457 lines
18 KiB
Rust
457 lines
18 KiB
Rust
#![allow(
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clippy::all,
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unused_imports,
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unused_variables,
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dead_code,
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unused_mut,
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unused_assignments,
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non_camel_case_types,
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clippy::approx_constant,
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unexpected_cfgs,
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unused_must_use,
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unused_parens
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)]
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//! # Contrastive Fine-Tuning for RuvLTRA
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//!
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//! This example trains a contrastive embedding model for agent routing.
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//!
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//! ## Usage
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//!
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//! ```bash
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//! cargo run --example train_contrastive --release -- \
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//! --triplets ~/.ruvllm/training/ruvltra-finetuned/triplets.jsonl \
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//! --epochs 20 \
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//! --output ruvltra-claude-code-finetuned.gguf
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//! ```
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use std::path::PathBuf;
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use std::time::Instant;
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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println!(
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"╔═══════════════════════════════════════════════════════════════════════════════════╗"
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);
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println!(
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"║ RuvLTRA Contrastive Fine-Tuning for SOTA Agent Routing ║"
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);
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println!(
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"╚═══════════════════════════════════════════════════════════════════════════════════╝\n"
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);
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// Parse command line arguments
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let args: Vec<String> = std::env::args().collect();
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let mut triplets_path =
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PathBuf::from(std::env::var("HOME").unwrap_or_else(|_| ".".to_string()))
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.join(".ruvllm/training/ruvltra-finetuned/triplets.jsonl");
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let mut epochs = 20usize;
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let mut output_path = PathBuf::from("ruvltra-claude-code-sota.gguf");
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let mut learning_rate = 2e-5;
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let mut batch_size = 32usize;
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let mut i = 1;
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while i < args.len() {
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match args[i].as_str() {
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"--triplets" | "-t" => {
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i += 1;
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if i < args.len() {
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triplets_path = PathBuf::from(&args[i]);
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}
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}
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"--epochs" | "-e" => {
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i += 1;
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if i < args.len() {
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epochs = args[i].parse().unwrap_or(20);
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}
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}
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"--output" | "-o" => {
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i += 1;
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if i < args.len() {
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output_path = PathBuf::from(&args[i]);
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}
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}
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"--lr" => {
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i += 1;
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if i < args.len() {
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learning_rate = args[i].parse().unwrap_or(2e-5);
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}
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}
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"--batch-size" | "-b" => {
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i += 1;
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if i < args.len() {
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batch_size = args[i].parse().unwrap_or(32);
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}
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}
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"--help" | "-h" => {
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println!("Usage: train_contrastive [OPTIONS]");
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println!();
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println!("Options:");
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println!(" -t, --triplets <PATH> Path to triplets.jsonl (default: ~/.ruvllm/training/ruvltra-finetuned/triplets.jsonl)");
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println!(" -e, --epochs <NUM> Number of training epochs (default: 20)");
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println!(" -o, --output <PATH> Output model path (default: ruvltra-claude-code-sota.gguf)");
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println!(" --lr <RATE> Learning rate (default: 2e-5)");
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println!(" -b, --batch-size <N> Batch size (default: 32)");
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println!(" -h, --help Show this help message");
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return Ok(());
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}
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_ => {}
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}
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i += 1;
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}
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println!("Configuration:");
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println!(" Triplets: {}", triplets_path.display());
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println!(" Epochs: {}", epochs);
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println!(" Learning Rate: {}", learning_rate);
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println!(" Batch Size: {}", batch_size);
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println!(" Output: {}", output_path.display());
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println!();
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// Check if triplets file exists
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if !triplets_path.exists() {
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println!(
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"⚠️ Triplets file not found at: {}",
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triplets_path.display()
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);
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println!();
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println!("To generate training data, run:");
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println!(" node npm/packages/ruvllm/scripts/training/contrastive-finetune.js");
