---
license: mit
language:
- en
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
pipeline_tag: text-generation
library_name: transformers
---

# 🎨 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 (Python)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ashishkummar/wrapbow.ai", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ashishkummar/wrapbow.ai", trust_remote_code=True)

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 — free to use, remix, and build upon.