Update README.md

README.md

@@ -7,21 +7,43 @@ base_model:
 pipeline_tag: text-generation
 library_name: transformers
 ---
-# 🌈 wrapbow.ai — Creative Ad Copy LLM (Based on Mistral 7B)
 
-`wrapbow.ai` is a domain-tuned version of Mistral 7B Instruct, customized for use cases in:
-- 🪄 Creative Ad Banner content generation.
-- 📢 Promotional messaging and generating quiz questions for pinkslip.in
-- 🧠 Prompt-driven content generation for digital ads and more.
+# 🎨 wrapbow.ai — Creative Copy & Ideation LLM  
+**Powered by Mistral 7B | Tuned by Ashish Kumar**
+
+`wrapbow.ai` is a domain-adapted LLM built on **Mistral-7B-Instruct-v0.2**, finely tuned to generate high-quality marketing, educational, and digital experience content. Designed for creators, marketers, startups, and educators — this model brings your prompts to life with flair and contextual intelligence.
+
+---
+
+## ✨ Primary Use Cases
+
+- 🪄 **Creative Ad Banner & Copy Generation**  
+  Generate punchy headlines, CTAs, and ad taglines for static, HTML5, or video banners.
+
+- 📢 **Promotional Messaging**  
+  Ideal for personalized offers, flash sale announcements, and event-based campaigns.
+
+- 📚 **Quiz Question Generation** *(for platforms like [pinkslip.in](https://pinkslip.in))*  
+  Automatically generate skill-based, gamified quiz questions for job-seekers and upskilling portals.
+
+- 🧠 **Prompt-Driven Content Ideation**  
+  Use it to brainstorm campaign themes, landing page hooks, or social content angles.
+
+- 🖋️ **Brand Messaging & Positioning Lines**  
+  Write startup one-liners, value propositions, and feature-focused marketing blurbs.
+
+- 🧩 **Use in EdTech, HRTech, and FinTech Landing Pages**  
+  Helps founders auto-generate customized landing copy for high conversion across sectors.
 
 ---
 
 ## ✅ Base Model
+
 - [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
 
 ---
 
-## 💡 Example Usage
+## 💡 Example Usage (Python)
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -33,8 +55,10 @@ prompt = "Generate a banner line for 50% discount on women's fashion"
 inputs = tokenizer(prompt, return_tensors="pt")
 outputs = model.generate(**inputs, max_new_tokens=50)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-
+```
 
 ---
-license: mit
----
+
+## 🔓 License
+
+MIT — free to use, remix, and build upon.