huggingface/autotrain-advanced

pip install autotrain-advanced

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

autotrain app --port 8080 --host 127.0.0.1

autotrain --config <path_to_config_file>

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

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

@inproceedings{thakur-2024-autotrain,
    title = "{A}uto{T}rain: No-code training for state-of-the-art models",
    author = "Thakur, Abhishek",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-demo.44",
    pages = "419--423",
    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.",
}