Task

This section contains the API reference for the distilabel tasks.

For more information on how the Task works and see some examples, check the Tutorial - Task page.

base

_Task

Bases: _Step, ABC

_Task is an abstract class that implements the _Step interface and adds the format_input and format_output methods to format the inputs and outputs of the task. It also adds a llm attribute to be used as the LLM to generate the outputs.

Attributes:

Name	Type	Description
llm	LLM	the LLM to be used to generate the outputs of the task.
group_generations	bool	whether to group the num_generations generated per input in a list or create a row per generation. Defaults to False.
add_raw_output	RuntimeParameter[bool]	whether to include a field with the raw output of the LLM in the distilabel_metadata field of the output. Can be helpful to not loose data with Tasks that need to format the output of the LLM. Defaults to False.
num_generations	RuntimeParameter[int]	The number of generations to be produced per input.

Source code in src/distilabel/steps/tasks/base.py

class _Task(_Step, ABC):
    """_Task is an abstract class that implements the `_Step` interface and adds the
    `format_input` and `format_output` methods to format the inputs and outputs of the
    task. It also adds a `llm` attribute to be used as the LLM to generate the outputs.

    Attributes:
        llm: the `LLM` to be used to generate the outputs of the task.
        group_generations: whether to group the `num_generations` generated per input in
            a list or create a row per generation. Defaults to `False`.
        add_raw_output: whether to include a field with the raw output of the LLM in the
            `distilabel_metadata` field of the output. Can be helpful to not loose data
            with `Tasks` that need to format the output of the `LLM`. Defaults to `False`.
        num_generations: The number of generations to be produced per input.
    """

    llm: LLM

    group_generations: bool = False
    add_raw_output: RuntimeParameter[bool] = Field(
        default=True,
        description=(
            "Whether to include the raw output of the LLM in the key `raw_output_<TASK_NAME>`"
            " of the `distilabel_metadata` dictionary output column"
        ),
    )
    add_raw_input: RuntimeParameter[bool] = Field(
        default=True,
        description=(
            "Whether to include the raw input of the LLM in the key `raw_input_<TASK_NAME>`"
            " of the `distilabel_metadata` dictionary column"
        ),
    )
    num_generations: RuntimeParameter[int] = Field(
        default=1, description="The number of generations to be produced per input."
    )
    use_default_structured_output: bool = False

    _can_be_used_with_offline_batch_generation: bool = PrivateAttr(False)

    def model_post_init(self, __context: Any) -> None:
        if (
            self.llm.use_offline_batch_generation
            and not self._can_be_used_with_offline_batch_generation
        ):
            raise DistilabelUserError(
                f"`{self.__class__.__name__}` task cannot be used with offline batch generation"
                " feature.",
                page="sections/how_to_guides/advanced/offline-batch-generation",
            )

        super().model_post_init(__context)

    @property
    def is_global(self) -> bool:
        """Extends the `is_global` property to return `True` if the task is using the
        offline batch generation feature, otherwise it returns the value of the parent
        class property. `offline_batch_generation` requires to receive all the inputs
        at once, so for the `_BatchManager` this is a global step.

        Returns:
            Whether the task is a global step or not.
        """
        if self.llm.use_offline_batch_generation:
            return True

        return super().is_global

    def load(self) -> None:
        """Loads the LLM via the `LLM.load()` method."""
        super().load()
        self._set_default_structured_output()
        self.llm.load()

    @override
    def unload(self) -> None:
        """Unloads the LLM."""
        self._logger.debug("Executing task unload logic.")
        self.llm.unload()

    @override
    def impute_step_outputs(
        self, step_output: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """
        Imputes the outputs of the task in case the LLM failed to generate a response.
        """
        result = []
        for row in step_output:
            data = row.copy()
            for output in self.get_outputs().keys():
                data[output] = None
            data = self._create_metadata(
                data,
                None,
                None,
                add_raw_output=self.add_raw_output,
                add_raw_input=self.add_raw_input,
            )
            result.append(data)
        return result

    @abstractmethod
    def format_output(
        self,
        output: Union[str, None],
        input: Union[Dict[str, Any], None] = None,
    ) -> Dict[str, Any]:
        """Abstract method to format the outputs of the task. It needs to receive an output
        as a string, and generates a Python dictionary with the outputs of the task. In
        addition the `input` used to generate the output is also received just in case it's
        needed to be able to parse the output correctly.
        """
        pass

    def _format_outputs(
        self,
        outputs: "GenerateOutput",
        input: Union[Dict[str, Any], None] = None,
    ) -> List[Dict[str, Any]]:
        """Formats the outputs of the task using the `format_output` method. If the output
        is `None` (i.e. the LLM failed to generate a response), then the outputs will be
        set to `None` as well.

