AutoTrain让零代码训练模型变得简单，支持多种任务和模态。
AutoTrain: No-code training for state-of-the-art models
- 零代码界面，自动处理不同任务的模型训练流程
- 支持语言、图像、表格等多模态任务，覆盖数十万Hugging Face模型
- 适合无机器学习背景的开发者快速部署定制化AI应用
随着开源模型的发展，在自定义数据集上训练（或微调）模型已成为开发面向特定工业或开源应用解决方案的关键步骤。然而，目前尚无单一工具能简化跨多种模态或任务的训练过程。我们推出了AutoTrain（又称AutoTrain Advanced）——一个开源的无代码工具/库，可用于训练（或微调）多种类型的任务模型，包括大语言模型微调、文本分类/回归、词元分类、序列到序列任务、句向量模型微调、视觉语言模型微调、图像分类/回归，以及表格数据的分类与回归任务。AutoTrain Advanced是一个提供最佳实践的开源库，支持在本地或云端运行，兼容Hugging Face Hub上数以万计的模型及其变体。
原文摘要 · Abstract (English)
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.
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