Create a Reasoning-Focused LLM: A Practical Guide to Streaming, Curating, and Fine-Tuning the SupraLabs Reasoning Corpus

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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.

Copy CodeCopiedUse a different Browserimport 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"]) pip_install([ "datasets>=3.0.0", "transformers>=4.46.0", "trl>=0.12.0", "peft>=0.13.0", "accelerate>=1.0.0", "bitsandbytes", "matplotlib", "pandas", ]) import os, re, json, math, random, itertools, warnings import pandas as pd import matplotlib.pyplot as plt import torch from collections import Counter from datasets import load_dataset, Dataset warnings.filterwarnings("ignore") random.seed(42) torch.manual_seed(42) DEVICE = "cuda" if torch.cuda.is_available() else "cpu" print(f"Device: …

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