/Blog_Generation_LLM_app

Primary LanguagePythonMIT LicenseMIT

Blog Generation LLM app (LLama - 2)

Llama 2

Overview

Llama 2 is a feature-rich, open-source software designed for managing and analyzing llama-related data. Building upon the success of its predecessor, Llama 2 introduces several enhancements, improvements, and new functionalities to provide users with a more seamless and efficient experience in working with llama-related information.

Features

  • Advanced Data Management: Llama 2 offers robust data management capabilities, allowing users to efficiently organize, store, and retrieve llama-related data.

  • Enhanced Analytics: Take advantage of powerful analytics tools to gain valuable insights into llama behavior, patterns, and trends. Llama 2 includes updated algorithms for more accurate analysis.

  • User-Friendly Interface: The intuitive and user-friendly interface ensures that both new and experienced users can navigate Llama 2 with ease. Enjoy a visually appealing and responsive design.

  • Customization Options: Tailor Llama 2 to your specific needs with customizable settings, themes, and user preferences. Adapt the software to fit your unique llama data requirements.

  • Improved Performance: Llama 2 is optimized for speed and efficiency, providing faster data processing and analysis. Experience a smoother workflow with reduced processing times.

Installation

To install Llama 2, follow these steps:

  1. Clone the repository: git clone https://github.com/your-username/llama2.git
  2. Navigate to the project directory: cd llama2
  3. Install dependencies: npm install
  4. Launch Llama 2: npm start

For detailed installation instructions and additional configuration options, refer to the Installation Guide.

Documentation

Visit the Llama 2 Documentation for comprehensive guides, tutorials, and API references. Learn how to make the most of Llama 2's features and integrate it into your projects.

Contributing

We welcome contributions from the community. To contribute to Llama 2, follow our Contribution Guidelines and join our vibrant community on Discord.

License

Llama 2 is released under the MIT License. Feel free to use, modify, and distribute it in your projects.

Llama 2 - 7B Chat GGML

This repository hosts the Llama 2 language model pre-trained on 7 billion conversations for chat-based tasks. The model is available on the Hugging Face Model Hub.

Model Information

Overview

Llama 2 is a powerful language model pre-trained on a diverse set of conversations to excel in chat-based tasks. This model is fine-tuned for general chat generation and can be used in various applications.

How to Use

To use the Llama 2 model in your projects, you can leverage the Hugging Face Transformers library. Here's an example of loading the model using Python:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model_name = "TheBloke/Llama-2-7B-Chat-GGML"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

This Streamlit app generates blog content using the Llama 2 language model.

Getting Started

Prerequisites

  • Laptop / PC with good RAM and core performance
  • Python 3.6 or later
  • Pip (Python package installer)

Installation

  1. Clone the repository:

    git clone https://github.com/your-username/your-repository.git
cd your-repository

  2. Install dependencies:

    pip install -r requirements.txt

Usage

  1. Run the Streamlit app:

    streamlit run app.py

  2. Open your web browser and navigate to http://localhost:8501.

  3. Enter the topic, select the blog style, and click the "Generate Blog" button.

Project Structure

  • app.py: The main Streamlit application script.
  • requirements.txt: List of Python dependencies.
  • models/: Directory containing the language model files.
  • venv/: Virtual environment directory (optional).

Configuration

  • Update the models/ directory with the appropriate language model file.
  • Modify the configurations in app.py as needed.

Contributing

  1. Fork the repository.
  2. Create a new branch: git checkout -b feature/my-feature.
  3. Commit your changes: git commit -m 'Add some feature'.
  4. Push to the branch: git push origin feature/my-feature.
  5. Submit a pull request.

License

This project is licensed under the MIT License.

Acknowledgments

  • The Llama 2 language model is provided by langchain.
  • Inspiration for the project: [Provide any relevant sources or inspirations].
  • In this project i have choosen to use "llama-2-7b-chat.ggmlv3.q6_K.bin", this version of Llama is about 5.3 GB

Note

  • If you are running this on your localruntime it will take a huge amount of time create the blog