AutoTrain: No-code training for state-of-the-art models

TL;DR

AutoTrain is a versatile, open-source, no-code tool that simplifies training and fine-tuning models across various modalities and tasks, making it accessible for both local and cloud environments.

Contribution

It introduces AutoTrain Advanced, a comprehensive library that supports multi-modal, multi-task model training without coding, integrating best practices and broad model compatibility.

Findings

Supports diverse modalities and tasks including LLMs, VLMs, and tabular data.

Enables training on local or cloud environments.

Integrates with Hugging Face Hub models.

Abstract

With the advancements in open-source models, training (or finetuning) models on custom datasets has become a crucial part of developing solutions which are tailored to specific industrial or open-source applications. Yet, there is no single tool which simplifies the process of training across different types of modalities or tasks. We introduce AutoTrain (aka AutoTrain Advanced) -- an open-source, no code tool/library which can be used to train (or finetune) models for different kinds of tasks such as: large language model (LLM) finetuning, text classification/regression, token classification, sequence-to-sequence task, finetuning of sentence transformers, visual language model (VLM) finetuning, image classification/regression and even classification and regression tasks on tabular data. AutoTrain Advanced is an open-source library providing best practices for training models on custom datasets. The library is available at https://github.com/huggingface/autotrain-advanced. AutoTrain can be used in fully local mode or on cloud machines and works with tens of thousands of models shared on Hugging Face Hub and their variations.