Blog Generation using LLaMA 2

This is an interactive LLM-based blog generation application built with Meta’s LLaMA 2, LangChain, and Streamlit. The app allows users to input a topic, choose the desired writing style, and receive a high-quality blog article generated using LLaMA 2.

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🔍 Project Overview

With the rise of LLMs in content generation, this project focuses on:

• ✅ Structuring a local LLM pipeline using LLaMA 2
• ✅ Leveraging LangChain for prompt templates
• ✅ Providing an interactive UI with Streamlit
• ✅ Creating personalized blog content with minimal input

I built this tool to be modular, lightweight, and easy to extend — whether for content marketing, education, or research.

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⚙️ Key Features

• 🔗 LLaMA 2 integration via CTransformers (runs locally)
• 🧠 Prompt engineering using LangChain templates
• 🖱️ Interactive UI built with Streamlit
• ✍️ Dynamic blog content generation based on topic, tone, and word count
• 🛠️ Minimal resource requirement (runs without GPU/cloud)
• 💡 Future-ready for vector-based retrieval integration

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🧱 Tech Stack

• Python 3.10+
• [Meta's LLaMA 2 (GGML Quantized)]
• [LangChain]
• [CTransformers]
• [Streamlit]
• [Uvicorn] (for API)
• Python Box (for cleaner configuration handling)

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🪪 License

This project is for educational and experimental use. Ensure compliance with Meta's LLaMA 2 license when using the model.