prometheus_pipeline.py

# pip install "distilabel[vllm] @ git+https://github.com/argilla-io/distilabel.git@develop"
# pip install flash-attn --no-build-isolation
# huggingface-cli login

import time

from distilabel.pipeline import Pipeline
from distilabel.steps import KeepColumns, LoadHubDataset
from distilabel.steps.tasks import PrometheusEval
from distilabel.llms import TransformersLLM

if __name__ == "__main__":
    start_time = time.time()

    with Pipeline(name="prometheus") as pipeline:
        load_dataset = LoadHubDataset(
            name="load_dataset",
            repo_id="HuggingFaceH4/instruction-dataset",
            split="test",
            output_mappings={"prompt": "instruction", "completion": "generation"},
        )

        task = PrometheusEval(
            name="task",
            llm=TransformersLLM(
                model="prometheus-eval/prometheus-7b-v2.0",
                chat_template="[INST] {{ messages[0]['content'] }}\n{{ messages[1]['content'] }}[/INST]",
            ),
            mode="absolute",
            rubric="factual-validity",
            reference=False,
            num_generations=1,
            group_generations=False,
        )

        keep_columns = KeepColumns(
            name="keep_columns",
            columns=["instruction", "generation", "feedback", "result", "model_name"],
        )

        load_dataset >> task >> keep_columns  # type: ignore

    distiset = pipeline.run(
        parameters={
            task.name: {  # type: ignore
                "llm": {
                    "generation_kwargs": {
                        "max_new_tokens": 1024,
                        "temperature": 0.7,
                    },
                },
            },
        },
    )
    print("--- %s seconds ---" % (time.time() - start_time))

    if distiset is not None:
        distiset.push_to_hub("instruction-dataset-prometheus")