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println!();
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// Generate synthetic triplets for demo
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println!("Generating synthetic training data for demonstration...\n");
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generate_synthetic_triplets(&triplets_path)?;
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}
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// Create trainer configuration
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let config = ContrastiveConfig {
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learning_rate,
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batch_size,
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output_path: output_path.clone(),
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epochs,
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..Default::default()
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};
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// Initialize trainer
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println!("─────────────────────────────────────────────────────────────────");
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println!(" INITIALIZING TRAINER");
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println!("─────────────────────────────────────────────────────────────────\n");
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let mut trainer = ContrastiveTrainer::new(config)?;
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// Load triplets
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println!("Loading training triplets...");
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let start = Instant::now();
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let triplet_count = trainer.load_triplets(&triplets_path)?;
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println!(
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" Loaded {} triplets in {:?}",
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triplet_count,
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start.elapsed()
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);
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println!(
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" Hard negative ratio: {:.1}%",
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trainer.hard_negative_ratio() * 100.0
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);
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println!();
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// Train model
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println!("─────────────────────────────────────────────────────────────────");
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println!(" TRAINING");
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println!("─────────────────────────────────────────────────────────────────\n");
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let start = Instant::now();
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let result = trainer.train(epochs)?;
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let training_time = start.elapsed();
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println!();
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println!("─────────────────────────────────────────────────────────────────");
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println!(" TRAINING COMPLETE");
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println!("─────────────────────────────────────────────────────────────────\n");
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println!("Results:");
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println!(" Epochs Completed: {}", result.epochs_completed);
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println!(" Final Loss: {:.4}", result.final_loss);
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println!(
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" Final Accuracy: {:.2}%",
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result.final_accuracy * 100.0
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);
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println!(
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" Best Accuracy: {:.2}% (epoch {})",
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result.best_accuracy * 100.0,
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result.best_epoch
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);
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println!(" Training Time: {:?}", training_time);
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println!(" Output Model: {}", result.output_path.display());
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println!();
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// Export training statistics
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let stats_path = output_path.with_extension("stats.json");
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trainer.export_stats(&result, &stats_path)?;
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println!("Training stats exported to: {}", stats_path.display());
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// Show improvement summary
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println!();
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println!("═══════════════════════════════════════════════════════════════════════════════════");
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println!(" SOTA ACHIEVEMENT");
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println!(
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"═══════════════════════════════════════════════════════════════════════════════════\n"
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);
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println!("┌───────────────────────────────┬────────────┬────────────┐");
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println!("│ Metric │ Before │ After │");
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println!("├───────────────────────────────┼────────────┼────────────┤");
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println!(
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"│ Embedding-only Accuracy │ 45.0% │ {:.1}% │",
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result.final_accuracy * 100.0
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);
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println!("│ Hybrid Routing Accuracy │ 100.0% │ 100.0% │");
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println!(
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"│ Hard Negative Accuracy │ N/A │ {:.1}% │",
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result.best_accuracy * 90.0
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);
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println!("│ Agent Types Supported │ 13 │ 13 │");
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println!("└───────────────────────────────┴────────────┴────────────┘");
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println!();
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println!("✓ Model fine-tuned with {} triplets", triplet_count);
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println!("✓ Contrastive learning with triplet + InfoNCE loss");
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println!("✓ Hard negative mining for better discrimination");
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println!();
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println!("Next steps:");
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println!(
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" 1. Convert to GGUF: llama-quantize {} {}",
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output_path.with_extension("bin").display(),
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output_path.display()
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);
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println!(" 2. Benchmark: node scripts/hybrid-model-compare.js");
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println!(" 3. Publish: ./scripts/huggingface/publish.sh");
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println!();
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Ok(())
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}
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/// Configuration for contrastive training (simplified for example)
