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YAML Metadata Warning: The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other

๐Ÿฆ™ Llama-2-GGML-CSV-Chatbot


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.

๐Ÿš€ Features

  • 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.

Development Specs

๐Ÿ› ๏ธ Installation

  1. Clone the Repository:
    git clone https://github.com/ThisIs-Developer/Llama-2-GGML-CSV-Chatbot.git
  2. Install Dependencies:
    pip install -r requirements.txt

Download the Llama 2 Model:

Download the Llama 2 model file named llama-2-7b-chat.ggmlv3.q4_0.bin from the following link:

Download Llama 2 Model

Llama 2 Model Information

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.

๐Ÿ“ Usage

  1. Run the Application:
    streamlit run app.py
  2. Access the Application:
    • Once the application is running, access it through the provided URL.

System Requirements

  • CPU: Intelยฎ Coreโ„ข i5 or equivalent.
  • RAM: 8 GB.
  • Disk Space: 7 GB.
  • Hardware: Operates on CPU; no GPU required.

๐Ÿค– How to Use

  • 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.

๐Ÿ“– ChatBot Conversession

โšกStreamlit ver. on #v2.0.2.dev20240102

ChatBot Conversession img-1 png

๐Ÿ“Œ Important Notes

  • While robust, this chatbot is not a substitute for professional advice.
  • Ensure the CSV file adheres to the expected format for optimal performance.

๐Ÿค Contributing

Contributions and suggestions are welcome! Feel free to fork the repository, make changes, and submit pull requests for improvements or bug fixes.

๐Ÿ“„ License

This project is licensed under the MIT License.

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