import sys import time from pathlib import Path from typing import Optional import typer from cli.config import Config, load_config from cli.options import add_options_from_config app = typer.Typer( help="Command-line interface for Unsloth training, chat, and export.", context_settings={"help_option_names": ["-h", "--help"]}, ) @app.command() @add_options_from_config(Config) def train( config: Optional[Path] = typer.Option( None, "--config", "-c", help="Path to YAML/JSON config file. CLI flags override config values.", ), hf_token: Optional[str] = typer.Option( None, "--hf-token", envvar="HF_TOKEN", help="Hugging Face token if needed." ), dry_run: bool = typer.Option( False, "--dry-run", help="Show resolved config and exit without training.", ), config_overrides: dict = None, # Injected by add_options_from_config decorator ): """Launch training using the existing Unsloth training backend.""" try: cfg = load_config(config) except FileNotFoundError as e: typer.echo(f"Error: {e}", err=True) raise typer.Exit(code=2) # Apply CLI overrides cfg.apply_overrides(**config_overrides) # Dry run: show resolved config and exit if dry_run: import yaml data = cfg.model_dump() data["training"]["output_dir"] = str(data["training"]["output_dir"]) typer.echo(yaml.dump(data, default_flow_style=False, sort_keys=False)) raise typer.Exit(code=0) # Validate required fields if not cfg.model: typer.echo("Error: provide --model or set model in --config", err=True) raise typer.Exit(code=2) if not cfg.data.dataset and not cfg.data.local_dataset: typer.echo( "Error: provide --dataset or --local-dataset (or via --config)", err=True ) raise typer.Exit(code=2) # Lazy imports to avoid triggering Unsloth patches on --help from backend.trainer import UnslothTrainer from backend.model_config import ModelConfig trainer = UnslothTrainer() model_config = ModelConfig.from_ui_selection( dropdown_value=cfg.model, search_value=None, hf_token=hf_token, is_lora=False ) if not model_config: typer.echo("Could not resolve model config", err=True) raise typer.Exit(code=1) is_vision = model_config.is_vision use_lora = cfg.training.training_type.lower() == "lora" if not trainer.load_model( model_name=model_config.identifier, max_seq_length=cfg.training.max_seq_length, load_in_4bit=cfg.training.load_in_4bit if use_lora else False, hf_token=hf_token, ): typer.echo("Model load failed", err=True) raise typer.Exit(code=1) if not trainer.prepare_model_for_training(**cfg.model_kwargs(use_lora, is_vision)): typer.echo("Model preparation failed", err=True) raise typer.Exit(code=1) ds = trainer.load_and_format_dataset( dataset_source=cfg.data.dataset or "", format_type=cfg.data.format_type, local_datasets=cfg.data.local_dataset, ) if ds is None: typer.echo("Dataset load failed", err=True) raise typer.Exit(code=1) started = trainer.start_training(dataset=ds, **cfg.training_kwargs()) if not started: typer.echo("Training failed to start", err=True) raise typer.Exit(code=1) try: while trainer.training_thread and trainer.training_thread.is_alive(): time.sleep(1) except KeyboardInterrupt: typer.echo("Stopping training (Ctrl+C detected)...") trainer.stop_training() finally: if trainer.training_thread: trainer.training_thread.join() final = trainer.get_training_progress() if getattr(final, "error", None): typer.echo(f"Training error: {final.error}", err=True) raise typer.Exit(code=1) @app.command() def inference( model: str = typer.Argument(..., help="HF model id or local path."), prompt: str = typer.Argument(..., help="Prompt to send to the model."), hf_token: Optional[str] = typer.Option( None, "--hf-token", envvar="HF_TOKEN", help="Hugging Face token if needed." ), temperature: float = typer.Option(0.7, "--temperature"), top_p: float = typer.Option(0.9, "--top-p"), top_k: int = typer.Option(40, "--top-k"), max_new_tokens: int = typer.Option(256, "--max-new-tokens"), repetition_penalty: float = typer.Option(1.1, "--repetition-penalty"), system_prompt: str = typer.Option( "", "--system-prompt", help="Optional system prompt to prepend.", ), max_seq_length: int = typer.Option(2048, "--max-seq-length"), load_in_4bit: bool = typer.Option(True, "--load-in-4bit/--no-load-in-4bit"), ): """Run a single inference using the specified model.""" # Lazy imports to avoid triggering Unsloth patches on --help from backend.model_config import ModelConfig from backend.inference import get_inference_backend inference_backend = get_inference_backend() model_config = ModelConfig.from_ui_selection( dropdown_value=model, search_value=None, hf_token=hf_token, is_lora=False ) if not model_config: typer.echo("Could not resolve model config", err=True) raise typer.Exit(code=1) if not inference_backend.load_model( config=model_config, max_seq_length=max_seq_length, load_in_4bit=load_in_4bit, hf_token=hf_token, ): typer.echo("Model load failed", err=True) raise typer.Exit(code=1) messages = [{"role": "user", "content": prompt}] stream = inference_backend.generate_chat_response( messages=messages, system_prompt=system_prompt, temperature=temperature, top_p=top_p, top_k=top_k, max_new_tokens=max_new_tokens, repetition_penalty=repetition_penalty, ) typer.echo("Assistant:", nl=True) previous = "" for chunk in stream: # Backend yields cumulative text; print only the delta delta = chunk[len(previous):] if delta: sys.stdout.write(delta) sys.stdout.flush() previous = chunk sys.stdout.write("\n") sys.stdout.flush() @app.command("list-checkpoints") def list_checkpoints( outputs_dir: Path = typer.Option( Path("./outputs"), "--outputs-dir", help="Directory that holds training runs." ), ): """ List checkpoints detected in the outputs directory. """ from backend.export import ExportBackend backend = ExportBackend() checkpoints = backend.scan_checkpoints(outputs_dir=str(outputs_dir)) if not checkpoints: typer.echo("No checkpoints found.") raise typer.Exit() for display, path in checkpoints: typer.echo(f"{display}: {path}") if __name__ == "__main__": app()