AutoTrain: No-code training for state-of-the-art models
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 21 Oct 2024.
Builds on
6 of 15 references.
References · 6
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- 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
- 2021Datasets: A Community Library for Natural Language Processingcited by 530
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Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalogcited by 10
- 2025Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisationcited by 0
- 2026The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Trainingcited by 0
- 2026The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training9 Apr 2026
- 2025Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation9 Jun 2025
- 2025SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalog9 Apr 2025
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- 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
- 2026Steering LLMs? Actually, Sparse Autoencoders can outperform simple baselines
- 2026The Finetuner's Fallacy: When to Pretrain with Your Finetuning Data
- 2026Guiding LLM Post-training Data Engineering with Model Internals from Sparse Autoencoders
- 2024Non-instructional Fine-tuning: Enabling Instruction-Following Capabilities in Pre-trained Language Models without Instruction-Following Data