Master AI from Basics to Deployment: LLMAIX2001a Course from the ABC4RD(AI)

"There is much that is new and interesting in this work. Unfortunately, everything new is uninteresting, and everything interesting is not new" - Landau.

In course LLMAIX2001a we will build a Storyteller AI Large Language Model (LLM). Hand in hand, you'll be able to create, refine and illustrate little stories with the AI. We are going to build everything end-to-end from basics to a functioning web app similar to ChatGPT, from scratch in Python, C and CUDA, and with minimal computer science prerequisites. By the end you should have a relatively deep understanding of AI, LLMs, and deep learning more generally.

LLMAIX2001a: Building a Storyteller AI Curriculum

ABC4RD(AI) Academy

Section 01: Bigram Language Model (Introduction to Language Modeling)

Begin with the basics of language modeling by building a Bigram model. Learn how to predict the next word in a sequence, laying the foundation for more complex models.

Section 02: Micrograd (Fundamentals of Machine Learning and Backpropagation)

Dive into machine learning concepts by implementing backpropagation from scratch using a micrograd library. Understand the core mechanics that power neural networks.

Section 03: N-gram Model (Introduction to Multi-layer Perceptron, Matmul, GELU)

Extend your language modeling skills to N-grams. Explore how multi-layer perceptrons (MLPs) and activation functions like GELU enhance model performance.

Section 04: Attention Mechanisms (Attention, Softmax, Positional Encoding)

Learn about attention mechanisms, a crucial component in modern LLMs. Implement softmax functions and positional encoding to improve the model’s understanding of context.

Section 05: Transformer Architecture (Transformer, Residual Connections, Layernorm, GPT-2)

Study the transformer architecture, including residual connections and layer normalization. Implement a scaled-down version of GPT-2 to solidify your understanding.

Section 06: Tokenization (minBPE, Byte Pair Encoding)

Master the process of tokenization using byte pair encoding (BPE). Learn how to preprocess text data for efficient use in your AI models.

Section 07: Optimization Techniques (Initialization, Optimization Algorithms, AdamW)

Delve into the optimization techniques essential for training large models. Learn about various initialization strategies and optimization algorithms, focusing on AdamW.

Section 08: Need for Speed I: Device Optimization (CPU, GPU, TPU)

Explore how to optimize your model's performance across different devices, including CPUs, GPUs, and TPUs. Understand the hardware considerations in AI development.

Section 09: Need for Speed II: Precision Optimization (Mixed Precision Training, fp16, bf16, fp8)

Study precision optimization techniques like mixed precision training to enhance computational efficiency without sacrificing model accuracy.

Section 10: Need for Speed III: Distributed Training (Distributed Optimization, DDP, ZeRO)

Learn how to scale your model training across multiple devices using distributed training techniques such as Distributed Data Parallel (DDP) and ZeRO.

Section 11: Working with Datasets (Data Loading, Synthetic Data Generation)

Gain expertise in handling large datasets, focusing on data loading techniques and generating synthetic data to improve model robustness.

Section 12: Inference I: Efficient Inference with kv-cache (Key-Value Cache)

Optimize your model’s inference phase by implementing key-value caches, reducing latency and improving performance.

Section 13: Inference II: Model Quantization (Quantization Techniques)

Explore quantization techniques to reduce model size and improve inference speed while maintaining accuracy.

Section 14: Finetuning I: Supervised Fine-Tuning (SFT, PEFT, LoRA, Chatbot Development)

Fine-tune your AI model using supervised learning techniques like SFT and PEFT. Learn to adapt your model for specific tasks, including chatbot development using LoRA.

Section 15: Finetuning II: Reinforcement Learning (RLHF, PPO, DPO)

Dive into reinforcement learning-based fine-tuning techniques such as Reinforcement Learning with Human Feedback (RLHF) and Proximal Policy Optimization (PPO).

Section 16: Deployment (API Development, Web Application Integration)

Learn how to deploy your AI model by creating APIs and integrating them into web applications, making your model accessible to users.

Section 17: Multimodal AI (VQVAE, Diffusion Transformer)

Conclude the course by exploring multimodal AI, integrating different data types such as images and text. Implement advanced architectures like VQVAE and diffusion transformers.

By the end of the LLMAIX2001a course, students will have built a complete Storyteller AI, from the initial language model to a fully deployable web application, gaining deep insights into AI, machine learning, and deep learning processes.

Additional Material for LLMAIX2001a: Building a Storyteller AI

ABC4RD(AI) Academy

In addition to the core curriculum, the following topics are recommended for further study to enhance your understanding and proficiency in AI development:

Programming Languages:

Assembly, C, Python

Data Types:

Integer, Float, String (ASCII, Unicode, UTF-8)

Tensor Operations:

Shapes, Views, Strides, Contiguous Memory Layout

Deep Learning Frameworks:

PyTorch, JAX

Neural Network Architectures:

GPT (1, 2, 3, 4), Llama (RoPE, RMSNorm, GQA), Mixture of Experts (MoE)

Multimodal AI:

Images, Audio, Video, Vector Quantized Variational Autoencoder (VQVAE), Vector Quantized Generative Adversarial Network (VQGAN), Diffusion Models

These topics will provide you with a deeper insight into the underlying technologies and advanced concepts that support the development and optimization of AI systems. Exploring these areas will further solidify your knowledge and prepare you for more complex AI challenges.

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