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
🦙 Llama-2-GGML-CSV-Chatbot
Overview
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.
🚀 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
- 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.
🛠️ Installation
- 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:
Download the Llama 2 model file named llama-2-7b-chat.ggmlv3.q4_0.bin from the following link:
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.
📝 Usage
- Run the Application:
streamlit run app.py Access the Application: - Once the application is running, access it through the provided URL.
System Requirements
- CPU: Intel® Core™ i5 or equivalent.
- RAM: 8 GB.
- Disk Space: 7 GB.
- Hardware: Operates on CPU; no GPU required.
🤖 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.
📖 ChatBot Conversession
⚡Streamlit ver. on #v2.0.2.dev20240102
📌 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.
🤝 Contributing
Contributions and suggestions are welcome! Feel free to fork the repository, make changes, and submit pull requests for improvements or bug fixes.
📄 License
This project is licensed under the MIT License.
- Downloads last month
- 0