# labmlai/annotated_deep_learning_paper_implementations architecture

> Architecture diagram of the GitHub repository labmlai/annotated_deep_learning_paper_implementations, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-28). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/labmlai/annotated_deep_learning_paper_implementations
- Repository: https://github.com/labmlai/annotated_deep_learning_paper_implementations

## Overview

labml_nn is a collection of annotated PyTorch implementations of neural networks and related algorithms, rather than one application with a single runtime. The principal workflow is to choose an implementation for a learning or generation task, provide task data or prompts, and use its model or algorithm to produce predictions, generated content, or learned strategies. The README documents broad capability areas; the tree supports their module locations, but sampled excerpts do not establish most cross-module call relationships.

Main entry points: [`labml_nn/transformers`](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/transformers) (Transformer Variants), [`labml_nn/neox`](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/neox) (GPT-NeoX), [`labml_nn/lora`](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/lora) (LoRA), [`labml_nn/sampling`](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/sampling) (Text Sampling)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_language["Language Models"]
  node_transformers["Transformer Variants"]
  node_neox["GPT-NeoX"]
  node_lora["LoRA"]
  node_sampling["Text Sampling"]
end

subgraph group_generation["Generative Models"]
  node_diffusion["Diffusion Models"]
  node_gans["GAN Variants"]
  node_sketch["Sketch RNN<br/>[__init__.py]"]
end

subgraph group_learning["Learning Algorithms"]
  node_rl["Reinforcement Learning"]
  node_cfr["Game Strategy CFR"]
  node_distillation["Model Distillation"]
  node_scaling["Scalable Training"]
end

subgraph group_building["Model Building Blocks"]
  node_vision["Vision Models"]
  node_graphs["Graph Networks"]
  node_recurrent["Recurrent Networks"]
  node_activations["Activations"]
  node_normalization["Normalization Layers"]
end

subgraph group_support["Shared Support"]
  node_optimizers["Optimizers"]
  node_helpers["Training Helpers"]
end

node_engineer(("Researcher"))
node_task["Task Data or Prompt"]
node_results["Model or Algorithm Results"]

node_engineer -->|"defines task"| node_task
node_task -->|"selects implementation"| node_transformers
node_task -->|"selects implementation"| node_neox
node_task -->|"selects implementation"| node_lora
node_task -->|"selects method"| node_sampling
node_task -->|"selects implementation"| node_diffusion
node_task -->|"selects implementation"| node_gans
node_task -->|"selects implementation"| node_sketch
node_task -->|"selects algorithm"| node_rl
node_task -->|"selects algorithm"| node_cfr
node_task -->|"selects method"| node_distillation
node_task -->|"selects technique"| node_scaling
node_task -->|"selects implementation"| node_vision
node_task -->|"selects implementation"| node_graphs
node_task -->|"selects implementation"| node_recurrent
node_task -->|"selects component"| node_activations
node_task -->|"selects component"| node_normalization
node_transformers -->|"produces predictions"| node_results
node_neox -->|"generates or evaluates"| node_results
node_lora -->|"adapts model"| node_results
node_sampling -->|"selects tokens"| node_results
node_diffusion -->|"generates images"| node_results
node_gans -->|"generates images"| node_results
node_sketch -->|"generates sketches"| node_results
node_rl -->|"learns policies"| node_results
node_cfr -->|"learns strategies"| node_results
node_distillation -->|"trains smaller models"| node_results
node_scaling -->|"enables scaled runs"| node_results
node_vision -->|"produces predictions"| node_results
node_graphs -->|"produces predictions"| node_results
node_recurrent -->|"produces predictions"| node_results
node_activations -->|"transforms features"| node_results
node_normalization -->|"normalizes features"| node_results

