Skip to content

EvolInstructGenerator

Generate evolved instructions using an LLM.

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Attributes

  • num_instructions: The number of instructions to be generated.

  • generate_answers: Whether to generate answers for the instructions or not. Defaults to False.

  • mutation_templates: The mutation templates to be used for the generation of the instructions.

  • min_length: Defines the length (in bytes) that the generated instruction needs to be higher than, to be considered valid. Defaults to 512.

  • max_length: Defines the length (in bytes) that the generated instruction needs to be lower than, to be considered valid. Defaults to 1024.

  • seed: The seed to be set for numpy in order to randomly pick a mutation method. Defaults to 42.

Runtime Parameters

  • min_length: Defines the length (in bytes) that the generated instruction needs to be higher than, to be considered valid.

  • max_length: Defines the length (in bytes) that the generated instruction needs to be lower than, to be considered valid.

  • seed: The seed to be set for numpy in order to randomly pick a mutation method.

Input & Output Columns

graph TD
    subgraph Dataset
        subgraph New columns
            OCOL0[instruction]
            OCOL1[answer]
            OCOL2[instructions]
            OCOL3[model_name]
        end
    end

    subgraph EvolInstructGenerator
        StepOutput[Output Columns: instruction, answer, instructions, model_name]
    end

    StepOutput --> OCOL0
    StepOutput --> OCOL1
    StepOutput --> OCOL2
    StepOutput --> OCOL3

Outputs

  • instruction (str): The generated instruction if generate_answers=False.

  • answer (str): The generated answer if generate_answers=True.

  • instructions (List[str]): The generated instructions if generate_answers=True.

  • model_name (str): The name of the LLM used to generate and evolve the instructions.

Examples

Generate evolved instructions without initial instructions

from distilabel.steps.tasks import EvolInstructGenerator
from distilabel.llms.huggingface import InferenceEndpointsLLM

# Consider this as a placeholder for your actual LLM.
evol_instruct_generator = EvolInstructGenerator(
    llm=InferenceEndpointsLLM(
        model_id="mistralai/Mistral-7B-Instruct-v0.2",
    ),
    num_instructions=2,
)

evol_instruct_generator.load()

result = next(scorer.process())
# result
# [{'instruction': 'generated instruction', 'model_name': 'test'}]

References