My Awesome AI Engineering Projects

Welcome to this cutting-edge AI Engineering project! This repository showcases a comprehensive exploration of various AI technologies, software engineering practices, and advanced programming concepts. It's structured to demonstrate a progressive learning journey through different aspects of AI and software development and is designed to follow my learnings at the AI Makerspace "LM-Engineering-Foundations-to-SLMs" bootcamp.

Table of Contents

• Week 1: Foundations and LLM Studies
• Week 2: Advanced Concepts and Implementations
• Week 3: AI Agents, CUDA, and DevOps

Week 1: Foundations and LLM Studies

This week focuses on establishing core concepts and diving into Large Language Model (LLM) studies.

Key Components

• hoc.py: Implementation of higher-order functions in Python.
• Latin Side Quest: A fascinating exploration of advanced NLP techniques applied to classical Latin:
  • DiachronicWordEmbeddings.py
  • Macron-AwareSubwordTokenization.py
  • MacronPositionalEncodingImproved.py
  • MacronPositionEncoding.py
  • Prosody-AwareAttentionMechanism.py

LLM Studies

A series of in-depth studies on Large Language Models:

• LLM_Studies-Nov-17-2024.md
• LLM_Studies-Nov-18-2024.md
• LLM_Studies-Nov-19-2024.md
• LLM_Studies-Nov-21-2024.md
• LLM_studies-Nov-22-2024.md

These studies cover various aspects of LLMs, including architecture, training methodologies, and applications.

Week 2: Advanced Concepts and Implementations

Week 2 delves deeper into AI and software engineering concepts, featuring a mix of theoretical studies and practical implementations.

Theoretical Explorations

• AfterKandV.md: Post-analysis of key AI concepts.
• AutoencodingNotes.md: Comprehensive notes on autoencoding techniques.
• CreatingDeepandWideNeuralNetworks.md: Guide to architecting complex neural networks.
• Positional_Encoding_Formula.md: Detailed explanation of positional encoding in transformer models.

Practical Implementations

• almost_orthogonal.py: Python script demonstrating almost orthogonal vector generation.
• llama_tests.py: Test suite for LLaMA model implementations.
• yi.py: Implementation related to the Yi language model.

DevOps and Infrastructure

• HardcoreKubernetesk3d.md: Advanced Kubernetes configurations using k3d.
• k3d-secure.yaml: Secure configuration for k3d clusters.

AI Model Development

• llamagguf-example: Rust implementation of LLaMA model quantization:

  • Cargo.lock
  • Cargo.toml
  • src/main.rs

• movies_demo_ml_python: Movie recommendation system using machine learning:

  • corpus_prep.py
  • corpus.txt
  • tokenizer/vocab.txt

• nvidia: NVIDIA-specific AI model implementations:

  • Dockerfile
  • model.py

Research and Language Studies

• Research.md: Compilation of research findings and insights.
• RustLifetimes.md: Deep dive into Rust's lifetime concept.
• RustTraits.md: Comprehensive guide to Rust traits.

Week 3: AI Agents, CUDA, and DevOps

The final week focuses on advanced AI applications, GPU computing, and DevOps practices.

AI Agents

Three innovative chatbot implementations in Rust:

1. comedian_chatbot:
2. rig_chatbot:
3. self_improving_perplexity_chatbot:

CUDA and GPU Computing

• check-nvidia-cuda.sh: Script to verify NVIDIA CUDA installation.
• cuda_program.cu: CUDA C program demonstrating GPU computation.
• cuda.py: Python interface for CUDA operations.
• InstallingCudaisBig.md: Comprehensive guide to CUDA installation.
• Rust_cuda_research.md: Research findings on integrating Rust with CUDA.

DevOps and MLOps

• k3d-secure.yaml: Enhanced secure configuration for k3d.
• llm-cli.md: Command-line interface guide for LLM operations.
• Modelfile: Configuration file for model deployment.
• UseEncryptionwithJenkins.md: Guide to implementing encryption in Jenkins pipelines.

Zarf Integration

Zarf-related configurations for AI deployment:

• Jenkinsfile
• WhyUseZarfforAI.md
• zarf-podinfo.yaml
• ZarfVLLM.yaml

Conclusion

This project demonstrates a comprehensive approach to AI engineering, covering everything from foundational concepts to cutting-edge implementations. It showcases proficiency in various programming languages, DevOps practices, and AI technologies, making it an impressive portfolio piece for AI engineering students and professionals.