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EvolComplexity

Evolve instructions to make them more complex using an LLM.

EvolComplexity is a task that evolves instructions to make them more complex, and it is based in the EvolInstruct task, using slight different prompts, but the exact same evolutionary approach.

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 number of evolutions to be run.

Input & Output Columns

graph TD
    subgraph Dataset
        subgraph Columns
            ICOL0[instruction]
        end
        subgraph New columns
            OCOL0[evolved_instruction]
            OCOL1[answer]
            OCOL2[model_name]
        end
    end

    subgraph EvolComplexity
        StepInput[Input Columns: instruction]
        StepOutput[Output Columns: evolved_instruction, answer, model_name]
    end

    ICOL0 --> StepInput
    StepOutput --> OCOL0
    StepOutput --> OCOL1
    StepOutput --> OCOL2
    StepInput --> StepOutput

Inputs

  • instruction (str): The instruction to evolve.

Outputs

  • evolved_instruction (str): The evolved instruction.

  • answer (str, optional): The answer to the instruction if generate_answers=True.

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

Examples

Evolve an instruction using an LLM

from distilabel.steps.tasks import EvolComplexity
from distilabel.models import InferenceEndpointsLLM

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

evol_complexity.load()

result = next(evol_complexity.process([{"instruction": "common instruction"}]))
# result
# [{'instruction': 'common instruction', 'evolved_instruction': 'evolved instruction', 'model_name': 'model_name'}]

References