Getting Started
Getting started with Galileo Fine-Tune is really easy. To provide you insights on your data, Galileo needs to either train a model on your data or hook into your training flow.
You have two options:
- Hooking into your model training loop
- Using dq.auto to automate the training loop
If you prefer not to include Galileo into your Training Pipeline and just want quick insights on your data, you can use Galileo
Auto. Auto takes in your dataset as a parameter and trains a model behind the scenes. All you need to do is run the following:from dataquality.integrations.seq2seq.auto import auto
from dataquality.integrations.seq2seq.schema import Seq2SeqDatasetConfig
dataset_config = Seq2SeqDatasetConfig(train_path="train.jsonl", eval_path="eval.jsonl")
auto(
project_name="s2s_auto",
run_name="completion_dataset",
dataset_config=dataset_config,
)
To display the data as fast as possible in the console and avoid fine-tuning entirely, simply create
training_config = Seq2SeqTrainingConfig(epochs=0) and pass it to auto.If you already have a model, we recommend hooking Galileo into it during training. This will allow Galileo to tailor its insights to your own model.
To integrate into your model training, use our dataquality library. We have built easy-to-use watch functions for the most popular model frameworks. To learn about how watch works, have a look at our documentation or follow the notebook below.
Once you train a model with Galileo (either manually or with
dq.auto), your data will magically appear in Galileo's Fine-Tune Console.Uploading the data directly into the Galileo UI will be coming soon.
Last modified 26d ago