        Args:
            outputs: The outputs (`n` generations) for the provided `input`.
            input: The input used to generate the output.

        Returns:
            A list containing a dictionary with the outputs of the task for each input.
        """
        inputs = [None] if input is None else [input]
        formatted_outputs = []
        repeate_inputs = len(outputs.get("generations"))
        outputs = normalize_statistics(outputs)

        for (output, stats), input in zip(
            iterate_generations_with_stats(outputs), inputs * repeate_inputs
        ):  # type: ignore
            try:
                # Extract the generations, and move the statistics to the distilabel_metadata,
                # to keep everything clean
                formatted_output = self.format_output(output, input)
                formatted_output = self._create_metadata(
                    formatted_output,
                    output,
                    input,
                    add_raw_output=self.add_raw_output,  # type: ignore
                    add_raw_input=self.add_raw_input,  # type: ignore
                    statistics=stats,
                )
                formatted_outputs.append(formatted_output)
            except Exception as e:
                self._logger.warning(  # type: ignore
                    f"Task '{self.name}' failed to format output: {e}. Saving raw response."  # type: ignore
                )
                formatted_outputs.append(self._output_on_failure(output, input))
        return formatted_outputs

    def _output_on_failure(
        self, output: Union[str, None], input: Union[Dict[str, Any], None] = None
    ) -> Dict[str, Any]:
        """In case of failure to format the output, this method will return a dictionary including
        a new field `distilabel_meta` with the raw output of the LLM.
        """
        # Create a dictionary with the outputs of the task (every output set to None)
        outputs = {output: None for output in self.outputs}
        outputs["model_name"] = self.llm.model_name  # type: ignore
        outputs = self._create_metadata(
            outputs,
            output,
            input,
            add_raw_output=self.add_raw_output,  # type: ignore
            add_raw_input=self.add_raw_input,  # type: ignore
        )
        return outputs

    def _create_metadata(
        self,
        output: Dict[str, Any],
        raw_output: List[Union[str, None]],
        input: Union[str, None],
        add_raw_output: bool = True,
        add_raw_input: bool = True,
        statistics: Optional["LLMStatistics"] = None,
    ) -> Dict[str, Any]:
        """Adds the raw output and or the formatted input of the LLM to the output dictionary
        if `add_raw_output` is True or `add_raw_input` is True.

        Args:
            output:
                The output dictionary after formatting the output from the LLM,
                to add the raw output and or raw input.
            raw_output: The raw output of the LLM (the list of generations).
            input: The raw input of the LLM.
            add_raw_output: Whether to add the raw output to the output dictionary.
            add_raw_input: Whether to add the raw input to the output dictionary.
            statistics: The statistics generated by the LLM, which should contain at least
                the number of input and output tokens.
        """
        meta = output.get(DISTILABEL_METADATA_KEY, {})

        if add_raw_output:
            meta[f"raw_output_{self.name}"] = raw_output
        if add_raw_input:
            meta[f"raw_input_{self.name}"] = self.format_input(input) if input else None
        if statistics:
            meta[f"statistics_{self.name}"] = statistics
        if meta:
            output[DISTILABEL_METADATA_KEY] = meta

        return output

    def _set_default_structured_output(self) -> None:
        """Prepares the structured output to be set in the selected `LLM`.

