a smol course

This is a practical course on aligning language models for your specific use case. It's a handy way to get started with aligning language models, because everything runs on most local machines. There are minimal GPU requirements and no paid services. The course is based on the SmolLM2 series of models, but you can transfer the skills you learn here to larger models or other small language models.

Participation is open, free, and now!

This course is open and peer reviewed. To get involved with the course open a pull request and submit your work for review. Here are the steps:

1. Fork the repo here
2. Read the material, make changes, do the exercises, add your own examples.
3. Open a PR on the december_2024 branch
4. Get it reviewed and merged

This should help you learn and to build a community-driven course that is always improving.

We can discuss the process in this discussion thread.

Course Outline

This course provides a practical, hands-on approach to working with small language models, from initial training through to production deployment.

Module	Description	Status	Release Date
Instruction Tuning	Learn supervised fine-tuning, chat templating, and basic instruction following	✅ Ready	Dec 3, 2024
Preference Alignment	Explore DPO and ORPO techniques for aligning models with human preferences	✅ Ready	Dec 6, 2024
Parameter-efficient Fine-tuning	Learn LoRA, prompt tuning, and efficient adaptation methods	✅ Ready	Dec 9, 2024
Evaluation	Use automatic benchmarks and create custom domain evaluations	✅ Ready	Dec 13, 2024
Vision-language Models	Adapt multimodal models for vision-language tasks	🚧 WIP	Dec 16, 2024
Synthetic Datasets	Create and validate synthetic datasets for training	🚧 WIP	Dec 20, 2024
Inference	Infer with models efficiently	📝 Planned	Dec 23, 2024

Why Small Language Models?

While large language models have shown impressive capabilities, they often require significant computational resources and can be overkill for focused applications. Small language models offer several advantages for domain-specific applications:

• Efficiency: Require significantly less computational resources to train and deploy
• Customization: Easier to fine-tune and adapt to specific domains
• Control: Better understanding and control of model behavior
• Cost: Lower operational costs for training and inference
• Privacy: Can be run locally without sending data to external APIs
• Green Technology: Advocates efficient usage of resources with reduced carbon footprint
• Easier Academic Research Development: Provides an easy starter for academic research with cutting-edge LLMs with less logistical constraints

Prerequisites

Before starting, ensure you have the following:

• Basic understanding of machine learning and natural language processing.
• Familiarity with Python, PyTorch, and the transformers library.
• Access to a pre-trained language model and a labeled dataset.

Installation

We maintain the course as a package so you can install dependencies easily via a package manager. We recommend uv for this purpose, but you could use alternatives like pip or pdm.

Using uv

With uv installed, you can install the course like this:

uv venv --python 3.11.0
uv sync

Using pip

All the examples run in the same python 3.11 environment, so you should create an environment and install dependencies like this:

# python -m venv .venv
# source .venv/bin/activate
pip install -r requirements.txt

Google Colab

From Google Colab you will need to install dependencies flexibly based on the hardware you're using. Like this:

pip install transformers trl datasets huggingface_hub

Engagement

Let's share this, so that loads of people can learn to finetune LLMs without expensive hardware.