click node_transformers "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/transformers"
click node_neox "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/neox"
click node_lora "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/lora"
click node_sampling "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/sampling"
click node_diffusion "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/diffusion"
click node_gans "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/gan"
click node_sketch "https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/sketch_rnn/__init__.py"
click node_rl "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/rl"
click node_cfr "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/cfr"
click node_distillation "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/distillation"
click node_scaling "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/scaling"
click node_vision "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn"
click node_graphs "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/graphs"
click node_recurrent "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn"
click node_activations "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/activations"
click node_normalization "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/normalization"
click node_optimizers "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/optimizers"
click node_helpers "https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/helpers"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_transformers,node_neox,node_lora,node_sampling toneBlue
class node_diffusion,node_gans,node_sketch toneAmber
class node_rl,node_cfr,node_distillation,node_scaling toneMint
class node_vision,node_graphs,node_recurrent,node_activations,node_normalization,node_task toneRose
class node_optimizers,node_helpers,node_engineer,node_results toneIndigo
```

## Components

### Language Models

- [Transformer Variants](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/transformers): `labml_nn/transformers`
- [GPT-NeoX](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/neox): `labml_nn/neox`
- [LoRA](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/lora): `labml_nn/lora`
- [Text Sampling](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/sampling): `labml_nn/sampling`

### Generative Models

- [Diffusion Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/diffusion): `labml_nn/diffusion`
- [GAN Variants](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/gan): `labml_nn/gan`
- [Sketch RNN](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/sketch_rnn/__init__.py): `labml_nn/sketch_rnn/__init__.py`

### Learning Algorithms

- [Reinforcement Learning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/rl): `labml_nn/rl`
- [Game Strategy CFR](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/cfr): `labml_nn/cfr`
- [Model Distillation](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/distillation): `labml_nn/distillation`
- [Scalable Training](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/scaling): `labml_nn/scaling`

### Model Building Blocks

- [Vision Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn): `labml_nn`
- [Graph Networks](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/graphs): `labml_nn/graphs`
- [Recurrent Networks](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn): `labml_nn`
- [Activations](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/activations): `labml_nn/activations`
- [Normalization Layers](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/normalization): `labml_nn/normalization`

### Shared Support

- [Optimizers](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/optimizers): `labml_nn/optimizers`
- [Training Helpers](https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/helpers): `labml_nn/helpers`

### Other components

- **Researcher**
- **Task Data or Prompt**
- **Model or Algorithm Results**

## Connections

- Researcher → Task Data or Prompt: defines task
- Task Data or Prompt → Transformer Variants: selects implementation
- Task Data or Prompt → GPT-NeoX: selects implementation
- Task Data or Prompt → LoRA: selects implementation
- Task Data or Prompt → Text Sampling: selects method
- Task Data or Prompt → Diffusion Models: selects implementation
- Task Data or Prompt → GAN Variants: selects implementation
- Task Data or Prompt → Sketch RNN: selects implementation
- Task Data or Prompt → Reinforcement Learning: selects algorithm
- Task Data or Prompt → Game Strategy CFR: selects algorithm
- Task Data or Prompt → Model Distillation: selects method
- Task Data or Prompt → Scalable Training: selects technique
- Task Data or Prompt → Vision Models: selects implementation
- Task Data or Prompt → Graph Networks: selects implementation
- Task Data or Prompt → Recurrent Networks: selects implementation
- Task Data or Prompt → Activations: selects component
- Task Data or Prompt → Normalization Layers: selects component
- Transformer Variants → Model or Algorithm Results: produces predictions
- GPT-NeoX → Model or Algorithm Results: generates or evaluates
- LoRA → Model or Algorithm Results: adapts model
- Text Sampling → Model or Algorithm Results: selects tokens
- Diffusion Models → Model or Algorithm Results: generates images
- GAN Variants → Model or Algorithm Results: generates images
- Sketch RNN → Model or Algorithm Results: generates sketches
- Reinforcement Learning → Model or Algorithm Results: learns policies
- Game Strategy CFR → Model or Algorithm Results: learns strategies
- Model Distillation → Model or Algorithm Results: trains smaller models
- Scalable Training → Model or Algorithm Results: enables scaled runs
- Vision Models → Model or Algorithm Results: produces predictions
- Graph Networks → Model or Algorithm Results: produces predictions
- Recurrent Networks → Model or Algorithm Results: produces predictions
- Activations → Model or Algorithm Results: transforms features
- Normalization Layers → Model or Algorithm Results: normalizes features