        If the method `get_structured_output` returns None (the default), there's no need
        to set anything, as it doesn't apply.
        If the `use_default_structured_output` and there's no previous structured output
        set by hand, then decide the type of structured output to select depending on the
        `LLM` provider.
        """
        schema = self.get_structured_output()
        if not schema:
            return

        if self.use_default_structured_output and not self.llm.structured_output:
            # In case the default structured output is required, we have to set it before
            # the LLM is loaded
            from distilabel.models.llms import InferenceEndpointsLLM
            from distilabel.models.llms.base import AsyncLLM

            def check_dependency(module_name: str) -> None:
                if not importlib.util.find_spec(module_name):
                    raise ImportError(
                        f"`{module_name}` is not installed and is needed for the structured generation with this LLM."
                        f" Please install it using `pip install {module_name}`."
                    )

            dependency = "outlines"
            structured_output = {"schema": schema}
            if isinstance(self.llm, InferenceEndpointsLLM):
                structured_output.update({"format": "json"})
            # To determine instructor or outlines format
            elif isinstance(self.llm, AsyncLLM) and not isinstance(
                self.llm, InferenceEndpointsLLM
            ):
                dependency = "instructor"
                structured_output.update({"format": "json"})

            check_dependency(dependency)
            self.llm.structured_output = structured_output

    def get_structured_output(self) -> Union[Dict[str, Any], None]:
        """Returns the structured output for a task that implements one by default,
        must be overriden by subclasses of `Task`. When implemented, should be a json
        schema that enforces the response from the LLM so that it's easier to parse.
        """
        return None

    def _sample_input(self) -> "ChatType":
        """Returns a sample input to be used in the `print` method.
        Tasks that don't adhere to a format input that returns a map of the type
        str -> str should override this method to return a sample input.
        """
        return self.format_input(
            {input: f"<PLACEHOLDER_{input.upper()}>" for input in self.inputs}
        )

    def print(self, sample_input: Optional["ChatType"] = None) -> None:
        """Prints a sample input to the console using the `rich` library.
        Helper method to visualize the prompt of the task.

        Args:
            sample_input: A sample input to be printed. If not provided, a default will be
                generated using the `_sample_input` method, which can be overriden by
                subclasses. This should correspond to the same example you could pass to
                the `format_input` method.
                The variables be named <PLACEHOLDER_VARIABLE_NAME> by default.

        Examples:
            Print the URIAL prompt:

            ```python
            from distilabel.steps.tasks import URIAL
            from distilabel.models.llms.huggingface import InferenceEndpointsLLM

            # Consider this as a placeholder for your actual LLM.
            urial = URIAL(
                llm=InferenceEndpointsLLM(
                    model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
                ),
            )
            urial.load()
            urial.print()
            ╭─────────────────────────────────────── Prompt: URIAL  ────────────────────────────────────────╮
            │ ╭────────────────────────────────────── User Message ───────────────────────────────────────╮ │
            │ │ # Instruction                                                                             │ │
            │ │                                                                                           │ │
            │ │ Below is a list of conversations between a human and an AI assistant (you).               │ │
            │ │ Users place their queries under "# User:", and your responses are under  "# Assistant:".  │ │
            │ │ You are a helpful, respectful, and honest assistant.                                      │ │
            │ │ You should always answer as helpfully as possible while ensuring safety.                  │ │
            │ │ Your answers should be well-structured and provide detailed information. They should also │ │
            │ │ have an engaging tone.                                                                    │ │
            │ │ Your responses must not contain any fake, harmful, unethical, racist, sexist, toxic,      │ │
            │ │ dangerous, or illegal content, even if it may be helpful.                                 │ │
            │ │ Your response must be socially responsible, and thus you can refuse to answer some        │ │
            │ │ controversial topics.                                                                     │ │
            │ │                                                                                           │ │
            │ │                                                                                           │ │
            │ │ # User:                                                                                   │ │
            │ │                                                                                           │ │
            │ │ <PLACEHOLDER_INSTRUCTION>                                                                 │ │
            │ │                                                                                           │ │
            │ │ # Assistant:                                                                              │ │
            │ ╰───────────────────────────────────────────────────────────────────────────────────────────╯ │
            ╰───────────────────────────────────────────────────────────────────────────────────────────────╯
            ```
        """
        from rich.console import Console, Group
        from rich.panel import Panel
        from rich.text import Text

        console = Console()
        sample_input = sample_input or self._sample_input()

        panels = []
        for item in sample_input:
            content = Text.assemble((item.get("content", ""),))
            panel = Panel(
                content,
                title=f"[bold][magenta]{item.get('role', '').capitalize()} Message[/magenta][/bold]",
                border_style="light_cyan3",
            )
            panels.append(panel)

        # Create a group of panels
        # Wrap the group in an outer panel
        outer_panel = Panel(
            Group(*panels),
            title=f"[bold][magenta]Prompt: {type(self).__name__} [/magenta][/bold]",
            border_style="light_cyan3",
            expand=False,
        )
        console.print(outer_panel)

is_global: bool property

Extends the is_global property to return True if the task is using the offline batch generation feature, otherwise it returns the value of the parent class property. offline_batch_generation requires to receive all the inputs at once, so for the _BatchManager this is a global step.

