Function cmd_convert

python/src/cli.py:1736–1801

Convert a HuggingFace model to a custom output directory.

(args)

def cmd_convert(args):
    """Convert a HuggingFace model to a custom output directory."""
    import tempfile

    model_id = args.model_name
    output_dir = args.output_dir
    lora_path = getattr(args, 'lora', None)

    if output_dir is None:
        output_dir = get_weights_dir(model_id)
    else:
        output_dir = Path(output_dir)

    cache_dir = getattr(args, 'cache_dir', None)
    token = getattr(args, 'token', None)

    temp_merged_dir = None

    if lora_path:
        merged_model, tokenizer = merge_lora_adapter(model_id, lora_path, cache_dir, token)
        if merged_model is None:
            return 1

        temp_merged_dir = tempfile.mkdtemp(prefix="cactus_lora_merged_")
        print_color(YELLOW, f"Saving merged model to temp directory: {temp_merged_dir}")
        merged_model.save_pretrained(temp_merged_dir)
        tokenizer.save_pretrained(temp_merged_dir)

        lora_tok_config = Path(lora_path) / "tokenizer_config.json"
        if lora_tok_config.exists():
            shutil.copy2(lora_tok_config, Path(temp_merged_dir) / "tokenizer_config.json")

        del merged_model
        import torch
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

        model_id = temp_merged_dir

    class DownloadArgs:
        pass

    download_args = DownloadArgs()
    download_args.model_id = model_id
    download_args.original_model_id = args.model_name
    download_args.precision = args.precision
    download_args.cache_dir = cache_dir
    download_args.token = token
    download_args.reconvert = True

    original_get_weights = get_weights_dir

    def custom_weights_dir(mid):
        return output_dir

    import src.cli as cli_module
    cli_module.get_weights_dir = custom_weights_dir