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Multimodal Memory LLM Agent

Welcome to the comprehensive documentation for the Multimodal Memory LLM Agent project. This framework provides a modular and extensible architecture for building advanced AI applications with large language models (LLMs), multimodal capabilities, and persistent memory.

Core Modules

Deep Learning Foundations

Comprehensive tutorial on deep learning from fundamentals to modern architectures:

  • History of neural networks from perceptrons to deep learning revolution
  • Convolutional Neural Networks (CNNs) and major architectures
  • Optimization techniques, regularization methods, and advanced training
  • Modern architectures, semi-supervised and self-supervised learning
  • Mathematical foundations and implementation guides

Self-Supervised Learning

Understand the principles and evolution of self-supervised learning:

  • Foundations of SSL from word embeddings to modern vision-language models
  • Evolution of language models and modality-specific SSL approaches
  • Multimodal self-supervised learning and contrastive methods
  • Training strategies, scaling laws, and theoretical foundations
  • Practical implementation guides and current research directions

Transformer Fundamentals

Learn about the core concepts of Transformer architecture:

  • Evolution from RNNs with attention to full Transformer models
  • Self-attention mechanisms and multi-head attention
  • Encoder-decoder architecture and positional encodings
  • Implementation details and code examples

Multimodal Embeddings

Comprehensive guide to generating embeddings across different modalities:

  • Text embeddings from Word2Vec to modern transformer-based approaches
  • SentenceTransformers framework and popular models like all-MiniLM-L6-v2
  • Siamese/Triplet architectures with various loss functions (triplet, contrastive, MNRL)
  • Vision-language models (CLIP, ViT), audio embeddings (Wav2Vec 2.0, Whisper)
  • Multimodal fusion techniques and cross-modal understanding

LLM Frameworks and Architectures

Dive into the technical details of LLM implementation:

  • Evolution from RNNs to Transformer architectures
  • Optimization techniques for inference and deployment
  • Integration with various LLM providers and frameworks

Memory Systems

Understand how persistent memory enhances LLM capabilities:

  • Context window management and conversation history
  • Vector-based retrieval for semantic search
  • Structured knowledge storage and retrieval
  • LangChain and LangGraph memory architectures
  • Model Context Protocol (MCP) for standardized memory systems

Tool Calling and Agent Capabilities

Explore the implementation of LLM agents with tool-calling capabilities:

  • Function calling and ReAct (Reasoning and Acting) approaches
  • Model Context Protocol (MCP) for standardized context injection
  • Multi-agent systems and agentic workflows
  • Framework implementations across OpenAI, LangChain, LlamaIndex, AutoGen, and CrewAI
  • Tool learning and evaluation benchmarks

Multi-Modal Language Models

Explore the evolution and capabilities of multi-modal language models:

  • Historical evolution from visual-semantic embeddings to transformer era
  • Vision-Language Models (VLMs) including CLIP, BLIP, LLaVA, and Flamingo
  • Cross-modal attention mechanisms and mathematical foundations
  • Large-scale pre-training approaches and modern architectures

Advanced Topics

Advanced Transformer Techniques

Explore cutting-edge modifications and optimizations for Transformers:

  • Architectural innovations addressing limitations of original Transformers
  • Efficient attention mechanisms for reduced complexity
  • Position encoding improvements for longer sequences
  • Memory-efficient implementations and inference optimizations

GPT Architecture Evolution

Comprehensive analysis of architectural advances from GPT-2 to modern models:

  • Evolution from GPT-2 baseline to GPT-oss and GPT-5 architectures
  • Key innovations: RoPE, SwiGLU, MoE, GQA, sliding window attention
  • MXFP4 quantization and efficiency optimizations
  • Practical implementation examples with official OpenAI code
  • Comparison with Qwen3 and other modern architectures

Inference Optimization

Discover techniques to optimize LLM inference for production deployment:

  • Computational efficiency improvements (KV caching, Flash Attention)
  • Memory optimization strategies (quantization, pruning)
  • Model compression techniques (distillation, pruning)
  • Hardware acceleration and system-level optimizations

Physical AI in Autonomous Driving

Getting Started

Explore the documentation for each module to understand the architecture, implementation details, and usage examples. The project provides a flexible framework that can be adapted to various use cases and deployment scenarios.