AutoTrain Advanced provides a no-code solution for training and deploying state-of-the-art machine learning models, targeting users who want to fine-tune LLMs or perform various NLP and image tasks without extensive coding. It simplifies the process of customizing models for specific applications, offering flexibility for both local and cloud-based execution.

How It Works

AutoTrain Advanced leverages configuration files and a user-friendly interface to abstract away complex training pipelines. It supports multiple LLM fine-tuning methods (SFT, ORPO, DPO, Reward) and various NLP tasks, with plans for image-related tasks. The system is designed to work seamlessly with Hugging Face Hub models, allowing users to specify base models, datasets, and training parameters like quantization, PEFT, and mixed precision through YAML configurations.

Quick Start & Requirements

• Install: pip install autotrain-advanced
• Prerequisites: Python >= 3.10, Git LFS, PyTorch with CUDA 12.1 support. A conda environment is recommended for managing dependencies.
• Run UI: autotrain app --port 8080 --host 127.0.0.1
• Run CLI: autotrain --config <path_to_config_file>
• Documentation: https://hf.co/docs/autotrain/

Highlighted Details

• Supports LLM SFT, ORPO, DPO, and Reward fine-tuning.
• Offers no-code UI and CLI/config-based training.
• Integrates with Hugging Face Hub for models and datasets.
• Supports advanced training parameters like PEFT, quantization (int4), and mixed precision.

Maintenance & Community

The project is actively maintained by Hugging Face. Community support channels are not explicitly mentioned in the README.

Licensing & Compatibility

The repository is licensed under the Apache 2.0 license, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

Some tasks, including Token Classification, Seq2Seq, Extractive Question Answering, Image Classification, and Image Scoring/Regression, are marked as "Coming Soon." Visual Language Model (VLM) finetuning is marked as unsupported (🟥).