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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-v0.2 |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# 🎨 wrapbow.ai — Creative Copy & Ideation LLM |
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**Powered by Mistral 7B | Tuned by Ashish Kumar** |
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`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. |
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--- |
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## ✨ Primary Use Cases |
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- 🪄 **Creative Ad Banner & Copy Generation** |
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Generate punchy headlines, CTAs, and ad taglines for static, HTML5, or video banners. |
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- 📢 **Promotional Messaging** |
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Ideal for personalized offers, flash sale announcements, and event-based campaigns. |
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- 📚 **Quiz Question Generation** *(for platforms like [pinkslip.in](https://pinkslip.in))* |
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Automatically generate skill-based, gamified quiz questions for job-seekers and upskilling portals. |
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- 🧠 **Prompt-Driven Content Ideation** |
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Use it to brainstorm campaign themes, landing page hooks, or social content angles. |
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- 🖋️ **Brand Messaging & Positioning Lines** |
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Write startup one-liners, value propositions, and feature-focused marketing blurbs. |
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- 🧩 **Use in EdTech, HRTech, and FinTech Landing Pages** |
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Helps founders auto-generate customized landing copy for high conversion across sectors. |
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--- |
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## ✅ Base Model |
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- [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) |
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--- |
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## 💡 Example Usage (Python) |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("ashishkummar/wrapbow.ai", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("ashishkummar/wrapbow.ai", trust_remote_code=True) |
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prompt = "Generate a banner line for 50% discount on women's fashion" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=50) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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--- |
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## 🔓 License |
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MIT — free to use, remix, and build upon. |
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