A collection of AI and machine learning examples demonstrating practical implementations of cutting-edge technologies including Retrieval-Augmented Generation (RAG), quantum computing, and GPU-optimized training pipelines.

This repository showcases production-ready AI applications and experiments, with a focus on:

• Quantum Computing & AI: RAG systems for quantum computing documentation
• Deep Learning Infrastructure: GPU-optimized training pipelines with modular architecture
• IBM Cloud AI Integration: Examples using IBM Watsonx AI and Granite models
• Modern ML Practices: Best practices for scalable, configurable AI systems

Contains practical AI examples and demonstrations, with a primary focus on quantum computing applications in finance.

Main Highlight: RAG_quantum.ipynb

• Complete RAG (Retrieval-Augmented Generation) pipeline for quantum computing documentation
• Uses IBM Watsonx AI with Granite models for question-answering
• Implements vector similarity search with Milvus database
• Processes research papers from arXiv using Docling
• Technologies: IBM Watsonx, Granite Embeddings, LangChain, Hugging Face Transformers

Additional Files

• extra/: Experimental scripts for quantum computing financial analysis using OpenAI's API
• Various implementations of web scraping and content analysis tools

📖 View Examples Documentation

A modular, production-ready deep learning training pipeline optimized for GPU training.

Key Features:

• Modular architecture with Hydra configuration management
• Support for text classification with Hugging Face Transformers
• Docker containerization with NVIDIA CUDA runtime
• Gradient accumulation and learning rate scheduling
• Optional Weights & Biases integration for experiment tracking
• Configurable models (DistilBERT, BERT, RoBERTa, etc.)

Use Cases:

• Text classification tasks
• Sentiment analysis
• Custom NLP model training
• GPU-accelerated training experiments

📖 View GPU Pipeline Documentation

• Python 3.8+
• (Optional) CUDA-capable GPU for training pipeline
• (Optional) IBM Cloud account with Watsonx AI access for RAG examples

cd examples
# Set up environment variables (never commit actual credentials!)
export WATSONX_APIKEY="your_api_key"
export WATSONX_PROJECT_ID="your_project_id"
export WATSONX_URL="your_watsonx_url"

# Launch Jupyter and open RAG_quantum.ipynb
jupyter notebook RAG_quantum.ipynb

cd gpu-training-pipeline

# Install dependencies
pip install -r requirements.txt

# Run training with default configuration
python train.py

# Or use Docker
docker build -t gpu-training-pipeline .
docker run --gpus all gpu-training-pipeline

• IBM Watsonx AI: Enterprise-grade AI platform for LLMs
• IBM Granite Models: Efficient embedding and language models
• PyTorch: Deep learning framework for GPU training
• Hugging Face Transformers: State-of-the-art NLP models
• LangChain: Framework for building LLM applications
• Milvus: High-performance vector database
• Docling: Advanced document parsing and conversion
• Hydra: Configuration management for ML experiments
• Docker: Containerization for reproducible environments

• Build intelligent Q&A systems for technical documentation
• Process and understand quantum computing research papers
• Demonstrate AI applications in emerging technologies
• Showcase integration between IBM Cloud AI services

• Train custom text classification models
• Experiment with different transformer architectures
• Scale training with GPU acceleration
• Manage ML experiments with structured configurations

This repository serves as a demonstration of AI capabilities and best practices. Feel free to explore, learn, and adapt these examples for your own projects.

Please refer to individual project directories for specific licensing information.

• IBM Watsonx AI Documentation
• Hugging Face Transformers
• LangChain Documentation
• PyTorch Documentation

Note: Some examples require API keys and credentials. Ensure you have the necessary access before running the notebooks and scripts.