Edit model card

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, video-text-to-text, keypoint-detection, any-to-any, 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:

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.

📝 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: -; Downloads are not tracked for this model. How to track

Inference Examples

Text Generation

This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

ThisIs-Developer
/

Llama-2-GGML-CSV-Chatbot