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---
license: mit
language:
- en
pipeline_tag: conversational
tags:
- llama2
- text-generation
- text-classification
- conversational
library_name: transformers
---
# 🦙 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.
<img width="2000" src="assets/workflow_1.jpg">
## 🚀 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](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main) and [Sentence Transformers](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) for robust functionality.
- Developed using [Langchain](https://github.com/langchain-ai/langchain) and [Streamlit](https://github.com/streamlit/streamlit) technologies for enhanced performance.
- Cross-platform compatibility with Linux, macOS, or Windows OS.
## 🛠️ Installation
1. **Clone the Repository:**
```bash
git clone https://github.com/ThisIs-Developer/Llama-2-GGML-CSV-Chatbot.git
```
2. **Install Dependencies:**
```bash
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](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main)
### 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
1. **Run the Application:**
```bash
streamlit run app.py
```
2. **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](https://github.com/ThisIs-Developer/Llama-2-GGML-CSV-Chatbot/releases/tag/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](https://github.com/ThisIs-Developer/Llama-2-GGML-CSV-Chatbot/blob/main/LICENSE). |