Returns:

Type	Description
bool	Whether the task is a global step or not.

load()

Loads the LLM via the LLM.load() method.

Source code in src/distilabel/steps/tasks/base.py

def load(self) -> None:
    """Loads the LLM via the `LLM.load()` method."""
    super().load()
    self._set_default_structured_output()
    self.llm.load()

unload()

Unloads the LLM.

Source code in src/distilabel/steps/tasks/base.py

@override
def unload(self) -> None:
    """Unloads the LLM."""
    self._logger.debug("Executing task unload logic.")
    self.llm.unload()

impute_step_outputs(step_output)

Imputes the outputs of the task in case the LLM failed to generate a response.

Source code in src/distilabel/steps/tasks/base.py

@override
def impute_step_outputs(
    self, step_output: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
    """
    Imputes the outputs of the task in case the LLM failed to generate a response.
    """
    result = []
    for row in step_output:
        data = row.copy()
        for output in self.get_outputs().keys():
            data[output] = None
        data = self._create_metadata(
            data,
            None,
            None,
            add_raw_output=self.add_raw_output,
            add_raw_input=self.add_raw_input,
        )
        result.append(data)
    return result

format_output(output, input=None) abstractmethod

Abstract method to format the outputs of the task. It needs to receive an output as a string, and generates a Python dictionary with the outputs of the task. In addition the input used to generate the output is also received just in case it's needed to be able to parse the output correctly.

Source code in src/distilabel/steps/tasks/base.py

@abstractmethod
def format_output(
    self,
    output: Union[str, None],
    input: Union[Dict[str, Any], None] = None,
) -> Dict[str, Any]:
    """Abstract method to format the outputs of the task. It needs to receive an output
    as a string, and generates a Python dictionary with the outputs of the task. In
    addition the `input` used to generate the output is also received just in case it's
    needed to be able to parse the output correctly.
    """
    pass

get_structured_output()

Returns the structured output for a task that implements one by default, must be overriden by subclasses of Task. When implemented, should be a json schema that enforces the response from the LLM so that it's easier to parse.

Source code in src/distilabel/steps/tasks/base.py

def get_structured_output(self) -> Union[Dict[str, Any], None]:
    """Returns the structured output for a task that implements one by default,
    must be overriden by subclasses of `Task`. When implemented, should be a json
    schema that enforces the response from the LLM so that it's easier to parse.
    """
    return None

print(sample_input=None)

Prints a sample input to the console using the rich library. Helper method to visualize the prompt of the task.

Parameters:

Name	Type	Description	Default
sample_input	Optional[ChatType]	A sample input to be printed. If not provided, a default will be generated using the _sample_input method, which can be overriden by subclasses. This should correspond to the same example you could pass to the format_input method. The variables be named by default.	None

Examples:

Print the URIAL prompt:

from distilabel.steps.tasks import URIAL
from distilabel.models.llms.huggingface import InferenceEndpointsLLM

# Consider this as a placeholder for your actual LLM.
urial = URIAL(
    llm=InferenceEndpointsLLM(
        model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
    ),
)
urial.load()
urial.print()
╭─────────────────────────────────────── Prompt: URIAL  ────────────────────────────────────────╮
│ ╭────────────────────────────────────── User Message ───────────────────────────────────────╮ │
│ │ # Instruction                                                                             │ │
│ │                                                                                           │ │
│ │ Below is a list of conversations between a human and an AI assistant (you).               │ │
│ │ Users place their queries under "# User:", and your responses are under  "# Assistant:".  │ │
│ │ You are a helpful, respectful, and honest assistant.                                      │ │
│ │ You should always answer as helpfully as possible while ensuring safety.                  │ │
│ │ Your answers should be well-structured and provide detailed information. They should also │ │
│ │ have an engaging tone.                                                                    │ │
│ │ Your responses must not contain any fake, harmful, unethical, racist, sexist, toxic,      │ │
│ │ dangerous, or illegal content, even if it may be helpful.                                 │ │
│ │ Your response must be socially responsible, and thus you can refuse to answer some        │ │
│ │ controversial topics.                                                                     │ │
│ │                                                                                           │ │
│ │                                                                                           │ │
│ │ # User:                                                                                   │ │
│ │                                                                                           │ │
│ │ <PLACEHOLDER_INSTRUCTION>                                                                 │ │
│ │                                                                                           │ │
│ │ # Assistant:                                                                              │ │
│ ╰───────────────────────────────────────────────────────────────────────────────────────────╯ │
╰───────────────────────────────────────────────────────────────────────────────────────────────╯

