codetether_agent/distill/
trainer.rs1use anyhow::Result;
4use std::path::Path;
5
6#[derive(Debug, Clone)]
8pub struct TrainConfig {
9 pub base_model_path: String,
10 pub output_dir: String,
11 pub lora_rank: usize,
12 pub learning_rate: f32,
13 pub epochs: usize,
14 pub batch_size: usize,
15}
16
17impl Default for TrainConfig {
18 fn default() -> Self {
19 Self {
20 base_model_path: String::new(),
21 output_dir: ".codetether/lora".to_string(),
22 lora_rank: 16,
23 learning_rate: 2e-4,
24 epochs: 3,
25 batch_size: 4,
26 }
27 }
28}
29
30#[derive(Debug, Clone)]
32pub struct TrainResult {
33 pub adapter_path: String,
34 pub version: usize,
35 pub train_loss: f32,
36 pub val_loss: f32,
37 pub records_used: usize,
38}
39
40pub fn train_lora(config: &TrainConfig, records_path: &Path) -> Result<TrainResult> {
42 let version = std::fs::read_dir(&config.output_dir)
43 .map(|entries| {
44 entries
45 .filter_map(|e| e.ok())
46 .filter_map(|e| {
47 e.file_name()
48 .to_str()?
49 .strip_prefix('v')?
50 .parse::<usize>()
51 .ok()
52 })
53 .max()
54 .unwrap_or(0)
55 + 1
56 })
57 .unwrap_or(1);
58 let adapter_path = format!("{}/v{}", config.output_dir, version);
59 tracing::info!(
60 version, records_path = %records_path.display(), base_model = %config.base_model_path,
61 "LoRA training initiated (stub)"
62 );
63 Ok(TrainResult {
64 adapter_path,
65 version,
66 train_loss: 0.0,
67 val_loss: 0.0,
68 records_used: 0,
69 })
70}