The Llama-2-GGML-CSV-Chatbot is a conversational tool powered by a fine-tuned large language model (LLM) known as Llama-2 7B. This chatbot utilizes CSV retrieval capabilities, enabling users to engage in multi-turn interactions based on uploaded CSV data.
- CSV Data Interaction: Allows users to engage in conversations based on uploaded CSV data.
- Multi-turn Interaction: Supports seamless multi-turn interactions for a better conversational experience.
- Utilizes Llama-2 7B and Sentence Transformers for robust functionality.
- Developed using Langchain and Streamlit technologies for enhanced performance.
- Cross-platform compatibility with Linux, macOS, or Windows OS.
- Clone the Repository:
git clone https://github.com/ThisIs-Developer/Llama-2-GGML-CSV-Chatbot.git
- Install Dependencies:
pip install -r requirements.txt
Download the Llama 2 model file named llama-2-7b-chat.ggmlv3.q4_0.bin
from the following link:
Name | Quant method | Bits | Size | Max RAM required |
---|---|---|---|---|
llama-2-7b-chat.ggmlv3.q4_0.bin | q4_0 | 4 | 3.79 GB | 6.29 GB |
Note: After downloading the model, add the model file to the models
directory. The file should be located at models\llama-2-7b-chat.ggmlv3.q4_0.bin
, in order to run the code.
- Run the Application:
streamlit run app.py
- Access the Application:
- Once the application is running, access it through the provided URL.
- CPU: Intel® Core™ i5 or equivalent.
- RAM: 8 GB.
- Disk Space: 7 GB.
- Hardware: Operates on CPU; no GPU required.
- Upon running the application, you'll be presented with a sidebar providing information about the chatbot and an option to upload a CSV file.
- Upload a CSV file containing the data you want the chatbot to interact with.
- Enter your query or prompt in the input field provided.
- The chatbot will process your query and generate a response based on the uploaded CSV data and the Llama-2-7B-Chat-GGML model.
⚡Streamlit ver. on #v2.0.2.dev20240102
- While robust, this chatbot is not a substitute for professional advice.
- Ensure the CSV file adheres to the expected format for optimal performance.
Contributions and suggestions are welcome! Feel free to fork the repository, make changes, and submit pull requests for improvements or bug fixes.
This project is licensed under the MIT License.