---
title: AutoScientist | Adaption
description: AutoScientist co-optimizes your data and model training recipe automatically, self-improving across both until quality converges on the goal you describe. Where Adaptive Data shapes the inputs, AutoScientist shapes the model: it runs the full research loop end-to-end so you walk away with a model adapted to your goal, rather than a dataset you still need to train on yourself.
---

## Getting Started

Once you’ve launched AutoScientist, start by selecting a dataset you’ve already run through Adaptive Data.

## Step 1 | Instruction Tuning

**Do you have size constraints for the model you want to train?**

- **All sizes** (no restrictions, best possible results)
- **Tiny AutoScientist** (restricts training to models under 10B parameters)

## Step 2 | Column Training

Functionally the same concept as Adaptive Data’s column mapping: choose a **Prompt column** and **Completion column**.\
The screen previews the result of the **Original prompt** & **completion** vs the **Enhanced prompt**, **Enhanced completion**. The option to select the original columns is also available.

## Step 3 | Augmentation

AutoScientist evaluates your dataset and can surface up to two independent, opt-in expansions, each priced and sized live as you move its slider:

- **Domain dataset expansion** — appears when you have fewer than 20,000 datapoints. You set a target datapoint count with a slider; the menu shows the resulting **Credits required** and **Expanded dataset size** live.
- **Diversity dataset expansion** — appears when the dataset isn’t diverse enough. Same slider-driven credits/row-count pattern as domain expansion, using general-purpose rather than domain-specific datapoints.

## Step 4 | Recipe

AutoScientist proposes a full hyperparameter recipe as editable fields, including Algorithm, Epochs, Rank of lora, Model Name, Alpha, Target Layers, Optimizer, Warmup Ratio, and Gradient clipping. Every field is editable before you continue.

## Step 5 | Confirmation & Training

**Confirm your optimization choices** — a raw JSON view of the exact configuration, with Copy and Edit controls, giving you a full technical read of what will actually run.

You are then met with a final order summary breaking cost down by component: domain dataset expansion, diversity dataset expansion, and AutoScientist training itself.\
On your dashboard, under AutoScientist, you can track the progression of the run.

## Step 6 | Understanding Results Once a run completes, the results page shows your trained model ID, with **Weights export** and **AutoScientist Config**.

The **Training Winrates** chart compares your base vs adapted dataset winrate.\
For example: base 30 vs adapted 70\
-The adapted model won roughly 70% of head-to-head comparisons against the original base model on your own dataset

**Train/Eval Metrics**\
Shows standard training diagnostics over training steps

## Common issues

- **Flat or negative quality/win rate after training** — use at least a 7–8B model, increase epochs, and consider the Augmentation step’s 20,000-datapoint threshold.
- **No way to get the model onto Hugging Face** — use the manual download → blank repo → web UI upload.
- **Can’t tell if a job is running, queued, or stuck** — check the AutoScientist tab for state and Measure tab for metrics; ask support to confirm status directly if truly uncertain after a long wait.
- **Pass rows in Autoscientist without adapting it** - you are not obliged to use enhanced rows, you can choose to use the original rows.