#4747th of 65 in Learning and cookbooks
smol-course
Hands-on course on fine-tuning and aligning small language models
- Stars
- 6.8k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Language
- Jupyter Notebook
6,554 on 4 Apr 20266,787 on 31 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
A Hugging Face course on aligning small language models, built around SmolLM3 and SmolVLM2. It covers instruction tuning, evaluation, preference alignment with DPO, and vision language models, with reinforcement learning and synthetic data listed as later modules. Exercises are meant to run on most local machines, and learners submit work by pull request and to a leaderboard.
Who it is for: ML engineers learning to fine-tune and align small LLMs and VLMs locally
Strengths
- Covers SFT, evaluation, DPO and vision language models in one sequence
- Uses small models (SmolLM3, SmolVLM2) so exercises run on local machines
- Open to pull requests, with a leaderboard for submitted models
- Original v1 notebooks are kept in the v1 directory
Weaknesses
- RL and synthetic data modules were dated Oct and Nov 2025; README does not confirm release
- README says the course will be re-released on Hugging Face Learn
- Assumes prior knowledge of PyTorch and the transformers library
- No exact RAM or GPU figures are given
What it needs
- GPU optional
- Needs PyTorch, transformers, Hugging Face Hub
- Models: SmolLM3, SmolVLM2
Quick answers
Is smol-course open source?
Yes. Its code is on GitHub under the Apache-2.0 license.
Does smol-course need a GPU?
No. It runs on a CPU and can use a GPU when you have one.
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