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URIAL

Generates a response using a non-instruct fine-tuned model.

URIAL is a pre-defined task that generates a response using a non-instruct fine-tuned model. This task is used to generate a response based on the conversation provided as input.

Input & Output Columns

graph TD
    subgraph Dataset
        subgraph Columns
            ICOL0[instruction]
            ICOL1[conversation]
        end
        subgraph New columns
            OCOL0[generation]
            OCOL1[model_name]
        end
    end

    subgraph URIAL
        StepInput[Input Columns: instruction, conversation]
        StepOutput[Output Columns: generation, model_name]
    end

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

Inputs

  • instruction (str, optional): The instruction to generate a response from.

  • conversation (List[Dict[str, str]], optional): The conversation to generate a response from (the last message must be from the user).

Outputs

  • generation (str): The generated response.

  • model_name (str): The name of the model used to generate the response.

Examples

Generate text from an instruction

from distilabel.llms import vLLM
from distilabel.steps.tasks import URIAL

step = URIAL(
    llm=vLLM(
        model="meta-llama/Meta-Llama-3.1-8B",
        generation_kwargs={"temperature": 0.7},
    ),
)

step.load()

results = next(
    step.process(inputs=[{"instruction": "What's the most most common type of cloud?"}])
)
# [
#     {
#         'instruction': "What's the most most common type of cloud?",
#         'generation': 'Clouds are classified into three main types, high, middle, and low. The most common type of cloud is the middle cloud.',
#         'distilabel_metadata': {...},
#         'model_name': 'meta-llama/Meta-Llama-3.1-8B'
#     }
# ]

References