Overview - Entrenar - Training & Optimization Library
Description: A comprehensive guide to building neural network training systems with autograd, optimizers, LoRA/QLoRA, and quantization using EXTREME TDD methodology

# Generative Models

Entrenar provides Generative Adversarial Networks (GANs) for code synthesis.

## Architecture

    Latent Vector z ─┬─► Generator ─► AST Tokens ─┬─► Discriminator ─► Valid/Invalid
                     │                            │
                     │   Real AST Samples ────────┘
                     │
                     └── (sampled from N(0, I))

## Key Components

### Generator

- Maps latent vectors to Rust AST token sequences
- MLP architecture with configurable hidden layers
- Xavier/He initialization for stable training

### Discriminator

- Classifies code as real (valid) or fake (invalid)
- Token embedding + MLP architecture
- Binary cross-entropy loss

### Latent Space

- Standard normal distribution sampling
- SLERP interpolation for smooth transitions
- Supports latent space exploration

## Quick Start

    #![allow(unused)]
    fn main() {
    use entrenar::generative::{CodeGan, CodeGanConfig};

    let config = CodeGanConfig::default();
    let mut gan = CodeGan::new(config);

    // Generate code samples
    let latent = gan.sample_latent(10);
    for z in &latent {
        let code = gan.generator.generate(z);
        println!("{:?}", code);
    }
    }

## Use Cases

1. Code Completion: Generate likely next tokens
2. Data Augmentation: Create synthetic training data
3. Novelty Search: Explore code generation space
4. Transfer Learning: Pre-train on code distributions