src/lighteval/main_endpoint.py [66:105]:
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    output_dir: Annotated[
        str, Option(help="Output directory for evaluation results.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = "results",
    results_path_template: Annotated[
        str | None,
        Option(
            help="Template path for where to save the results, you have access to 3 variables, `output_dir`, `org` and `model`. for example a template can be `'{output_dir}/1234/{org}+{model}'`",
            rich_help_panel=HELP_PANEL_NAME_2,
        ),
    ] = None,
    push_to_hub: Annotated[
        bool, Option(help="Push results to the huggingface hub.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    push_to_tensorboard: Annotated[
        bool, Option(help="Push results to tensorboard.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    public_run: Annotated[
        bool, Option(help="Push results and details to a public repo.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    results_org: Annotated[
        Optional[str], Option(help="Organization to push results to.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = None,
    save_details: Annotated[
        bool, Option(help="Save detailed, sample per sample, results.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    wandb: Annotated[
        bool,
        Option(
            help="Push results to wandb. This will only work if you have wandb installed and logged in. We use env variable to configure wandb. see here: https://docs.wandb.ai/guides/track/environment-variables/",
            rich_help_panel=HELP_PANEL_NAME_2,
        ),
    ] = False,
    # === debug ===
    max_samples: Annotated[
        Optional[int], Option(help="Maximum number of samples to evaluate on.", rich_help_panel=HELP_PANEL_NAME_3)
    ] = None,
    job_id: Annotated[
        int, Option(help="Optional job id for future reference.", rich_help_panel=HELP_PANEL_NAME_3)
    ] = 0,
):
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src/lighteval/main_endpoint.py [420:459]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    output_dir: Annotated[
        str, Option(help="Output directory for evaluation results.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = "results",
    results_path_template: Annotated[
        str | None,
        Option(
            help="Template path for where to save the results, you have access to 3 variables, `output_dir`, `org` and `model`. for example a template can be `'{output_dir}/1234/{org}+{model}'`",
            rich_help_panel=HELP_PANEL_NAME_2,
        ),
    ] = None,
    push_to_hub: Annotated[
        bool, Option(help="Push results to the huggingface hub.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    push_to_tensorboard: Annotated[
        bool, Option(help="Push results to tensorboard.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    public_run: Annotated[
        bool, Option(help="Push results and details to a public repo.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    results_org: Annotated[
        Optional[str], Option(help="Organization to push results to.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = None,
    save_details: Annotated[
        bool, Option(help="Save detailed, sample per sample, results.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    wandb: Annotated[
        bool,
        Option(
            help="Push results to wandb. This will only work if you have wandb installed and logged in. We use env variable to configure wandb. see here: https://docs.wandb.ai/guides/track/environment-variables/",
            rich_help_panel=HELP_PANEL_NAME_2,
        ),
    ] = False,
    # === debug ===
    max_samples: Annotated[
        Optional[int], Option(help="Maximum number of samples to evaluate on.", rich_help_panel=HELP_PANEL_NAME_3)
    ] = None,
    job_id: Annotated[
        int, Option(help="Optional job id for future reference.", rich_help_panel=HELP_PANEL_NAME_3)
    ] = 0,
):
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