RSK World - Complete Documentation - GPT-2 Chatbot | GPT-2 Chatbot | Text Generation | Conversational AI | Transformers | PyTorch | RSK World

Project Description

This project implements a GPT-2 chatbot system for text generation and conversational AI. GPT-2 (Generative Pre-trained Transformer 2) uses autoregressive language modeling to generate coherent and contextually relevant text, making it excellent for chatbots and creative text generation. The system includes fine-tuning strategies, response generation, context management, and advanced features like temperature-controlled generation, batch processing, and comprehensive evaluation.

GPT-2 uses transformer decoder architecture with autoregressive generation to produce text one token at a time. The implementation provides complete PyTorch support with Transformers (Hugging Face), comprehensive training pipeline, REST API server, evaluation metrics, and deployment tools for text generation and conversational AI applications.

Project Screenshots

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Core Features

GPT-2 Architecture

Autoregressive text generation
Transformer decoder layers
Pre-trained GPT-2 models (base, medium, large, xl)
Fine-tuning for specific domains
Natural language generation

Context Management

Multi-turn conversation support
Session-based context
Sliding window context
Context-aware responses
Conversation history tracking

Text Generation

Temperature-controlled generation
Top-p (nucleus) sampling
Top-k sampling
Repetition penalty control
Diverse output generation

Batch Generation

Batch text generation
Multiple prompt processing
Efficient GPU utilization
JSON and text output formats
High-throughput processing

Model Evaluation

Perplexity calculation
Generation speed metrics
BLEU, ROUGE-L, METEOR scores
Response quality scoring
Comprehensive metrics

REST API Server

Flask-based API
Multiple endpoints
CORS enabled
Request logging
Production-ready

Advanced Features

Evaluation Metrics

BLEU scores (1-4 grams)
METEOR score
ROUGE-L score
Perplexity calculation
Batch evaluation support

Data Augmentation

Synonym replacement
Paraphrasing techniques
Noise injection
Automated dataset expansion

Response Ranking

Multi-factor ranking system
Similarity-based (50%)
Intent confidence (20%)
Sentiment matching (30%)

Conversation Analytics

Dataset statistics
Word frequency analysis
Topic distribution
Conversation length analysis
Export capabilities

REST API Endpoints

Endpoint	Method	Description	Request Body	Response
/api/chat	POST	Main chat interface	{"message": "text", "session_id": "optional"}	Generated response with metadata
/api/generate	POST	Text generation	{"prompt": "text", "max_length": 100, "temperature": 0.7}	Generated text
/api/batch	POST	Batch text generation	{"prompts": ["text1", "text2"], "max_length": 100}	List of generated texts
/api/evaluate	POST	Evaluate model performance	{"test_file": "path", "model": "gpt2"}	Perplexity and speed metrics
/api/health	GET	Health check	N/A	Server status
/api/clear	POST	Clear conversation	{"session_id": "optional"}	Success message

Technologies Used

This GPT-2 chatbot project is built using modern deep learning and web technologies. The core implementation uses Python as the primary programming language, PyTorch for deep learning operations, and the Transformers library from Hugging Face for GPT-2 model integration. The project includes a Flask-based REST API for web integration, Jupyter Notebook support for interactive development and demonstrations, and comprehensive evaluation metrics for assessing model performance.

The GPT-2 model variants (base, medium, large, xl) are provided by Hugging Face, enabling easy access to pre-trained models and fine-tuning capabilities. The system supports autoregressive text generation with temperature control, top-p and top-k sampling, making it suitable for various text generation and conversational AI applications.