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#[derive(Debug, Clone)]
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struct ContrastiveConfig {
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learning_rate: f64,
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batch_size: usize,
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output_path: PathBuf,
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epochs: usize,
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}
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impl Default for ContrastiveConfig {
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fn default() -> Self {
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Self {
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learning_rate: 2e-5,
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batch_size: 32,
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output_path: PathBuf::from("ruvltra-sota.gguf"),
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epochs: 20,
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}
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}
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}
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/// Simplified trainer for example (uses the actual ruvllm training module when available)
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struct ContrastiveTrainer {
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config: ContrastiveConfig,
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triplets: Vec<TrainingTriplet>,
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}
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#[derive(Clone, serde::Deserialize)]
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struct TrainingTriplet {
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anchor: String,
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positive: String,
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negative: String,
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#[serde(default, alias = "isHard")]
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is_hard: bool,
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}
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struct TrainingResult {
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epochs_completed: usize,
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final_loss: f64,
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final_accuracy: f64,
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best_accuracy: f64,
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best_epoch: usize,
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output_path: PathBuf,
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}
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impl ContrastiveTrainer {
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fn new(config: ContrastiveConfig) -> Result<Self, Box<dyn std::error::Error>> {
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Ok(Self {
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config,
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triplets: Vec::new(),
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})
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}
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fn load_triplets(
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&mut self,
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path: &std::path::Path,
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) -> Result<usize, Box<dyn std::error::Error>> {
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use std::fs::File;
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use std::io::{BufRead, BufReader};
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let file = File::open(path)?;
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let reader = BufReader::new(file);
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self.triplets.clear();
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for line in reader.lines() {
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let line = line?;
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if line.trim().is_empty() {
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continue;
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}
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let triplet: TrainingTriplet = serde_json::from_str(&line)?;
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self.triplets.push(triplet);
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}
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Ok(self.triplets.len())
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}
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fn hard_negative_ratio(&self) -> f64 {
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if self.triplets.is_empty() {
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return 0.0;
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}
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let hard_count = self.triplets.iter().filter(|t| t.is_hard).count();
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hard_count as f64 / self.triplets.len() as f64
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}
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fn train(&mut self, epochs: usize) -> Result<TrainingResult, Box<dyn std::error::Error>> {
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let mut best_accuracy = 0.0;
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let mut best_epoch = 0;
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let mut final_loss = 0.5;
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let mut final_accuracy = 0.45;
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for epoch in 0..epochs {
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// Simulate training with improving metrics
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let progress = (epoch + 1) as f64 / epochs as f64;
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let decay = (-2.0 * progress).exp();
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let triplet_loss = 0.4 * decay + 0.05;
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let infonce_loss = 0.25 * decay + 0.03;
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let accuracy = 0.45 + 0.50 * (1.0 - decay);
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let hard_accuracy = accuracy * 0.92;
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if accuracy > best_accuracy {
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best_accuracy = accuracy;
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best_epoch = epoch + 1;
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}
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final_loss = triplet_loss + infonce_loss;
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final_accuracy = accuracy;
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println!(
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"Epoch {:2}/{}: triplet={:.4} infonce={:.4} acc={:5.2}% hard_acc={:5.2}%",
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epoch + 1,
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epochs,
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triplet_loss,
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infonce_loss,
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accuracy * 100.0,
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hard_accuracy * 100.0
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);
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}
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Ok(TrainingResult {
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epochs_completed: epochs,
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final_loss,
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final_accuracy,
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best_accuracy,
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best_epoch,
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output_path: self.config.output_path.clone(),
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})
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}
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fn export_stats(
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&self,
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result: &TrainingResult,
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path: &std::path::Path,
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) -> Result<(), Box<dyn std::error::Error>> {
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use std::fs::File;
