Nexus Documentation Index

Comprehensive documentation for Nexus - a modular deep learning framework covering cutting-edge models in reinforcement learning, multimodal learning, and graph neural networks.

1. Reinforcement Learning (01_reinforcement_learning)

Value-Based Methods: DQN, Double DQN, Dueling DQN, Rainbow, C51, QR-DQN, IQN
Policy Gradient: A2C, PPO, TRPO, SAC, TD3, DDPG
Offline RL: Conservative Q-Learning (CQL), Implicit Q-Learning (IQL), Decision Transformer
Alignment: RLHF, DPO, KTO, RRHF, RAFT
Multi-Agent: QMIX, MADDPG, CommNet
Model-Based: World Models, MuZero, Dreamer
Exploration: ICM, RND, NGU
Sequence-Based: Decision Transformer, Trajectory Transformer
Reward Modeling: Preference learning, reward shaping
Planning: MCTS, model predictive control

2. Attention Mechanisms (02_attention_mechanisms)

Multi-head attention
Flash Attention
Linear attention
Sparse attention patterns

3. State Space Models (03_state_space_models)

Mamba
S4 (Structured State Spaces)
Variants and extensions

4. Hybrid Architectures (04_hybrid_architectures)

Combining attention and state space models
Transformer-SSM hybrids

5. Positional Encodings (05_positional_encodings)

Absolute positional encoding
Relative positional encoding
Rotary Position Embedding (RoPE)
ALiBi

6. Architecture Components (06_architecture_components)

MoE: Mixture of Experts
Normalization: LayerNorm, RMSNorm, GroupNorm
Activation: GELU, SwiGLU, variants

7. Inference Optimizations (07_inference_optimizations)

KV-cache optimization
Speculative decoding
Quantization techniques

8. Computer Vision (08_computer_vision)

Vision Transformers: ViT, DeiT, Swin Transformer
Object Detection: DETR, Deformable DETR
Segmentation: Segment Anything (SAM), Mask2Former
NeRF & 3D: Neural Radiance Fields, 3D Gaussian Splatting

9. Generative Models (09_generative_models)

Diffusion: DDPM, DDIM, Stable Diffusion, Consistency Models
Audio/Video: AudioLDM, Video Diffusion, Sora-style models

10. NLP & LLM (10_nlp_llm)

Reasoning: Chain-of-Thought, Tree-of-Thought, Self-Consistency
RAG: Retrieval-Augmented Generation
PEFT: LoRA, QLoRA, Adapter methods
Quantization: GPTQ, AWQ, GGUF
Pruning: Structured and unstructured pruning
Distillation: Knowledge distillation techniques
Structured Generation: Constrained decoding, grammar-based
Embeddings: Contrastive learning, instruction embeddings
Tokenization: BPE, SentencePiece, Unigram

11. Training Infrastructure (11_training_infrastructure)

Distributed training
Mixed precision training
Gradient accumulation
Optimizer strategies
Learning rate schedules
Checkpointing

12. Self-Supervised Learning (12_self_supervised_learning)

Contrastive learning
Masked modeling
Self-distillation

13. Multimodal Models (13_multimodal_models) ✓ DOCUMENTED

Comprehensive documentation for vision-language models that combine visual and textual understanding.

Available Documentation

Fully Documented Models

LLaVA-RLHF (18.5KB, 460 lines)
- Large Language and Vision Assistant with RLHF alignment
- Experience bank and quality assessment
- Hallucination reduction techniques
- Code: nexus/models/multimodal/llava_rlhf.py
Qwen2-VL (18.5KB, 464 lines)
- Multimodal Rotary Position Embedding (M-RoPE)
- Dynamic resolution without interpolation
- 2D/3D position encoding for images/videos
- Code: nexus/models/multimodal/qwen2_vl.py

Placeholder Files (To Be Documented)

PaLM-E - Embodied multimodal language model for robotics
- Code: nexus/models/multimodal/palm_e.py
HiViLT - Hierarchical Vision-Language Transformer
- Code: nexus/models/multimodal/hivilt.py
LLaVA-NeXT - Advanced LLaVA with dynamic resolution
- Code: nexus/models/multimodal/llava_next.py
Molmo - Fully open vision-language model from AI2
- Code: nexus/models/multimodal/molmo.py
Phi-3-Vision - Lightweight model with 128K context
- Code: nexus/models/multimodal/phi3_vision.py
BiomedCLIP - Biomedical vision-language model
- Code: nexus/models/multimodal/biomedclip.py
NVLM - NVIDIA's multimodal model (implementation pending)
- Code: TBD

Key Features

Vision-language alignment techniques
Cross-modal fusion architectures
Contrastive learning (CLIP-style)
LLM-centric approaches
Domain specialization (medical, robotics)

14. Graph Neural Networks (14_graph_neural_networks) ✓ DOCUMENTED

Comprehensive documentation for graph learning architectures.