Python 3.8+ PyTorch 2.0+ Transformers 4.30+ Hugging Face GPT-2 Text Generation Language Modeling Jupyter Notebook Flask 2.3+ Evaluation Metrics

Installation & Usage

Installation

Install all required dependencies for the GPT-2 chatbot project:

# Install all requirements
pip install -r requirements.txt

# The GPT-2 model will be automatically downloaded
# from Hugging Face on first use

Quick Setup

Set up the project structure and verify installation:

# Run quick setup script
python quick_start.py setup

# Test imports
python quick_start.py test-imports

# Run quick test
python quick_start.py quick-test

Training the Model

Fine-tune the GPT-2 model on your text dataset:

# Basic training
python train.py --train_file data/train.txt --output_dir models/fine_tuned_gpt2

# Training with custom parameters
python train.py --train_file data/train.txt \
                --output_dir models/fine_tuned_gpt2 \
                --epochs 3 \
                --batch_size 4 \
                --learning_rate 5e-5 \
                --model_name gpt2

Interactive Chatbot

Run the chatbot in interactive mode for conversations:

# Run chatbot with default model
python chatbot.py

# Run with trained model
python chatbot.py --fine_tuned_path models/fine_tuned_gpt2

# Run with custom model variant
python chatbot.py --model_name gpt2-medium

# Commands in chat:
# - Type 'quit', 'exit', or 'bye' to end
# - Type 'clear' to clear context
# - Type 'save' to save conversation
# - Type 'load ' to load conversation
# - Type 'stats' to see conversation statistics

REST API Server

Start the Flask API server for web integration:

# Start API server (default port 5000)
python app.py

# API will be available at http://localhost:5000

# Example API calls:
# POST /api/chat - {"message": "Hello", "session_id": "user123"}
# POST /api/generate - {"prompt": "Once upon a time", "max_length": 100}
# POST /api/batch - {"prompts": ["Prompt 1", "Prompt 2"]}
# POST /api/evaluate - {"test_file": "data/val.txt", "model": "gpt2"}
# GET /api/health
# POST /api/clear - {"session_id": "user123"}

Batch Text Generation

Generate text from multiple prompts at once:

# Generate from text file
python batch_generate.py --input data/sample_prompts.txt --output results.json

# Generate from JSON file
python batch_generate.py --input data/sample_prompts.json --output results.json

# This will process multiple prompts efficiently
# and save results in JSON format

Evaluation Metrics

Evaluate chatbot responses using comprehensive metrics:

from evaluate_model import evaluate_model

# Evaluate model on test dataset
results = evaluate_model(
    model_name='gpt2',
    test_file='data/val.txt',
    device='cuda'
)

# Returns dictionary with:
# - perplexity: Language model perplexity
# - avg_generation_time: Average generation time per token
# - tokens_per_second: Generation speed
# - total_tokens: Total tokens processed

# Print detailed results
for metric, value in results.items():
    print(f"{metric}: {value:.4f}")

# Advanced evaluation with multiple metrics
from advanced_evaluation import AdvancedEvaluator

evaluator = AdvancedEvaluator()
scores = evaluator.evaluate(
    reference="Hello, how can I help you?",
    candidate="Hi there! How may I assist you?"
)

# Returns BLEU, ROUGE-L, METEOR, and semantic similarity scores

Metric Descriptions:

BLEU (1-4): Measures n-gram precision between reference and candidate
METEOR: Considers synonyms and word order, more semantic than BLEU
ROUGE-L: Measures longest common subsequence, good for longer responses
Perplexity: Measures how well the model predicts the next word

Conversation Analytics

Analyze your conversation dataset:

# Generate conversation statistics
python conversation_stats.py

# This will display:
# - Total conversations
# - Word frequency analysis
# - Topic distribution
# - Conversation length statistics

Export Conversations

Export conversations to various formats:

# Export conversations
python export_conversations.py

# Exports to:
# - CSV format
# - Plain text format
# - Training format
# - Statistics export

Jupyter Notebook

Open the interactive Jupyter notebook for demonstrations:

