🤗 AutoTrain Advanced

AutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning models. AutoTrain Advanced is a no-code solution that allows you to train machine learning models in just a few clicks. Please note that you must upload data in correct format for project to be created. For help regarding proper data format and pricing, check out the documentation.

NOTE: AutoTrain is free! You only pay for the resources you use in case you decide to run AutoTrain on Hugging Face Spaces. When running locally, you only pay for the resources you use on your own infrastructure.

Supported Tasks

Task	Status	Python Notebook	Example Configs
LLM SFT Finetuning	✅		llm_sft_finetune.yaml
LLM ORPO Finetuning	✅		llm_orpo_finetune.yaml
LLM DPO Finetuning	✅		llm_dpo_finetune.yaml
LLM Reward Finetuning	✅		llm_reward_finetune.yaml
LLM Generic/Default Finetuning	✅		llm_generic_finetune.yaml
Text Classification	✅		text_classification.yaml
Text Regression	✅		text_regression.yaml
Token Classification	✅	Coming Soon	token_classification.yaml
Seq2Seq	✅	Coming Soon	seq2seq.yaml
Extractive Question Answering	✅	Coming Soon	extractive_qa.yaml
Image Classification	✅	Coming Soon	image_classification.yaml
Image Scoring/Regression	✅	Coming Soon	image_regression.yaml
DreamBooth LoRA	✅		dreambooth_lora.yaml
VLM	🟥	Coming Soon	vlm.yaml

Running UI on Colab or Hugging Face Spaces

Deploy AutoTrain on Hugging Face Spaces:
Run AutoTrain UI on Colab via ngrok:

Local Installation

You can Install AutoTrain-Advanced python package via PIP. Please note you will need python >= 3.10 for AutoTrain Advanced to work properly.

pip install autotrain-advanced

Please make sure that you have git lfs installed. Check out the instructions here: https://github.com/git-lfs/git-lfs/wiki/Installation

You also need to install torch, torchaudio and torchvision.

The best way to run autotrain is in a conda environment. You can create a new conda environment with the following command:

conda create -n autotrain python=3.10
conda activate autotrain
pip install autotrain-advanced
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
conda install -c "nvidia/label/cuda-12.1.0" cuda-nvcc

Once done, you can start the application using:

autotrain app --port 8080 --host 127.0.0.1

If you are not fond of UI, you can use AutoTrain Configs to train using command line or simply AutoTrain CLI.

To use config file for training, you can use the following command:

autotrain --config <path_to_config_file>

You can find sample config files in the configs directory of this repository.

Example config file for finetuning SmolLM2:

task: llm-sft
base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
project_name: autotrain-smollm2-finetune
log: tensorboard
backend: local

data:
  path: HuggingFaceH4/no_robots
  train_split: train
  valid_split: null
  chat_template: tokenizer
  column_mapping:
    text_column: messages

params:
  block_size: 2048
  model_max_length: 4096
  epochs: 2
  batch_size: 1
  lr: 1e-5
  peft: true
  quantization: int4
  target_modules: all-linear
  padding: right
  optimizer: paged_adamw_8bit
  scheduler: linear
  gradient_accumulation: 8
  mixed_precision: bf16
  merge_adapter: true

hub:
  username: ${HF_USERNAME}
  token: ${HF_TOKEN}
  push_to_hub: true

To fine-tune a model using the config file above, you can use the following command:

$ export HF_USERNAME=<your_hugging_face_username>
$ export HF_TOKEN=<your_hugging_face_write_token>
$ autotrain --config <path_to_config_file>

Documentation

Documentation is available at https://hf.co/docs/autotrain/

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