""" SmolLM3 Training Configuration for OpenHermes-FR Dataset - Multiple Passes Optimized for A100 GPUs with multiple passes (3-5 epochs) on 800k+ datapoints """ import os from dataclasses import dataclass from typing import Optional from config.train_smollm3 import SmolLM3Config @dataclass class SmolLM3ConfigOpenHermesFRMultiplePasses(SmolLM3Config): """Configuration for SmolLM3 fine-tuning with multiple passes on OpenHermes-FR dataset""" # Model configuration - optimized for A100 model_name: str = "HuggingFaceTB/SmolLM3-3B" max_seq_length: int = 8192 # Increased for better context understanding use_flash_attention: bool = True use_gradient_checkpointing: bool = False # Disabled for A100 efficiency # Training configuration - Multiple passes optimized batch_size: int = 6 # Slightly smaller for stability during long training gradient_accumulation_steps: int = 20 # Effective batch size = 6 * 20 = 120 learning_rate: float = 3e-6 # Conservative LR for multiple passes weight_decay: float = 0.01 warmup_steps: int = 2000 # Longer warmup for multiple passes max_iters: int = 25000 # 4 passes on 800k dataset (25k steps) eval_interval: int = 1000 # Less frequent evaluation log_interval: int = 50 # Less frequent logging save_interval: int = 2000 # Less frequent saving # Optimizer configuration - stability focused optimizer: str = "adamw_torch" beta1: float = 0.9 beta2: float = 0.999 # Higher beta2 for stability eps: float = 1e-8 # Scheduler configuration - longer training with multiple passes scheduler: str = "cosine" min_lr: float = 3e-7 # Lower min LR # Mixed precision - A100 optimized fp16: bool = False # Use bf16 for A100 bf16: bool = True # Better for A100 # DDP configuration ddp_backend: str = "nccl" ddp_find_unused_parameters: bool = False # Logging and saving - optimized for long training save_steps: int = 2000 eval_steps: int = 1000 logging_steps: int = 50 save_total_limit: Optional[int] = 8 # Keep more checkpoints for long training # Evaluation eval_strategy: str = "steps" metric_for_best_model: str = "eval_loss" greater_is_better: bool = False load_best_model_at_end: bool = True # OpenHermes-FR Dataset configuration dataset_name: str = "legmlai/openhermes-fr" dataset_split: str = "train" input_field: str = "prompt" target_field: str = "accepted_completion" filter_bad_entries: bool = True bad_entry_field: str = "bad_entry" # Data configuration (not used for HF datasets but kept for compatibility) data_dir: str = None train_file: str = None validation_file: Optional[str] = None test_file: Optional[str] = None # Chat template configuration use_chat_template: bool = True chat_template_kwargs: dict = None # Trackio monitoring configuration enable_tracking: bool = True trackio_url: Optional[str] = None trackio_token: Optional[str] = None log_artifacts: bool = True log_metrics: bool = True log_config: bool = True experiment_name: Optional[str] = None # HF Datasets configuration hf_token: Optional[str] = None dataset_repo: Optional[str] = None # Additional A100 optimizations dataloader_num_workers: int = 8 # More workers for faster data loading dataloader_pin_memory: bool = True dataloader_prefetch_factor: int = 2 # Memory optimizations max_grad_norm: float = 1.0 # Gradient clipping group_by_length: bool = True # Group similar length sequences # Training duration calculations # With 800k datapoints and effective batch size of 120: # Steps per epoch = 800,000 / 120 = 6,667 steps # For 3 passes: 6,667 * 3 = 20,000 steps # For 4 passes: 6,667 * 4 = 26,667 steps # For 5 passes: 6,667 * 5 = 33,333 steps # Current max_iters = 25,000 (about 3.75 passes) def __post_init__(self): if self.chat_template_kwargs is None: self.chat_template_kwargs = { "add_generation_prompt": True, "no_think_system_message": True # Set to True to add /no_think tag } # Validate configuration if self.fp16 and self.bf16: raise ValueError("Cannot use both fp16 and bf16") if self.max_seq_length > 131072: # 128k limit raise ValueError("max_seq_length cannot exceed 131072") # Calculate training statistics effective_batch_size = self.batch_size * self.gradient_accumulation_steps steps_per_epoch = 800000 // effective_batch_size # Approximate for 800k dataset epochs_for_max_iters = self.max_iters / steps_per_epoch print(f"=== Multiple Passes Training Configuration ===") print(f"Effective batch size: {effective_batch_size}") print(f"Steps per epoch: ~{steps_per_epoch}") print(f"Training for ~{epochs_for_max_iters:.1f} epochs") print(f"Total training steps: {self.max_iters}") print(f"Learning rate: {self.learning_rate}") print(f"Mixed precision: {'bf16' if self.bf16 else 'fp16'}") print(f"Max sequence length: {self.max_seq_length}") print(f"Gradient checkpointing: {self.use_gradient_checkpointing}") print(f"Warmup steps: {self.warmup_steps}") print(f"Save interval: {self.save_interval}") print("=" * 50) # Set default experiment name if not provided if self.experiment_name is None: self.experiment_name = "smollm3_openhermes_fr_multiple_passes" def get_config(config_path: str) -> SmolLM3ConfigOpenHermesFRMultiplePasses: """Load configuration from file or return default""" if os.path.exists(config_path): # Load from file if it exists import importlib.util spec = importlib.util.spec_from_file_location("config_module", config_path) config_module = importlib.util.module_from_spec(spec) spec.loader.exec_module(config_module) if hasattr(config_module, 'config'): return config_module.config else: # Try to find a config class for attr_name in dir(config_module): attr = getattr(config_module, attr_name) if isinstance(attr, SmolLM3ConfigOpenHermesFRMultiplePasses): return attr # Return default configuration return SmolLM3ConfigOpenHermesFRMultiplePasses() # Default configuration instance config = SmolLM3ConfigOpenHermesFRMultiplePasses()