Conference Talk 6: Train Almost Any LLM Model Using 🤗 autotrain
notes
llms
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).
- Link:
- 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 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
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.
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.
Interested in working together? Fill out my Quick AI Project Assessment form or learn more about me.