# Start Jupyter notebook
jupyter notebook notebooks/fine_tune_gpt2.ipynb

# Or use JupyterLab
jupyter lab notebooks/text_generation_demo.ipynb

Project Structure

                gpt2-chatbot/

                ├── README.md                          # Main documentation

                ├── requirements.txt                   # Python dependencies

                ├── config.py                          # Configuration settings

                ├── LICENSE                            # License file

                ├── PROJECT_INFO.md                    # Project overview

                │

                ├── Core Modules

                │   ├── chatbot.py                     # Interactive chatbot

                │   ├── generate_text.py               # Text generation script

                │   ├── batch_generate.py              # Batch text generation

                │   ├── train.py                       # Fine-tuning script

                │   ├── conversation_manager.py       # Conversation management

                │   └── utils.py                       # Utility functions

                │

                ├── Advanced Features

                │   ├── advanced_evaluation.py         # Advanced evaluation metrics

                │   ├── advanced_train.py              # Multi-domain training

                │   ├── evaluate_model.py              # Model evaluation

                │   ├── model_comparison.py            # Model comparison tools

                │   ├── prompt_templates.py            # Prompt engineering

                │   └── response_quality.py            # Response quality scoring

                │

                ├── API & Services

                │   ├── app.py                         # Flask web API

                │   └── index.html                     # Web interface

                │

                ├── Data

                │   ├── train.txt                      # Training data

                │   ├── val.txt                        # Validation data

                │   ├── domain_technical.txt           # Technical domain data

                │   ├── domain_creative.txt            # Creative writing samples

                │   └── sample_prompts.txt            # Sample prompts

                │

                ├── Models

                │   └── (trained model checkpoints)

                │

                ├── Notebooks

                │   ├── fine_tune_gpt2.ipynb           # Fine-tuning notebook

                │   └── text_generation_demo.ipynb      # Generation demo

                │

                └── Conversations

                    └── (saved conversations)

Configuration Options

Model Configuration (config.py)

Customize model and training parameters in config.py:

# Model Configuration
MODEL_NAME = "gpt2"               # GPT-2 model variant (gpt2, gpt2-medium, gpt2-large, gpt2-xl)
MAX_LENGTH = 100                  # Maximum generation length
BATCH_SIZE = 4                    # Training batch size
LEARNING_RATE = 5e-5              # Learning rate
NUM_EPOCHS = 3                     # Number of training epochs
WARMUP_STEPS = 100                 # Warmup steps for LR scheduler

# Generation Parameters
TEMPERATURE = 0.7                 # Generation temperature (higher = more creative)
TOP_P = 0.9                       # Nucleus sampling
TOP_K = 50                        # Top-K sampling
REPETITION_PENALTY = 1.2          # Repetition penalty
DO_SAMPLE = True                  # Enable sampling

# Chatbot Configuration
CONTEXT_LENGTH = 5                 # Previous messages in context
SYSTEM_PROMPT = "You are a helpful AI assistant."

Advanced Features Usage

Text Generation

Generate text with customizable parameters:

from generate_text import generate_text
from config import MODEL_NAME, DEVICE

# Generate text from prompt
text = generate_text(
    prompt="Once upon a time",
    model_name=MODEL_NAME,
    max_length=100,
    temperature=0.7,
    top_p=0.9,
    top_k=50,
    device=DEVICE
)

# Returns generated text string

# Or use the chatbot class
from chatbot import GPTChatbot

chatbot = GPTChatbot(model_name='gpt2')
response = chatbot.generate_response("Hello, how are you?")
print(response)

Batch Text Generation

Generate text from multiple prompts efficiently:

from batch_generate import batch_generate

# Generate from list of prompts
prompts = [
    "Once upon a time",
    "In a galaxy far away",
    "The future of AI is"
]

results = batch_generate(
    prompts=prompts,
    model_name='gpt2',
    max_length=100,
    output_file='results.json'
)

# Returns list of generated texts
# Also saves to JSON file with metadata

Model Evaluation

Evaluate model performance with comprehensive metrics:

from evaluate_model import evaluate_model

# Evaluate model on test dataset
results = evaluate_model(
    model_name='gpt2',
    test_file='data/val.txt',
    device='cuda'
)

# Returns dictionary with:
# - perplexity: Language model perplexity
# - avg_generation_time: Average time per token
# - tokens_per_second: Generation speed
# - total_tokens: Total tokens processed

# Advanced evaluation
from advanced_evaluation import AdvancedEvaluator

evaluator = AdvancedEvaluator()
scores = evaluator.evaluate(
    reference="Reference text",
    candidate="Generated text"
)
# Returns BLEU, ROUGE-L, METEOR, semantic similarity

