HandsOnLLM/Hands-On-Large-Language-Models
Hands On Large Language Models
This project is an educational resource focused on the internal mechanics and design principles of transformer-based neural networks. It provides a structured guide to the fundamental components of generative artificial intelligence, including sequence modeling, semantic embeddings, and the mathematical foundations of large language models.
The repository distinguishes itself through a heavy emphasis on visual documentation, utilizing diagrams and step-by-step explanations to clarify how data flows through complex neural architectures. It serves as a technical reference for developers seeking to understand the operational logic of these systems during the development and deployment process.
Beyond foundational theory, the project covers practical optimization techniques for large-scale neural networks. It explains methods such as weight quantization and mixture of experts routing, providing guidance on how to improve memory efficiency and execution speed for models running on resource-constrained hardware.
Features
- LLM Education - Acts as a technical guide explaining the internal mechanics and design patterns of large language models.
- Transformer Tutorials - Serves as a comprehensive educational resource for learning the design principles and mechanics of transformer architectures.
- Large Language Model Tutorials - Offers visual tutorials and educational guides to help developers understand how large language models function.
- Large Language Model Optimization - Focuses on applying advanced optimization techniques to improve the performance and memory efficiency of large language models.
- Awesome List - A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.
- Generative AI Development - Provides foundational knowledge on sequence modeling and embeddings for building generative artificial intelligence systems.
- Sequence Models - Covers transformer-based sequence modeling as a core component of modern generative artificial intelligence.
- Architecture Visualizers - Uses diagrams and step-by-step visual explanations to clarify the internal operational logic of neural architectures.
- Machine Learning Tutorials - Provides a collection of diagrams and tutorials illustrating data flow through neural networks.
- Model Performance Optimization - Explains model optimization techniques like quantization and mixture of experts to improve performance and efficiency.
- Model Quantization - Provides techniques for weight quantization to reduce memory footprint and accelerate inference on resource-constrained hardware.
- Multi-Head Attention Mechanisms - Details the multi-head attention mechanism used to capture semantic relationships within transformer architectures.
- Books and Guides - Hands-on guide to building and deploying LLMs.
- Learning Resources - Practical guide to building LLM applications.
- Optimization Guides - Provides a reference for understanding model compression techniques to improve performance and efficiency.
- Mixture of Experts - Provides guidance on implementing and understanding mixture of experts routing for efficient model inference.
- Vector Embeddings - Explains the mapping of tokens into vector-space semantic embeddings for contextual meaning.
- System Architecture Visualizers - Utilizes system architecture visualizers to illustrate data flow through complex neural networks.
- Backpropagation - Explains the mathematical foundations of backpropagation used to calculate gradients for training neural networks.
Star history
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