In this tutorial, we build an end-to-end workflow for working with the SupraLabs reasoning corpus. We stream a representative subset directly from the Hugging Face Hub, inspect its source distribution, token-length patterns, task composition, and reasoning-to-answer ratios, and then apply a series of quality filters to remove unsuitable training examples. We transform the retained samples into a chat-based supervised fine-tuning format with explicit <think> reasoning tags and use them to adapt SmolLM2-135M-Instruct with LoRA through TRL’s SFTTrainer. By combining scalable data access, exploratory analysis, dataset curation, parameter-efficient fine-tuning, structured inference, and Parquet export, we create a complete Google Colab pipeline for turning a large multi-model reasoning corpus into a compact reasoning-focused language model.
import subprocess, sys
def pip_install(pkgs):
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", *pkgs])
subprocess.call([sys.executable, "-m", "pip", "uninstall", "-y", "-q", "torchao"])
