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pipeline_tag: text-generation
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library_name: transformers
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---
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# π wrapbow.ai β Creative Ad Copy LLM (Based on Mistral 7B)
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---
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## β
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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## π‘ Example Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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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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pipeline_tag: text-generation
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library_name: transformers
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---
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# π¨ wrapbow.ai β 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 β this model brings your prompts to life with flair and contextual intelligence.
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---
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## β¨ Primary Use Cases
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- πͺ **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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- π’ **Promotional Messaging**
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Ideal for personalized offers, flash sale announcements, and event-based campaigns.
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- π **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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- π§ **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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- ποΈ **Brand Messaging & Positioning Lines**
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Write startup one-liners, value propositions, and feature-focused marketing blurbs.
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- π§© **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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## β
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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## π‘ Example Usage (Python)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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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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## π License
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MIT β free to use, remix, and build upon.
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