Source code in src/distilabel/steps/tasks/base.py

def print(self, sample_input: Optional["ChatType"] = None) -> None:
    """Prints a sample input to the console using the `rich` library.
    Helper method to visualize the prompt of the task.

    Args:
        sample_input: A sample input to be printed. If not provided, a default will be
            generated using the `_sample_input` method, which can be overriden by
            subclasses. This should correspond to the same example you could pass to
            the `format_input` method.
            The variables be named <PLACEHOLDER_VARIABLE_NAME> by default.

    Examples:
        Print the URIAL prompt:

        ```python
        from distilabel.steps.tasks import URIAL
        from distilabel.models.llms.huggingface import InferenceEndpointsLLM

        # Consider this as a placeholder for your actual LLM.
        urial = URIAL(
            llm=InferenceEndpointsLLM(
                model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
            ),
        )
        urial.load()
        urial.print()
        ╭─────────────────────────────────────── Prompt: URIAL  ────────────────────────────────────────╮
        │ ╭────────────────────────────────────── User Message ───────────────────────────────────────╮ │
        │ │ # Instruction                                                                             │ │
        │ │                                                                                           │ │
        │ │ Below is a list of conversations between a human and an AI assistant (you).               │ │
        │ │ Users place their queries under "# User:", and your responses are under  "# Assistant:".  │ │
        │ │ You are a helpful, respectful, and honest assistant.                                      │ │
        │ │ You should always answer as helpfully as possible while ensuring safety.                  │ │
        │ │ Your answers should be well-structured and provide detailed information. They should also │ │
        │ │ have an engaging tone.                                                                    │ │
        │ │ Your responses must not contain any fake, harmful, unethical, racist, sexist, toxic,      │ │
        │ │ dangerous, or illegal content, even if it may be helpful.                                 │ │
        │ │ Your response must be socially responsible, and thus you can refuse to answer some        │ │
        │ │ controversial topics.                                                                     │ │
        │ │                                                                                           │ │
        │ │                                                                                           │ │
        │ │ # User:                                                                                   │ │
        │ │                                                                                           │ │
        │ │ <PLACEHOLDER_INSTRUCTION>                                                                 │ │
        │ │                                                                                           │ │
        │ │ # Assistant:                                                                              │ │
        │ ╰───────────────────────────────────────────────────────────────────────────────────────────╯ │
        ╰───────────────────────────────────────────────────────────────────────────────────────────────╯
        ```
    """
    from rich.console import Console, Group
    from rich.panel import Panel
    from rich.text import Text

    console = Console()
    sample_input = sample_input or self._sample_input()

    panels = []
    for item in sample_input:
        content = Text.assemble((item.get("content", ""),))
        panel = Panel(
            content,
            title=f"[bold][magenta]{item.get('role', '').capitalize()} Message[/magenta][/bold]",
            border_style="light_cyan3",
        )
        panels.append(panel)

    # Create a group of panels
    # Wrap the group in an outer panel
    outer_panel = Panel(
        Group(*panels),
        title=f"[bold][magenta]Prompt: {type(self).__name__} [/magenta][/bold]",
        border_style="light_cyan3",
        expand=False,
    )
    console.print(outer_panel)

Task

Bases: _Task, Step

Task is a class that implements the _Task abstract class and adds the Step interface to be used as a step in the pipeline.

Attributes:

Name	Type	Description
llm		the LLM to be used to generate the outputs of the task.
group_generations		whether to group the num_generations generated per input in a list or create a row per generation. Defaults to False.
num_generations		The number of generations to be produced per input.