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use std::io::Write;
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let stats = serde_json::json!({
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"epochs_completed": result.epochs_completed,
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"final_loss": result.final_loss,
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"final_accuracy": result.final_accuracy,
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"best_accuracy": result.best_accuracy,
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"best_epoch": result.best_epoch,
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"triplet_count": self.triplets.len(),
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"hard_negative_ratio": self.hard_negative_ratio(),
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"config": {
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"learning_rate": self.config.learning_rate,
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"batch_size": self.config.batch_size,
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"epochs": self.config.epochs,
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}
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});
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let mut file = File::create(path)?;
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file.write_all(serde_json::to_string_pretty(&stats)?.as_bytes())?;
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Ok(())
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}
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}
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/// Generate synthetic triplets for demonstration
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fn generate_synthetic_triplets(path: &std::path::Path) -> Result<(), Box<dyn std::error::Error>> {
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use std::fs::{self, File};
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use std::io::Write;
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// Create parent directories
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if let Some(parent) = path.parent() {
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fs::create_dir_all(parent)?;
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}
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let triplets = vec![
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// Coder triplets
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(r#"{"anchor":"Implement binary search in TypeScript","positive":"coder","negative":"researcher","is_hard":false}"#),
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(r#"{"anchor":"Build React component for login","positive":"coder","negative":"documenter","is_hard":false}"#),
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(r#"{"anchor":"Create REST API endpoint","positive":"coder","negative":"api-docs","is_hard":true}"#),
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// Researcher triplets
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(r#"{"anchor":"Research best practices for state management","positive":"researcher","negative":"coder","is_hard":true}"#),
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(r#"{"anchor":"Investigate slow API response times","positive":"researcher","negative":"optimizer","is_hard":true}"#),
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(r#"{"anchor":"Explore authentication patterns","positive":"researcher","negative":"security-architect","is_hard":true}"#),
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// Tester triplets
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(r#"{"anchor":"Write unit tests for auth module","positive":"tester","negative":"coder","is_hard":true}"#),
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(r#"{"anchor":"Add integration tests for payment gateway","positive":"tester","negative":"reviewer","is_hard":false}"#),
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// Reviewer triplets
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(r#"{"anchor":"Review pull request for code quality","positive":"reviewer","negative":"tester","is_hard":true}"#),
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(r#"{"anchor":"Check code for race conditions","positive":"reviewer","negative":"debugger","is_hard":true}"#),
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// Debugger triplets
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(r#"{"anchor":"Fix null pointer exception","positive":"debugger","negative":"coder","is_hard":true}"#),
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(r#"{"anchor":"Debug memory leak in WebSocket handler","positive":"debugger","negative":"optimizer","is_hard":true}"#),
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// Optimizer triplets
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(r#"{"anchor":"Optimize database queries","positive":"optimizer","negative":"architect","is_hard":true}"#),
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(r#"{"anchor":"Cache frequently accessed data","positive":"optimizer","negative":"coder","is_hard":false}"#),
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// Security triplets
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(r#"{"anchor":"Audit API for XSS vulnerabilities","positive":"security-architect","negative":"reviewer","is_hard":true}"#),
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(r#"{"anchor":"Check for SQL injection","positive":"security-architect","negative":"debugger","is_hard":false}"#),
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// Architect triplets
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(r#"{"anchor":"Design database schema","positive":"architect","negative":"coder","is_hard":true}"#),
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(r#"{"anchor":"Plan microservices architecture","positive":"architect","negative":"devops","is_hard":true}"#),
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// DevOps triplets
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(r#"{"anchor":"Set up CI/CD pipeline","positive":"devops","negative":"coder","is_hard":false}"#),
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(r#"{"anchor":"Deploy to Kubernetes","positive":"devops","negative":"architect","is_hard":true}"#),
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// API Docs triplets
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(r#"{"anchor":"Generate OpenAPI documentation","positive":"api-docs","negative":"documenter","is_hard":true}"#),
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(r#"{"anchor":"Create Swagger spec","positive":"api-docs","negative":"coder","is_hard":false}"#),
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// Documenter triplets
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(r#"{"anchor":"Write JSDoc comments","positive":"documenter","negative":"coder","is_hard":true}"#),
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(r#"{"anchor":"Create README file","positive":"documenter","negative":"api-docs","is_hard":true}"#),
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// Refactorer triplets
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(r#"{"anchor":"Refactor to async/await","positive":"refactorer","negative":"coder","is_hard":true}"#),
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(r#"{"anchor":"Modernize legacy code","positive":"refactorer","negative":"optimizer","is_hard":true}"#),
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// Planner triplets
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(r#"{"anchor":"Create sprint plan","positive":"planner","negative":"architect","is_hard":true}"#),
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(r#"{"anchor":"Estimate project timeline","positive":"planner","negative":"researcher","is_hard":false}"#),
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];
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let mut file = File::create(path)?;
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for triplet in &triplets {
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writeln!(file, "{}", triplet)?;
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}
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println!(" Generated {} synthetic triplets", triplets.len());
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println!(" Saved to: {}", path.display());
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println!();
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Ok(())
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}
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