File size: 1,526 Bytes
a88cff4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- lora
- qlora
- script-writing
- ads
- youtube-shorts
- reels
- marketing
- content-creation
language:
- en
pipeline_tag: text-generation
---
# SireIQ-Scripts 🧠✍️
**SireIQ-Scripts** is an instruction-tuned AI model for generating **viral content scripts**, including:
- Short-form video scripts (Reels, TikTok, Shorts)
- Marketing & ad copy
- Image/video storytelling scripts
- Hook-based social media content
This model is **fine-tuned using LoRA** on top of **Mistral-7B-Instruct**.
---
## 🔧 Model Details
- **Base Model:** mistralai/Mistral-7B-Instruct-v0.2
- **Fine-tuning:** LoRA / QLoRA
- **Training Type:** Instruction-following
- **Framework:** Hugging Face Transformers
- **Environment:** Single GPU (Colab / Ubuntu)
---
## 📥 How to Use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
base_model = "mistralai/Mistral-7B-Instruct-v0.2"
lora_model = "your-username/SireIQ-Scripts"
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(
base_model,
device_map="auto",
load_in_4bit=True
)
model = PeftModel.from_pretrained(model, lora_model)
prompt = """Write a viral Instagram reel script about AI replacing jobs."""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|