Foundation Models Handbook
A comprehensive guide to foundation models, covering neural architectures, transformers, large-scale language and multimodal systems, and advanced augmentation techniques.
This handbook is inspired by the need for a comprehensive resource on Foundation Models, building on advancements in large-scale pre-training and cross-domain applications. All credit for the conceptual framework goes to the foundation models community, including pivotal tools like Hugging Face Transformers, DeepSpeed, and FairScale. I’ve curated and structured the content to provide a cohesive learning path, adding practical examples and hands-on guidance to enhance the educational experience.
Handbook Sections
Section I: Foundation Models and Their Applications
Goal: Introduce the concept of foundation models and their broad impact across AI applications.
Read section →Section II: NLP and Computer Vision
Goal: Explore the role of foundation models in natural language processing and computer vision tasks.
Read section →Section III: RNN and CNN
Goal: Survey recurrent and convolutional neural networks as precursors to modern foundation models.
Read section →Section IV: Early Transformer Variants
Goal: Examine early transformer models that laid the groundwork for large-scale architectures.
Read section →Section V: Self-Attention and Transformers
Goal: Introduce the self-attention mechanism and its role in transformer architectures.
Read section →Section VI: Efficient Transformers
Goal: Investigate transformer variants designed for improved computational efficiency.
Read section →Section VII: Parameter-Efficient Tuning
Goal: Explore methods for fine-tuning models with minimal parameter updates.
Read section →Section VIII: Language Model Pretraining
Goal: Examine techniques for pretraining large language models on vast datasets.
Read section →Section IX: Large Language Models
Goal: Survey the architecture and capabilities of large-scale language models.
Read section →Section X: Scaling Law
Goal: Analyze scaling laws governing performance improvements in large models.
Read section →Section XI: Instruction Tuning and RLHF
Goal: Explore fine-tuning techniques using instructions and reinforcement learning with human feedback.
Read section →Section XII: Efficient LLM Training
Goal: Investigate methods for optimizing the training of large language models.
Read section →Section XIII: Efficient LLM Inference
Goal: Examine techniques for faster and resource-efficient LLM inference.
Read section →Section XIV: Compress and Sparsify LLM
Goal: Explore compression and sparsification methods for large language models.
Read section →Section XV: LLM Prompting
Goal: Survey prompting strategies for optimizing large language model performance.
Read section →Section XVI: Vision Transformers
Goal: Introduce transformer-based architectures for computer vision tasks.
Read section →Section XVII: Diffusion Models
Goal: Examine diffusion models for generative tasks in vision and beyond.
Read section →Section XVIII: Image Generation
Goal: Explore techniques for generating high-quality images using foundation models.
Read section →Section XIX: Multimodal Pretraining
Goal: Investigate pretraining strategies for models combining language, vision, and other modalities.
Read section →Section XX: Large Multimodal Models
Goal: Survey large-scale models integrating multiple modalities for unified tasks.
Read section →Section XXI: Tool Augmentation
Goal: Explore how foundation models leverage external tools to enhance functionality.
Read section →Section XXII: Retrieval Augmentation
Goal: Examine retrieval-based methods to improve model performance and context awareness.
Read section →Related Handbooks
- Natural Language Processing Handbook - Master language modeling and transformer architectures
- Explainable AI Handbook - Explore interpretability techniques for AI models
- Reinforcement Learning Handbook - Explore decision-making and optimization techniques
Learning Path
- Begin with the fundamentals of foundation models, their applications, and early architectures like RNNs and CNNs
- Progress through the evolution of transformers, from self-attention to efficient variants and tuning methods
- Explore large language models, their pretraining, scaling, and optimization techniques
- Examine vision transformers, diffusion models, and multimodal systems for integrated tasks
- Discover advanced augmentation techniques, including tool integration and retrieval enhancement