Run on non-adapted data

Use raw dataset processing when a tabular file already contains training-ready prompt and completion columns and you want AutoScientist to train on those rows as-is with SFT. This skips Adaptive Data’s adaptation pipeline.

For best results, adapt your data first. Use raw processing only when training on the original rows is intentional.

Raw processing does not mark a dataset as preference_pairs. For DPO, first complete an Adaptive Data preference-pair run, then create a DPO run.

Raw processing supports CSV, JSON, JSONL, and Parquet files. Set processing_mode to raw and map both required source columns when creating the dataset.

Upload and train

This example uploads a CSV whose instruction and response columns contain the training pairs:

from adaption import Adaption

client = Adaption()

dataset = client.datasets.upload_file(
    "training_data.csv",
    processing_mode="raw",
    column_mapping={
        "prompt": "instruction",
        "completion": "response",
    },
)

client.datasets.wait_for_completion(dataset.dataset_id)

run = client.autoscientist.create(dataset_id=dataset.dataset_id)
run = client.autoscientist.wait_for_completion(run.id)

if run.status == "failed":
    raise RuntimeError(f"failed: {run.error}")
if run.status != "succeeded":
    raise RuntimeError(f"Run ended with status: {run.status}")

print(run.id, run.best_win_rate)

Do not call client.datasets.adapt for this workflow: raw processing makes the uploaded dataset ready for AutoScientist directly. The column_mapping above refers to the original file headers; an AutoScientist column_mapping, if supplied later, refers to the processed dataset schema.

See Running AutoScientist for polling, cancellation, and status details. For endpoint details, see the datasets.create and autoscientist.create references.