Create a Reasoning-Focused LLM: A Practical Guide to Streaming, Curating, and Fine-Tuning the SupraLabs Reasoning Corpus
Small models can reason too. Here's how to build one with SupraLabs' corpus and LoRA.
Why it matters
A practical tutorial on fine-tuning compact reasoning models (135M parameters) using open datasets and efficient methods—relevant to practitioners building specialized models without massive compute budgets.
The key facts
11 to knowSmolLM2-135M-Instruct used as base model
SupraLabs reasoning corpus from Hugging Face
LoRA fine-tuning approach for efficiency
End-to-end pipeline: dataset curation, SFT, inference
Focus on quality filtering and heuristic data cleaning
Designed for resource-constrained training
SupraLabs reasoning corpus used as training data source
SmolLM2-135M-Instruct base model
End-to-end pipeline: streaming, curation, SFT, inference
Focus on small model reasoning capability without excessive compute
Data cleaning and heuristic filtering included in workflow
Go to the source
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Publisher excerpt: This tutorial provides a complete workflow for building a compact, reasoning-focused language model. By streaming the SupraLabs reasoning corpus from Hugging Face, we apply quality filters and curate data for Supervised Fine-Tuning (SFT). Using SmolLM2-135M-Instruct and LoRA, we demonstrate an…