Available Documentation

Fully Documented Models

GPS (GPS: General, Powerful, Scalable Graph Transformer) (24KB, 562 lines)
- Combines local MPNN with global attention
- Laplacian and random walk positional encodings
- Modular design for diverse graph tasks
- Code: nexus/models/gnn/gps.py

Placeholder Files (To Be Documented)

Base GNN - Foundational message passing with multi-head attention
- Code: nexus/models/gnn/base_gnn.py
Message Passing - Adaptive message passing layer
- Code: nexus/models/gnn/message_passing.py
GraphSAGE - Inductive learning via sampling and aggregation
- Code: nexus/models/gnn/graph_sage.py
GATv2 - Graph attention with dynamic attention mechanism
- Code: nexus/models/gnn/gatv2.py
Exphormer - Sparse graph transformer with expander graphs
- Code: nexus/models/gnn/exphormer.py

Key Concepts

Message passing framework
Graph attention mechanisms
Positional encodings (LapPE, RWSE)
Scalability through sampling and sparse attention
Hybrid local-global architectures

Documentation Structure

Each model documentation follows a consistent 10-section structure:

1. Overview & Motivation

Key innovations and contributions
Problem setting and use cases
Why this model/approach was developed

2. Theoretical Background

Foundational concepts
Related work and context
Key insights and intuitions

3. Mathematical Formulation

Rigorous mathematical definitions
Loss functions and objectives
Algorithmic details

4. High-Level Architecture

System diagrams and visualizations
Component interactions
Data flow through the model

5. Implementation Details

Code structure and organization
Key classes and functions
Integration with Nexus framework

6. Code Walkthrough

Step-by-step usage examples
Training and inference code
Best practices

7. Optimization Tricks

Training optimizations
Inference acceleration
Memory management
Hyperparameter tuning

8. Experiments & Results

Benchmark performance
Ablation studies
Comparison with baselines
Scalability analysis

9. Common Pitfalls

Frequent mistakes and how to avoid them
Edge cases and error handling
Debugging tips

10. References

Original papers
Code repositories
Related work
Datasets and benchmarks

Implementation Philosophy

Modular Design

All models in Nexus follow a consistent architecture:

from nexus.core.base import NexusModule
from nexus.core.mixins import ConfigValidatorMixin, FeatureBankMixin

class MyModel(ConfigValidatorMixin, FeatureBankMixin, NexusModule):
    def __init__(self, config):
        super().__init__(config)
        # Validate configuration
        self.validate_config(config, required_keys=[...])

        # Initialize components
        ...

    def forward(self, *args, **kwargs):
        # Forward pass logic
        ...
        return outputs

Key Components

NexusModule: Base class for all models

Configuration management
Device handling
State serialization

ConfigValidatorMixin: Configuration validation

Type checking
Required field validation
Value range validation

FeatureBankMixin: Feature caching and replay

Circular buffer for features
Memory-efficient storage
Integration with experience replay

HierarchicalVisualizer: Visualization support

Model architecture diagrams
Attention weight visualization
Training dynamics plotting

Quick Navigation

By Research Area

Vision-Language: Multimodal Models
Graph Learning: Graph Neural Networks
Deep RL: Reinforcement Learning
Efficient Inference: Inference Optimizations
Foundation Models: NLP & LLM

By Application

Robotics: PaLM-E, RL algorithms, World Models
Healthcare: BiomedCLIP, medical imaging models
Recommendation: Graph neural networks
Content Generation: Diffusion models, LLMs
Scientific Computing: Graph models, molecular property prediction

By Implementation Status

Fully Documented (1,027 lines):

✓ Multimodal Models: README (6.3KB), LLaVA-RLHF (18.5KB), Qwen2-VL (18.5KB)
✓ Graph Neural Networks: README (10.4KB), GPS (24KB)

Placeholder Files Created (ready for documentation):

Multimodal: PaLM-E, HiViLT, LLaVA-NeXT, Molmo, Phi-3-Vision, BiomedCLIP, NVLM
GNN: Base GNN, Message Passing, GraphSAGE, GATv2, Exphormer

To Be Created:

All other categories (RL, Attention, SSM, CV, etc.)

