How-To
Fine-tune SmolLM2 on a reasoning corpus
A Colab workflow streams 8,000 reasoning records, filters them, fine-tunes SmolLM2 with LoRA, and exports training data as Parquet.
A compact language model can be adapted for structured reasoning without downloading an entire multi-million-record corpus. In a tutorial, Marktechpost demonstrates a Google Colab workflow that streams the SupraLabs reasoning corpus, filters its records, fine-tunes SmolLM2-135M-Instruct with LoRA, and exports the resulting datasets as Parquet files.
The pipeline uses Hugging Face Datasets, Transformers, TRL, PEFT, Accelerate, bitsandbytes, pandas, and Matplotlib. It also removes the incompatible torchao package before setup.
The result is a small, reusable training pipeline that keeps the large corpus out of Colab memory while preserving source inspection, quality controls, parameter-efficient fine-tuning, structured generation, and dataset export. The tutorial points readers to its full code for the complete implementation.