https://github.com/thekartikeyamishra/small-language-model-chatbot

The Small Language Model Chatbot is a Python-based interactive chatbot built using Hugging Face's distilGPT-2 model. The chatbot is fun, lightweight, and ideal for demonstrating the capabilities of small-scale language models. It includes interactive widgets for user input and a visual element that tracks and displays .
https://github.com/thekartikeyamishra/small-language-model-chatbot

ai chatbot distilgpt-2 googlecolab gpt2 gpt2-chatbot ipywidgets jupyter-notebook llm python slm transformer

Last synced: about 1 month ago
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The Small Language Model Chatbot is a Python-based interactive chatbot built using Hugging Face's distilGPT-2 model. The chatbot is fun, lightweight, and ideal for demonstrating the capabilities of small-scale language models. It includes interactive widgets for user input and a visual element that tracks and displays .

• Host: GitHub
• URL: https://github.com/thekartikeyamishra/small-language-model-chatbot
• Owner: thekartikeyamishra
• Created: 2025-01-10T19:02:16.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2025-01-10T19:10:30.000Z (about 1 month ago)
• Last Synced: 2025-01-10T20:20:15.050Z (about 1 month ago)
• Topics: ai, chatbot, distilgpt-2, googlecolab, gpt2, gpt2-chatbot, ipywidgets, jupyter-notebook, llm, python, slm, transformer
• Language: Jupyter Notebook
• Homepage:
• Size: 11.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# **Small Language Model Chatbot**

## **Project Overview**

The **Small Language Model Chatbot** is a Python-based interactive chatbot built using **Hugging Face's `distilGPT-2`** model. The chatbot is fun, lightweight, and ideal for demonstrating the capabilities of small-scale language models. It includes interactive widgets for user input and a visual element that tracks and displays the word count of user inputs over time.

This project is designed to run seamlessly in **Google Colab**.

---

## **Features**

### 🤖 **Interactive Chatbot**

- Accepts user input and generates meaningful responses using `distilGPT-2`.

- Incorporates randomness and creativity in responses through fine-tuned parameters.

### 📊 **Visualization**

- Displays a bar chart to track the word count of user inputs for each interaction.

### ⚙️ **Enhanced Parameters for Text Generation**

- **`temperature=0.7`**: Balances creativity and determinism.

- **`top_p=0.9`**: Uses nucleus sampling to generate high-quality responses.

- **`min_length=20`**: Ensures meaningful replies.

### 🛡️ **Fallback Mechanism**

- If the chatbot generates a very short or meaningless response, it provides a fallback message:

  ```

  I couldn't think of anything interesting to say! Let's try again.

  ```

---

## **Installation Instructions**

### 1. Clone the Repository

```bash

git clone https://github.com/thekartikeyamishra/small-language-model-chatbot.git

cd small-language-model-chatbot

```

### 2. Install Required Libraries

Make sure to install the necessary Python libraries:

```bash

pip install transformers ipywidgets

```

---

## **How to Use**

1. **Open Google Colab**:

   - Copy and paste the code into a new Google Colab notebook.

2. **Run the Script**:

   - Execute the code cells to initialize the chatbot.

3. **Interact with the Chatbot**:

   - Enter your message in the text input field.

   - Click the **Send** button to receive the chatbot's reply.

4. **View Visualizations**:

   - The chatbot displays a bar chart showing the word count of user inputs after each interaction.

---

#### Visualization:

A bar chart shows the number of words in the user's message for each interaction.

---

## **Potential Enhancements**

1. **Sentiment Analysis**:

   - Add a sentiment analysis layer to analyze the tone of user messages.

2. **Multilingual Support**:

   - Integrate pre-trained multilingual models for global audiences.

3. **Save Conversations**:

   - Allow users to save chat history for later review.

4. **Fine-Tuned Models**:

   - Use domain-specific datasets to fine-tune the chatbot for specific applications (e.g., customer support, education).

5. **Voice Input/Output**:

   - Add support for voice-based interactions using libraries like `SpeechRecognition` and `pyttsx3`.

---

## **Use Cases**

- **Learning Tool**:

  - Explore the capabilities of small language models.

  - Understand text generation and visualization in NLP.

- **Prototyping**:

  - Use this chatbot as a starting point for building advanced conversational AI.

- **Fun and Engagement**:

  - Interact with a lightweight chatbot for entertainment or quick inspiration.

---

## **Contribute**

Contributions are welcome! To contribute:

1. Fork the repository.

2. Create a new branch:

   ```bash

   git checkout -b feature-name

   ```

3. Commit your changes:

   ```bash

   git commit -m "Add new feature"

   ```

4. Push to the branch:

   ```bash

   git push origin feature-name

   ```

5. Open a pull request.

---

## **Support**

If you enjoy using this project, please:

- 🌟 Star this repository on GitHub.

- 🗣️ Share it with your network.

- 💬 Submit feedback or feature requests.

---

## **Acknowledgments**

- **Hugging Face Transformers**: For providing pre-trained models and an intuitive API.

- **Matplotlib**: For data visualization.

- **IPyWidgets**: For building an interactive interface.

---

Let me know if you'd like further refinements or additional sections! 🚀