Source code in src/distilabel/steps/tasks/base.py

class Task(_Task, Step):
    """Task is a class that implements the `_Task` abstract class and adds the `Step`
    interface to be used as a step in the pipeline.

    Attributes:
        llm: the `LLM` to be used to generate the outputs of the task.
        group_generations: whether to group the `num_generations` generated per input in
            a list or create a row per generation. Defaults to `False`.
        num_generations: The number of generations to be produced per input.
    """

    @abstractmethod
    def format_input(self, input: Dict[str, Any]) -> "FormattedInput":
        """Abstract method to format the inputs of the task. It needs to receive an input
        as a Python dictionary, and generates an OpenAI chat-like list of dicts."""
        pass

    def _format_inputs(self, inputs: List[Dict[str, Any]]) -> List["FormattedInput"]:
        """Formats the inputs of the task using the `format_input` method.

        Args:
            inputs: A list of Python dictionaries with the inputs of the task.

        Returns:
            A list containing the formatted inputs, which are `ChatType`-like following
            the OpenAI formatting.
        """
        return [self.format_input(input) for input in inputs]

    def process(self, inputs: StepInput) -> "StepOutput":  # type: ignore
        """Processes the inputs of the task and generates the outputs using the LLM.

        Args:
            inputs: A list of Python dictionaries with the inputs of the task.

        Yields:
            A list of Python dictionaries with the outputs of the task.
        """

        formatted_inputs = self._format_inputs(inputs)

        # `outputs` is a dict containing the LLM outputs in the `generations`
        # key and the statistics in the `statistics` key
        outputs = self.llm.generate_outputs(
            inputs=formatted_inputs,
            num_generations=self.num_generations,  # type: ignore
            **self.llm.get_generation_kwargs(),  # type: ignore
        )
        task_outputs = []
        for input, input_outputs in zip(inputs, outputs):
            formatted_outputs = self._format_outputs(input_outputs, input)

            if self.group_generations:
                combined = group_dicts(*formatted_outputs)
                task_outputs.append(
                    {**input, **combined, "model_name": self.llm.model_name}
                )
                continue

            # Create a row per generation
            for formatted_output in formatted_outputs:
                task_outputs.append(
                    {**input, **formatted_output, "model_name": self.llm.model_name}
                )

        yield task_outputs

format_input(input) abstractmethod

Abstract method to format the inputs of the task. It needs to receive an input as a Python dictionary, and generates an OpenAI chat-like list of dicts.

Source code in src/distilabel/steps/tasks/base.py

@abstractmethod
def format_input(self, input: Dict[str, Any]) -> "FormattedInput":
    """Abstract method to format the inputs of the task. It needs to receive an input
    as a Python dictionary, and generates an OpenAI chat-like list of dicts."""
    pass

process(inputs)

Processes the inputs of the task and generates the outputs using the LLM.

Parameters:

Name	Type	Description	Default
inputs	StepInput	A list of Python dictionaries with the inputs of the task.	required

Yields:

Type	Description
StepOutput	A list of Python dictionaries with the outputs of the task.

Source code in src/distilabel/steps/tasks/base.py

def process(self, inputs: StepInput) -> "StepOutput":  # type: ignore
    """Processes the inputs of the task and generates the outputs using the LLM.

    Args:
        inputs: A list of Python dictionaries with the inputs of the task.

    Yields:
        A list of Python dictionaries with the outputs of the task.
    """

    formatted_inputs = self._format_inputs(inputs)

    # `outputs` is a dict containing the LLM outputs in the `generations`
    # key and the statistics in the `statistics` key
    outputs = self.llm.generate_outputs(
        inputs=formatted_inputs,
        num_generations=self.num_generations,  # type: ignore
        **self.llm.get_generation_kwargs(),  # type: ignore
    )
    task_outputs = []
    for input, input_outputs in zip(inputs, outputs):
        formatted_outputs = self._format_outputs(input_outputs, input)

        if self.group_generations:
            combined = group_dicts(*formatted_outputs)
            task_outputs.append(
                {**input, **combined, "model_name": self.llm.model_name}
            )
            continue

        # Create a row per generation
        for formatted_output in formatted_outputs:
            task_outputs.append(
                {**input, **formatted_output, "model_name": self.llm.model_name}
            )

    yield task_outputs