Model Comparison

Compare different GPT-2 variants to find the best model:

from model_comparison import compare_models

# Compare different GPT-2 variants
models = ['gpt2', 'gpt2-medium', 'gpt2-large']
results = compare_models(
    models=models,
    test_prompts=["Prompt 1", "Prompt 2"],
    max_length=100
)

# Returns comparison with:
# - Generation quality scores
# - Generation speed
# - Memory usage
# - Model size

# Helps choose best model for your use case

Advanced Prompt Engineering

Use pre-built templates and custom prompts for optimized generation:

from prompt_templates import PromptTemplates

templates = PromptTemplates()

# Use pre-built templates
prompt = templates.get_template('creative_writing')
# Returns: "Write a creative story about: {topic}"

# Custom prompt builder
custom_prompt = templates.build_prompt(
    style='technical',
    context='machine learning',
    instruction='explain'
)

# Available templates:
# - creative_writing, technical, conversational
# - question_answer, summarization, translation

API Usage Examples

Chat Endpoint (cURL)

Send a chat message and get response with intent and sentiment:

curl -X POST http://localhost:5000/api/chat \
  -H "Content-Type: application/json" \
  -d '{
    "message": "Hello, what is GPT-2?",
    "session_id": "user123"
  }'

# Response:
# {
#   "response": "GPT-2 is a transformer model...",
#   "session_id": "user123",
#   "timestamp": "2025-01-15T10:30:00"
# }

Text Generation (cURL)

Generate text from a prompt:

curl -X POST http://localhost:5000/api/generate \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Once upon a time",
    "max_length": 100,
    "temperature": 0.7
  }'

# Response:
# {
#   "generated_text": "Once upon a time, in a land far away...",
#   "length": 100
# }

Batch Generation (cURL)

Generate text from multiple prompts:

curl -X POST http://localhost:5000/api/batch \
  -H "Content-Type: application/json" \
  -d '{
    "prompts": ["Prompt 1", "Prompt 2", "Prompt 3"],
    "max_length": 100
  }'

# Response:
# {
#   "results": [
#     {"prompt": "Prompt 1", "generated": "..."},
#     {"prompt": "Prompt 2", "generated": "..."}
#   ]
# }

Model Evaluation (cURL)

Evaluate model performance:

curl -X POST http://localhost:5000/api/evaluate \
  -H "Content-Type: application/json" \
  -d '{
    "test_file": "data/val.txt",
    "model": "gpt2"
  }'

# Response:
# {
#   "perplexity": 25.3,
#   "avg_generation_time": 0.05,
#   "tokens_per_second": 20.0
# }

Python Requests Example

Use the API with Python requests library:

import requests

# Chat endpoint
response = requests.post(
    'http://localhost:5000/chat',
    json={
        'message': 'Hello, how are you?',
        'session_id': 'user123'
    }
)
data = response.json()
print(f"Bot: {data['response']}")
print(f"Intent: {data['intent']}")
print(f"Sentiment: {data['sentiment']}")

# Get context
context = requests.get('http://localhost:5000/context/user123')
print(context.json())

# Clear context
requests.delete('http://localhost:5000/context/user123')

GPT-2 Model Variants

Model	Parameters	Size	Use Case	Speed
gpt2	117M	~500 MB	Fast inference, basic tasks	Fastest
gpt2-medium	345M	~1.4 GB	Balanced quality/speed	Fast
gpt2-large	762M	~3.0 GB	Higher quality generation	Moderate
gpt2-xl	1.5B	~6.0 GB	Best quality, research	Slower

Dataset Information

Training Datasets

The project includes multiple domain-specific datasets for fine-tuning:

General training data (train.txt, val.txt)
Technical domain data
Creative writing samples
Conversation samples (Q&A pairs)
Science & technology topics
Business & economics content
Health & wellness topics
Education & learning content
Philosophy & ethics topics
History & culture content
Sample prompts (text and JSON formats)
Advanced training examples

Data Format

Training data is stored in plain text format (one example per line):

# train.txt format (one example per line)
Machine learning is a subset of artificial intelligence.
GPT-2 is a transformer-based language model.
Natural language processing enables computers to understand text.
Deep learning uses neural networks with multiple layers.
Text generation creates coherent sequences of words.

