README.md

metadata

title: Llm Chatbot
emoji: 👀
colorFrom: indigo
colorTo: red
sdk: gradio
sdk_version: 5.4.0
app_file: app.py
pinned: false
license: apache-2.0

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

LLM-Based Chat Application

This is a language model-based chat application that provides users with relevant, up-to-date, and easy-to-understand responses by leveraging web search and a powerful language model. The basic flow involves performing a web search using DuckDuckGo, followed by generating a response using the Qwen2.5-0.5b-Instruct language model.

Live project:

Features

• Web Search: Performs real-time searches using DuckDuckGo to retrieve the latest relevant information.
• Language Model Integration: Uses the qwen2.5-0.5b-instruct model for generating accurate and coherent responses.
• Simple Interface: Built with Gradio to offer an easy-to-use web interface for users to interact with the model.
• Efficient and Scalable: Optimized for performance using Intel-specific optimizations via optimum-intel and other acceleration libraries.

Model Used

• Language Model: qwen2.5-0.5b-instruct
• A powerful and efficient model that generates human-like responses based on the query.

Application Flow

• User Query: The user inputs a question or request.
• Web Search: The system performs a web search using DuckDuckGo to gather recent and relevant information.
• Response Generation: The gathered data is used to generate a clear, relevant, and easy-to-understand response using the qwen2.5-0.5b-instruct language model.
• User Response: The application returns the generated response to the user.

Dependencies

The following dependencies are required for running this application:

• openvino>=2024.2.0
• openvino-tokenizers[transformers]
• torch>=2.1
• datasets
• duckduckgo-search
• langchain-community
• accelerate
• gradio>=4.19
• onnx<=1.16.1 (For Windows platform sys_platform=='win32')
• einops
• transformers>=4.43.1
• transformers_stream_generator
• tiktoken
• bitsandbytes
• optimum-intel (installed via git+https://github.com/huggingface/optimum-intel.git)
• nncf (installed via git+https://github.com/openvinotoolkit/nncf.git)

Installation

To install the required dependencies, you can use the following command:

pip install -r requirements.txt

License

• This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Acknowledgements

• OpenVINO: For efficient acceleration of the language model.
• DuckDuckGo: For providing real-time web search capabilities.
• Hugging Face: For providing powerful transformer models and tools.