wifi-densepose/vendor/ruvector/docs/training/QUICKSTART.md

Quick Start: Claude Task Dataset Generation

Generate fine-tuning datasets for RuvLTRA models in 5 minutes.

Installation

Add to your Cargo.toml:

[dependencies]
ruvllm = { version = "0.1.0", features = ["training"] }

Basic Usage

1. Generate a Dataset

use ruvllm::training::{DatasetGenerator, DatasetConfig};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create generator with default config
    let config = DatasetConfig::default();
    let mut generator = DatasetGenerator::new(config);

    // Generate dataset
    let dataset = generator.generate();

    println!("Generated {} examples", dataset.examples.len());

    Ok(())
}

2. Export to JSONL

// Export full dataset
dataset.export_jsonl("training.jsonl")?;

// Export statistics
dataset.export_stats("stats.json")?;

3. Create Train/Val/Test Splits

// 70% train, 15% validation, 15% test
let (train, val, test) = dataset.split(0.7, 0.15, 0.15, 42);

// Export each split
ClaudeTaskDataset::new(train).export_jsonl("train.jsonl")?;
ClaudeTaskDataset::new(val).export_jsonl("val.jsonl")?;
ClaudeTaskDataset::new(test).export_jsonl("test.jsonl")?;

Run the Example

# Generate a complete dataset
cargo run --example generate_claude_dataset --release

# Output:
# - claude_training_full.jsonl (~2,700 examples)
# - claude_training_train.jsonl (70% split)
# - claude_training_val.jsonl (15% split)
# - claude_training_test.jsonl (15% split)
# - claude_training_stats.json (statistics)

Custom Configuration

Control Dataset Size

let config = DatasetConfig {
    examples_per_category: 200,  // 200 examples per category
    ..Default::default()
};

Disable Augmentation

let config = DatasetConfig {
    examples_per_category: 100,
    enable_augmentation: false,  // No augmentation
    ..Default::default()
};

Fine-Tune Augmentation

use ruvllm::training::AugmentationConfig;

let config = DatasetConfig {
    examples_per_category: 100,
    enable_augmentation: true,
    augmentation: AugmentationConfig {
        paraphrases_per_example: 3,      // 3 paraphrases
        complexity_variations: 2,         // 2 complexity levels
        enable_domain_transfer: true,    // Cross-domain transfer
    },
    seed: 42,  // For reproducibility
};

Understanding the Data

Dataset Structure

Each example contains:

{
  "input": "Implement JWT authentication middleware in TypeScript",
  "context": "Should verify Bearer tokens, check expiration, validate RS256 signature",
  "output_agent": "coder",
  "metadata": {
    "category": "Coder",
    "complexity": "Moderate",
    "domain": "Web",
    "expected_model": "sonnet",
    "quality_score": 0.87,
    "tags": ["authentication", "middleware", "jwt"]
  }
}

Task Categories

1. Coder (20%) - Code generation, debugging, refactoring
2. Researcher (20%) - Analysis, exploration, documentation
3. Security (20%) - Audits, vulnerabilities, compliance
4. Architecture (20%) - System design, planning
5. Reviewer (20%) - Code review, quality assessment

Model Selection

The dataset includes intelligent routing:

• Haiku: Simple tasks (cheap, fast)
• Sonnet: Moderate complexity (balanced)
• Opus: Complex/security tasks (highest quality)

Dataset Statistics

Default configuration generates:

Base examples:      500 (5 categories × 100)
Paraphrased:      1,000 (500 × 2)
Complexity varied:  800 (500 × 2, filtered)
Domain transfer:    400 (500 × 1, filtered)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Total:           ~2,700 examples

Category distribution:

Coder:        ~540 examples (20%)
Researcher:   ~540 examples (20%)
Security:     ~540 examples (20%)
Architecture: ~540 examples (20%)
Reviewer:     ~540 examples (20%)

Model distribution:

Haiku:   ~730 examples (27%) - Cost-effective
Sonnet: ~1,270 examples (47%) - Balanced
Opus:    ~700 examples (26%) - High-quality

Inspect the Data

// Print first 5 examples
for (i, example) in dataset.examples.iter().take(5).enumerate() {
    println!("Example {}:", i + 1);
    println!("  Input: {}", example.input);
    println!("  Agent: {}", example.output_agent);
    println!("  Model: {}", example.metadata.expected_model);
    println!("  Quality: {:.2}\n", example.metadata.quality_score);
}

Filter by Category

// Get all security tasks
let security_tasks: Vec<_> = dataset.examples
    .iter()
    .filter(|e| e.metadata.category == TaskCategory::Security)
    .collect();

println!("Security tasks: {}", security_tasks.len());

Filter by Complexity

// Get all simple tasks
let simple_tasks: Vec<_> = dataset.examples
    .iter()
    .filter(|e| e.metadata.complexity == ComplexityLevel::Simple)
    .collect();

println!("Simple tasks: {}", simple_tasks.len());

Next Steps

1. Fine-tune a model: Use the generated JSONL files with your favorite ML framework
2. Customize templates: Modify claude_dataset.rs to add domain-specific tasks
3. Integrate with SONA: Use RuvLLM's SONA learning for continuous improvement
4. Deploy: Use RuvLLM's serving engine for production inference

Common Issues

"Not enough examples"

Increase examples_per_category:

let config = DatasetConfig {
    examples_per_category: 500,  // Generate more
    ..Default::default()
};

"Too much variation"

Disable augmentation:

let config = DatasetConfig {
    enable_augmentation: false,
    ..Default::default()
};

"Need specific domain"

Filter after generation:

let web_tasks: Vec<_> = dataset.examples
    .iter()
    .filter(|e| e.metadata.domain == DomainType::Web)
    .cloned()
    .collect();

ClaudeTaskDataset::new(web_tasks).export_jsonl("web_tasks.jsonl")?;

Resources

• Full Documentation: ../crates/ruvllm/src/training/README.md
• Format Spec: ../docs/claude_dataset_format.md
• Example Code: ../crates/ruvllm/examples/generate_claude_dataset.rs
• Tests: ../crates/ruvllm/src/training/tests.rs

Support

• GitHub Issues: https://github.com/ruvector/issues
• Documentation: https://docs.ruvector.io