# For batch generation, JSON format:
[
  {"prompt": "Once upon a time", "max_length": 100},
  {"prompt": "In a galaxy far away", "max_length": 100}
]

Adding Custom Training Data

Add your own text data for fine-tuning:

# Simply append to training file
with open('data/train.txt', 'a', encoding='utf-8') as f:
    f.write("Your custom training text here.\n")
    f.write("Each line is a separate training example.\n")
    f.write("The model will learn from these examples.\n")

# Or create a new domain-specific file
with open('data/domain_custom.txt', 'w', encoding='utf-8') as f:
    f.write("Domain-specific training data...\n")

# Use in training
python train.py --train_file data/domain_custom.txt --output_dir models/custom

Troubleshooting & Best Practices

Common Issues

CUDA Out of Memory: Reduce batch_size in config.py, use smaller model (gpt2 instead of gpt2-large), or use CPU mode
Model Not Found: Ensure GPT-2 model downloads correctly from Hugging Face on first run
Slow Generation: Use smaller model (gpt2 or gpt2-medium) or reduce max_length
API Connection Error: Check if app.py is running on port 5000
Import Errors: Verify all dependencies installed: pip install -r requirements.txt
Context Too Long: Reduce CONTEXT_LENGTH in config.py or MAX_LENGTH
Repetitive Output: Adjust temperature (higher) or repetition_penalty (higher) in config.py

Best Practices

Training Data: Use diverse, high-quality text data for better results
Context Management: Keep CONTEXT_LENGTH between 3-7 for optimal performance
Batch Size: Use smaller batches (4-8) for limited GPU memory, especially with larger models
Learning Rate: Start with 5e-5 and adjust based on training loss
Temperature: Use 0.7-0.9 for creative text, 0.3-0.5 for more focused output
Model Selection: Start with gpt2 for speed, use gpt2-medium or larger for quality
Evaluation: Regularly evaluate perplexity on validation set during training
Session Management: Use unique session_ids for different users
API Rate Limiting: Implement rate limiting for production deployments
Logging: Monitor training logs for debugging and optimization

Performance Optimization

GPU Usage: Set CUDA_VISIBLE_DEVICES for multi-GPU systems
Model Selection: Use gpt2 for fastest inference, larger models for better quality
Batch Processing: Use batch_generate.py for processing multiple prompts efficiently
Caching: Cache model and tokenizer to avoid reloading
Context Pruning: Remove old context beyond CONTEXT_LENGTH
Generation Parameters: Adjust temperature, top_p, and top_k for desired output style

Contact Information

Get in Touch

Developer: Molla Samser
Designer & Tester: Rima Khatun

rskworld.in

help@rskworld.in support@rskworld.in

+91 93305 39277

License

This project is for educational purposes only. See LICENSE file for more details.

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GPT-2 Chatbot

Project Description

Project Screenshots

Core Features

GPT-2 Architecture

Context Management

Text Generation

Batch Generation

Model Evaluation

REST API Server

Advanced Features

Evaluation Metrics

Data Augmentation

Response Ranking

Conversation Analytics

REST API Endpoints

Technologies Used

Installation & Usage

Installation

Quick Setup

Training the Model

Interactive Chatbot

REST API Server

Batch Text Generation

Evaluation Metrics

Conversation Analytics

Export Conversations

Jupyter Notebook

Project Structure

Configuration Options

Model Configuration (config.py)

Advanced Features Usage

Text Generation

Batch Text Generation

Model Evaluation

Model Comparison

Advanced Prompt Engineering

API Usage Examples

Chat Endpoint (cURL)

Text Generation (cURL)

Batch Generation (cURL)

Model Evaluation (cURL)

Python Requests Example

GPT-2 Model Variants

Dataset Information

Training Datasets

Data Format

Adding Custom Training Data

Troubleshooting & Best Practices

Common Issues

Best Practices

Performance Optimization

Contact Information

Get in Touch

License