I'll help you create an AI language model similar to ChatGPT using Python. This will be a comprehensive implementation that includes data preprocessing, model architecture, training, and a chat interface.

I've created a comprehensive AI language model similar to ChatGPT using Python! This implementation includes all the essential components:

Key Features:

1. Transformer Architecture (model.py):

   • Multi-head attention mechanism
   • Feed-forward networks
   • Layer normalization and residual connections
   • GPT-style decoder architecture

2. Tokenizer (tokenizer.py):

   • Text preprocessing and tokenization
   • Vocabulary building
   • Encoding/decoding functionality

3. Training Pipeline (trainer.py):

   • Custom dataset handling
   • Training loop with loss calculation
   • Model saving/loading capabilities

4. Chat Interface (chat_interface.py):

   • Interactive chatbot functionality
   • Text generation with temperature and top-k sampling
   • Real-time conversation handling

5. Data Preprocessing (data_preprocessor.py):

   • Data cleaning and augmentation
   • Conversation format preparation
   • Sample data generation

6. Main Script (main.py):

   • Complete workflow orchestration
   • User-friendly interface for training and chatting

How to Use:

1. First, train the model:

   • Run the main script and choose option 1
   • The model will train on sample conversation data
   • Training progress will be displayed

2. Then chat with your AI:

   • Choose option 2 to start the chatbot
   • Have conversations with your trained model
   • Type 'quit' to exit

Model Architecture Details:

• Embedding Layer: Converts tokens to dense vectors
• Positional Encoding: Adds position information
• Multi-Head Attention: Enables the model to focus on different parts of the input
• Feed-Forward Networks: Processes the attention output
• Layer Normalization: Stabilizes training
• Causal Masking: Ensures the model only looks at previous tokens

The model is designed to be educational and demonstrates the core concepts behind modern language models like ChatGPT. While simplified compared to production models, it includes all the fundamental components and can be extended with more data and computational resources.

To configure the generation, complete these steps: