| """ |
| MINDI 1.5 Vision-Coder — Training Pipeline |
| |
| LoRA fine-tuning pipeline using Hugging Face Transformers + PEFT. |
| Designed to run on AMD MI300X (192GB) cloud GPU for full training, |
| with RTX 4060 (8GB) local overrides for development testing. |
| """ |
|
|
| from __future__ import annotations |
|
|
| from pathlib import Path |
| from typing import Optional |
|
|
| import yaml |
| from transformers import TrainingArguments |
|
|
|
|
| class TrainingPipeline: |
| """Manages the LoRA fine-tuning pipeline for MINDI 1.5.""" |
|
|
| def __init__( |
| self, |
| config_path: Optional[Path] = None, |
| local_mode: bool = True, |
| ) -> None: |
| self.config_path = config_path or Path("./configs/training_config.yaml") |
| self.local_mode = local_mode |
| self.config = self._load_config() |
|
|
| def _load_config(self) -> dict: |
| """Load training configuration from YAML.""" |
| if not self.config_path.exists(): |
| raise FileNotFoundError(f"Training config not found: {self.config_path}") |
|
|
| with open(self.config_path, "r", encoding="utf-8") as f: |
| full_config = yaml.safe_load(f) |
|
|
| config = full_config.get("training", {}) |
|
|
| |
| if self.local_mode and "local_overrides" in config: |
| overrides = config.pop("local_overrides") |
| config.update(overrides) |
| print("[TrainingPipeline] Applied local GPU overrides (RTX 4060 mode)") |
|
|
| return config |
|
|
| def build_training_args(self, output_dir: Optional[Path] = None) -> TrainingArguments: |
| """Build HuggingFace TrainingArguments from config.""" |
| output = output_dir or Path("./checkpoints/finetuned") |
| output.mkdir(parents=True, exist_ok=True) |
|
|
| return TrainingArguments( |
| output_dir=str(output), |
| num_train_epochs=self.config.get("epochs", 3), |
| per_device_train_batch_size=self.config.get("batch_size", 1), |
| gradient_accumulation_steps=self.config.get("gradient_accumulation_steps", 16), |
| learning_rate=self.config.get("learning_rate", 2e-4), |
| weight_decay=self.config.get("weight_decay", 0.01), |
| warmup_ratio=self.config.get("warmup_ratio", 0.03), |
| lr_scheduler_type=self.config.get("lr_scheduler", "cosine"), |
| max_grad_norm=self.config.get("max_grad_norm", 1.0), |
| bf16=self.config.get("precision", "bf16") == "bf16", |
| logging_steps=self.config.get("logging_steps", 10), |
| save_strategy=self.config.get("save_strategy", "steps"), |
| save_steps=self.config.get("save_steps", 500), |
| save_total_limit=self.config.get("save_total_limit", 5), |
| eval_strategy=self.config.get("eval_strategy", "steps"), |
| eval_steps=self.config.get("eval_steps", 250), |
| report_to=self.config.get("report_to", "wandb"), |
| gradient_checkpointing=self.config.get("gradient_checkpointing", True), |
| optim=self.config.get("optim", "adamw_torch"), |
| dataloader_num_workers=2, |
| remove_unused_columns=False, |
| ) |
|
|
