Conference Talk 6: Train Almost Any LLM Model Using 🤗 autotrain

In this talk, Abhishek Thakur, who leads AutoTrain at 🤗, shows how to use 🤗 AutoTrain to train/fine-tune LLMs without having to write any code.

This post is part of the following series:

• Mastering LLMs Course Notes: My notes from the course Mastering LLMs: A Conference For Developers & Data Scientists by Hamel Husain and Dan Becker.

• Introduction to AutoTrain
• Getting Started with AutoTrain
• Fine-tuning LLMs with AutoTrain
• Training Your Model
• Config Files and Advanced Options
• Additional Features and Considerations
• Q&A Session

Introduction to AutoTrain

• Homepage: https://huggingface.co/autotrain
• Documentation: https://huggingface.co/docs/autotrain/index
• GitHub Repository: autotrain-advanced
• Simplifies model training and fine-tuning for users with varying levels of expertise, from beginners to experienced data scientists.
• Supported Tasks:
  • NLP: Token classification, text classification, LLM tasks (e.g., SFT, R4, DPO, reward tuning), sentence transformer fine-tuning, etc.
  • Computer Vision: Image classification, Object Detection
  • Tabular Data: Classification, Regression
• Leverages the Hugging Face ecosystem, including transformers, datasets, diffusers, and Accelerate, ensuring compatibility with the latest models and tools.

Getting Started with AutoTrain

• Create a new project:
  • Link: Create new project
  • Optionally specify an organization and attach hardware (local or Hugging Face spaces).
  • Choose the desired task (e.g., LLM SFT).
• User-Friendly Interface:
  • Select a task.
  • Upload your data or use a dataset from the Hugging Face Hub.
  • Configure parameters or use default settings.
  • Monitor training progress and logs.
• Documentation: Creating a New AutoTrain Space

Fine-tuning LLMs with AutoTrain

Supervised Fine-tuning (SFT) and Generic Fine-tuning

• Documentation: Supervised Fine-tuning Trainer
• Both trainers are similar, but SFT uses the TRL library’s SFT trainer.
  • Documentation: TRL - Transformer Reinforcement Learning
• Requires a “text” column in your dataset (can be mapped from a different column name).
• Supports chat template formatting (chatML, Sapphire, tokenizer’s template).
• Example datasets:
  • Salesforce/wikitext: plain text format
  • Chat format with “content” and “role” fields (requires chat template).

Reward Modeling

• Documentation: Reward Modeling
• Trains a custom reward model for sequence classification.
• Dataset requires “chosen” and “rejected” text columns.

DPO and ORPO

• DPO - Direct Preference Optimization
  • Documentation: DPO Trainer
• ORPO - Odds Ratio Preference Optimization
  • Documentation: ORPO Trainer
• ORPO is recommended over DPO as it requires less memory and compute.
• Dataset requires “prompt,” “chosen,” and “rejected” columns (all conversations).
• Supports chat templates.

Training Your Model

Data Format

• Use CSV or JSON Lines (JSONL) format
  • JSONL preferred for readability and ease of use.
• Format examples:
  • Alpaca dataset: Single “text” field with formatted text (no chat template needed).
  • Chat format: Requires chat template or offline conversion to plain text.

Training Locally

• Documentation: Quickstart

• Set Hugging Face token:

  •   export HF_TOKEN=<your_token>

• Run the AutoTrain app: autotrain app

  •   autotrain app --port 8080 --host 127.0.0.1

• Alternatively, use config files or CLI commands.

  •   autotrain --config <path_to_config_file>

Local Installation

PIP

pip install autotrain-advanced

Conda

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

Training on Other Platforms

• Jarvis Labs: Provides AutoTrain templates for easy setup and training.
• DGX Cloud: Rent high-performance GPUs for training large models.
• Google Colab: Run AutoTrain directly in Colab using provided notebooks and UI.

Config Files and Advanced Options

• Documentation: AutoTrain Configs
• Config files offer more flexibility and control over training parameters.
• Define task, base model, data paths, column mapping, hyperparameters, logging, and more.
• Access example config files in the AutoTrain GitHub repository.
  • GitHub Repository: autotrain-advanced/configs

Additional Features and Considerations

• AutoTrain automatically handles multi-GPU training using DeepSpeed or distributed data parallel.
• QLORA is supported on DeepSpeed for efficient training.
• Sentence Transformer fine-tuning is available for tasks like improving RAG models.

Q&A Session

• Logging: Supports Weights & Biases (W&B) logging when using config files.
• Mixed Precision: Supports BF16 and FP16, but not FP8.
• Parameter Compatibility: AutoTrain ensures parameter compatibility based on the chosen base model.
• Hyperparameter Optimization: Not currently supported for LLMs due to long training times.
• CPU Training: Possible, but may come with performance limitations.
• Custom Chat Templates: Can be added by modifying the tokenizer_config.json file of a cloned model.
• Synthetic Data Generation: Not currently supported, but users can generate their own.

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About Me:

I’m Christian Mills, a deep learning consultant specializing in practical AI implementations. I help clients leverage cutting-edge AI technologies to solve real-world problems.

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