Documentation Statistics

Current Status

Total Documentation Files: 15 markdown files
Total Lines Written: 1,027 lines
Average Documentation Size: 68.5 lines per file
Comprehensive Guides: 3 (LLaVA-RLHF, Qwen2-VL, GPS)
Category READMEs: 2 (Multimodal, GNN)

Coverage by Category

Category	Models	Documented	Placeholder	Coverage
Multimodal	9	2	7	22%
GNN	6	1	5	17%
RL	~40	0	0	0%
Other	~50	0	0	0%

Detailed Documentation Examples

The comprehensive guides include:

Mathematical rigor: Full derivations and formulations
Architecture diagrams: Visual representations in ASCII art
Code examples: Complete training and inference workflows
Optimization techniques: Memory, speed, and quality improvements
Experimental results: Benchmark comparisons and ablations
Common pitfalls: Real-world debugging scenarios

Contributing to Documentation

Adding New Model Documentation

Create the file: docs/<category>/<model_name>.md
Follow the template: Use the 10-section structure
- Each section should be comprehensive yet accessible
- Include mathematical formulations where appropriate
- Provide working code examples
Reference implementation: Link to actual code in Nexus/nexus/models/
- Explain key design decisions
- Show how to use the model
- Document configuration options
Add to category README: Update the category's README.md with model summary
Update this index: Add entry to DOCUMENTATION_INDEX.md

Documentation Standards

Mathematical Notation:

Use LaTeX formatting in code blocks: $$...$$, $...$
Define all variables and symbols
Include dimensionality annotations

Code Examples:

Always use absolute imports
Include necessary dependencies
Test code snippets for correctness
Add comments explaining non-obvious logic

Diagrams:

Use ASCII art for architecture diagrams
Keep diagrams readable in monospace font
Include data dimensions and flow directions

References:

Cite original papers with arXiv links
Include official implementations when available
Link to relevant datasets and benchmarks

Future Documentation Plans

Phase 1: Complete Multimodal & GNN (Current)

✓ Create category READMEs
✓ Document 2-3 flagship models per category
⏳ Complete remaining placeholder files

Phase 2: Core Components

Document RL algorithms (value-based, policy gradient)
Document attention mechanisms
Document training infrastructure

Phase 3: Advanced Topics

Document specialized models (NeRF, Diffusion, etc.)
Add tutorial notebooks
Create integration guides

Phase 4: Polish & Extend

Add interactive visualizations
Create video walkthroughs
Develop benchmarking suite

Additional Resources

External Documentation

Papers & Surveys

Community

GitHub Issues: Report bugs and request features
Discussions: Ask questions and share ideas
Pull Requests: Contribute code and documentation

License & Citation

Nexus is an educational framework for exploring cutting-edge deep learning architectures.

If you use this documentation or code, please cite:

@software{nexus2025,
  title = {Nexus: A Modular Deep Learning Framework},
  year = {2025},
  url = {https://github.com/yourusername/Nexus}
}

Last Updated: February 6, 2025 Total Documentation: 1,027 lines across 15 files Status: Active development - Phase 1 in progress

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Nexus Documentation Index

Table of Contents

1. Reinforcement Learning (01_reinforcement_learning)

2. Attention Mechanisms (02_attention_mechanisms)

3. State Space Models (03_state_space_models)

4. Hybrid Architectures (04_hybrid_architectures)

5. Positional Encodings (05_positional_encodings)

6. Architecture Components (06_architecture_components)

7. Inference Optimizations (07_inference_optimizations)

8. Computer Vision (08_computer_vision)

9. Generative Models (09_generative_models)

10. NLP & LLM (10_nlp_llm)

11. Training Infrastructure (11_training_infrastructure)

12. Self-Supervised Learning (12_self_supervised_learning)

13. Multimodal Models (13_multimodal_models) ✓ DOCUMENTED

Available Documentation

Fully Documented Models

Placeholder Files (To Be Documented)

Key Features

14. Graph Neural Networks (14_graph_neural_networks) ✓ DOCUMENTED

Available Documentation

Fully Documented Models

Placeholder Files (To Be Documented)

Key Concepts

Documentation Structure

1. Overview & Motivation

2. Theoretical Background

3. Mathematical Formulation

4. High-Level Architecture

5. Implementation Details

6. Code Walkthrough

7. Optimization Tricks

8. Experiments & Results

9. Common Pitfalls

10. References

Implementation Philosophy

Modular Design

Key Components

Quick Navigation

By Research Area

By Application

By Implementation Status

Documentation Statistics

Current Status

Coverage by Category

Detailed Documentation Examples

Contributing to Documentation

Adding New Model Documentation

Documentation Standards

Future Documentation Plans

Phase 1: Complete Multimodal & GNN (Current)

Phase 2: Core Components

Phase 3: Advanced Topics

Phase 4: Polish & Extend

Additional Resources

External Documentation

Papers & Surveys

Community

License & Citation