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Self instruct

SelfInstruct

Bases: Task

SelfInstruct is a pre-defined task that, given a number of instructions, a certain criteria for query generations, an application description, and an input, generates a number of instruction related to the given input and following what is stated in the criteria for query generation and the application description. It is based in the SelfInstruct framework from the paper "Self-Instruct: Aligning Language Models with Self-Generated Instructions".

Attributes:

Name Type Description
num_instructions int

The number of instructions to be generated. Defaults to 5.
criteria_for_query_generation str

The criteria for the query generation. Defaults to the criteria defined within the paper.
application_description str

The description of the AI application that one want to build with these instructions. Defaults to AI assistant.
Input columns
  • input (str): The input to generate the instructions. It's also called seed in the paper.
Output columns
  • instructions (List[str]): The generated instructions.
Reference
Source code in src/distilabel/steps/tasks/self_instruct.py 
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class SelfInstruct(Task):
    """SelfInstruct is a pre-defined task that, given a number of instructions, a
    certain criteria for query generations, an application description, and an input,
    generates a number of instruction related to the given input and following what
    is stated in the criteria for query generation and the application description.
    It is based in the SelfInstruct framework from the paper "Self-Instruct: Aligning
    Language Models with Self-Generated Instructions".

    Attributes:
        num_instructions: The number of instructions to be generated. Defaults to 5.
        criteria_for_query_generation: The criteria for the query generation. Defaults
            to the criteria defined within the paper.
        application_description: The description of the AI application that one want
            to build with these instructions. Defaults to `AI assistant`.

    Input columns:
        - input (`str`): The input to generate the instructions. It's also called seed in the paper.

    Output columns:
        - instructions (`List[str]`): The generated instructions.

    Reference:
        - [`Self-Instruct: Aligning Language Models with Self-Generated Instructions`](https://arxiv.org/abs/2212.10560)
    """

    num_instructions: int = 5

    criteria_for_query_generation: str = (
        "Incorporate a diverse range of verbs, avoiding repetition.\n"
        "Ensure queries are compatible with AI model's text generation functions and are limited to 1-2 sentences.\n"
        "Design queries to be self-contained and standalone.\n"
        'Blend interrogative (e.g., "What is the significance of x?") and imperative (e.g., "Detail the process of x.") styles.'
    )

    application_description: str = "AI assistant"

    _template: Template = PrivateAttr(default=...)

    def load(self) -> None:
        """Loads the Jinja2 template for SelfInstruct."""
        super().load()

        _path = str(
            importlib_resources.files("distilabel")
            / "steps"
            / "tasks"
            / "templates"
            / "self-instruct.jinja2"
        )

        self._template = Template(open(_path).read())

    @property
    def inputs(self) -> List[str]:
        """The input for the task is the `input` i.e. seed text."""
        return ["input"]

    def format_input(self, input: Dict[str, Any]) -> "ChatType":
        """The input is formatted as a `ChatType` assuming that the instruction
        is the first interaction from the user within a conversation."""

        return [
            {
                "role": "user",
                "content": self._template.render(
                    input=input["input"],
                    application_description=self.application_description,
                    criteria_for_query_generation=self.criteria_for_query_generation,
                    num_instructions=self.num_instructions,
                ),
            }
        ]

    @property
    def outputs(self):
        """The output for the task is a list of `instructions` containing the generated instructions."""
        return ["instructions"]

    def format_output(
        self,
        output: Union[str, None],
        input: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, Any]:
        """The output is formatted as a list with the generated instructions.

        Args:
            output: the raw output of the LLM.
            input: the input to the task. Used for obtaining the number of responses.

        Returns:
            A dict with containing the generated instructions.
        """

        if output is None:
            return {"instructions": []}

        lines = [line for line in output.split("\n") if line != ""]
        return {"instructions": lines}

inputs: List[str] property

The input for the task is the input i.e. seed text.

outputs property

The output for the task is a list of instructions containing the generated instructions.

format_input(input)

The input is formatted as a ChatType assuming that the instruction is the first interaction from the user within a conversation.

Source code in src/distilabel/steps/tasks/self_instruct.py 
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def format_input(self, input: Dict[str, Any]) -> "ChatType":
    """The input is formatted as a `ChatType` assuming that the instruction
    is the first interaction from the user within a conversation."""

    return [
        {
            "role": "user",
            "content": self._template.render(
                input=input["input"],
                application_description=self.application_description,
                criteria_for_query_generation=self.criteria_for_query_generation,
                num_instructions=self.num_instructions,
            ),
        }
    ]

format_output(output, input=None)

The output is formatted as a list with the generated instructions.

Parameters:

Name Type Description Default
output Union[str, None]

the raw output of the LLM.

 required
input Optional[Dict[str, Any]]

the input to the task. Used for obtaining the number of responses.

 None

Returns:

Type Description
Dict[str, Any]

A dict with containing the generated instructions.
Source code in src/distilabel/steps/tasks/self_instruct.py 
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def format_output(
    self,
    output: Union[str, None],
    input: Optional[Dict[str, Any]] = None,
) -> Dict[str, Any]:
    """The output is formatted as a list with the generated instructions.

    Args:
        output: the raw output of the LLM.
        input: the input to the task. Used for obtaining the number of responses.

    Returns:
        A dict with containing the generated instructions.
    """

    if output is None:
        return {"instructions": []}

    lines = [line for line in output.split("\n") if line != ""]
    return {"instructions": lines}

load()

Loads the Jinja2 template for SelfInstruct.

Source code in src/distilabel/steps/tasks/self_instruct.py 
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def load(self) -> None:
    """Loads the Jinja2 template for SelfInstruct."""
    super().load()

    _path = str(
        importlib_resources.files("distilabel")
        / "steps"
        / "tasks"
        / "templates"
        / "self-instruct.jinja2"
    )

    self._template = Template(open(_path).read())