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