Genstruct¶

Generate a pair of instruction-response from a document using an LLM.

Genstruct is a pre-defined task designed to generate valid instructions from a given raw document, with the title and the content, enabling the creation of new, partially synthetic instruction finetuning datasets from any raw-text corpus. The task is based on the Genstruct 7B model by Nous Research, which is inspired in the Ada-Instruct paper.

Note¶

The Genstruct prompt i.e. the task, can be used with any model really, but the safest / recommended option is to use NousResearch/Genstruct-7B as the LLM provided to the task, since it was trained for this specific task.

Attributes¶

_template: a Jinja2 template used to format the input for the LLM.

Input & Output Columns¶

graph TD
    subgraph Dataset
        subgraph Columns
            ICOL0[title]
            ICOL1[content]
        end
        subgraph New columns
            OCOL0[user]
            OCOL1[assistant]
            OCOL2[model_name]
        end
    end

    subgraph Genstruct
        StepInput[Input Columns: title, content]
        StepOutput[Output Columns: user, assistant, model_name]
    end

    ICOL0 --> StepInput
    ICOL1 --> StepInput
    StepOutput --> OCOL0
    StepOutput --> OCOL1
    StepOutput --> OCOL2
    StepInput --> StepOutput

Inputs¶

title (str): The title of the document.
content (str): The content of the document.

Outputs¶

user (str): The user's instruction based on the document.
assistant (str): The assistant's response based on the user's instruction.
model_name (str): The model name used to generate the feedback and result.

Examples¶

Generate instructions from raw documents using the title and content¶

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

# Consider this as a placeholder for your actual LLM.
genstruct = Genstruct(
    llm=InferenceEndpointsLLM(
        model_id="NousResearch/Genstruct-7B",
    ),
)

genstruct.load()

result = next(
    genstruct.process(
        [
            {"title": "common instruction", "content": "content of the document"},
        ]
    )
)
# result
# [
#     {
#         'title': 'An instruction',
#         'content': 'content of the document',
#         'model_name': 'test',
#         'user': 'An instruction',
#         'assistant': 'content of the document',
#     }
# ]