketing and growth teams
Automating A/B test copy variants for offers and sales
# Limitations
May produce overly playful or repetitive content if prompts are vague
Trained only for short-form marketing copywriting
Avoid using for sensitive topics or regulated industries
# Technical Configuration
Parameter Value
Optimizer AdamW (8-bit)
Learning Rate 2 × 10⁻⁴
Epochs 2
Gradient Accumulation 4
Batch Size (effective) 8
Quantization 4-bit QLoRA
Training Data Categories
Category Example
Sale / Offer “Diwali deals up to 50% off ✨”
Food Craving “Lunch o’clock alert! Your cravings just went live 🍛”
FOMO “Blink and it’s gone 👀 Flash sale ends in 2 hours!”
Re-engagement “We miss your clicks 😢 Come back for something tasty!”
Festive “Play with colors, not your budget! Holi offers just dropped 🎨”
Fashion “New drops just landed 💃 Make your wardrobe jealous!”
# License
MIT License - open for research and non-commercial use.
Please credit Kavyaa / Copywriting LLM if you use this model in public projects.
# Acknowledgements
Fine-tuned using Unsloth for 2× faster training
Base weights from Mistral-7B-Instruct v0.3
Created by Kavyaa for creative and marketing AI research |