Create README.md

README.md

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+---
+license: mit
+language:
+- en
+base_model:
+- mistralai/Mistral-7B-Instruct-v0.3
+pipeline_tag: text-generation
+---
+
+# 🪄 Copywriting LLM
+
+Generate short, high-converting push notifications and ad copies in the style of Zomato, Swiggy, Myntra, and Zepto.
+
+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