The SupraLabs reasoning corpus is massive — millions of rows spanning multiple models, tasks, and reasoning styles. But raw scale doesn’t equal training quality. This tutorial walks through a complete pipeline that starts by streaming just 8,000 shuffled rows from the Hugging Face Hub, enough to spot structural problems before committing GPU hours to a full run.

What jumps out immediately is the token length spread. The sample histogram shows heavy right-skew, which is typical for reasoning datasets: a long tail of examples with extended thought traces and relatively short final answers. The reasoning ratio — calculated as think characters divided by think-plus-answer characters — peaks around 0.8 to 0.9. Most of the content is reasoning, not the answer. That’s useful signal if you want a model that actually works through problems rather than pattern-matching to solutions.

Source distribution matters too. The top repos in the sample skew toward code and math-heavy datasets, with a heuristic classifier splitting rows into code, math, medical, MCQ/logic, and general buckets. Code examples are easy to spot — triple backticks and function definitions are dead giveaways. Math shows up through LaTeX markers like integrals and fractions. The heuristic approach isn’t perfect, but for a first-pass filter it’s surprisingly effective.

The real value here is the curation step before fine-tuning. Instead of dumping 5 million rows into SFTTrainer and hoping for the best, the pipeline filters out unsuitable examples, wraps retained samples in chat format with explicit tags, and applies LoRA to SmolLM2-135M-Instruct. That’s a 135-million-parameter model — small enough to fine-tune in Colab, but capable of learning structured reasoning patterns when the data is clean. The tag convention has become something of a de facto standard since DeepSeek-R1 popularized it, and forcing the model to generate reasoning before answers gives you a hook for structured inference later.

The export to Parquet at the end is the quietly important step. It means the curated dataset isn’t trapped in a notebook — it’s a reusable artifact you can feed into other training runs or share. For anyone experimenting with reasoning models on a budget, this pipeline removes the two biggest barriers: data access and compute cost.