Building Reasoning-Focused LLMs: A Guide to Data Curation and Fine-Tuning
A practical walkthrough on streaming, filtering, and fine-tuning the SupraLabs Reasoning Corpus with SmolLM2 and LoRA.
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- Convert dataset samples into a pandas DataFrame to inspect token lengths and source repositories.
- Apply heuristic rules to classify tasks into code, mathematics, medical, and logic categories.
- Configure LoRA adapters and SFTConfig parameters to fine-tune the SmolLM2 model via TRL.
- Evaluate training performance and export curated datasets for downstream experiments.
Developing a reasoning-focused Large Language Model requires a robust pipeline for data exploration, curation, and efficient training. This comprehensive guide outlines practical methods for working with the SupraLabs Reasoning Corpus, guiding developers from raw data streaming to parameter-efficient model adaptation.
As an initial step, developers convert the sampled dataset into a pandas DataFrame to analyze source repository distributions and token lengths. By calculating character counts for thought traces think_chars and answers answer_chars, the pipeline derives the reasoning-to-response ratio reason_ratio, visualized clearly through Matplotlib analytical charts.
Analyzing the balance between internal reasoning traces and final answers is vital for reasoning models. It ensures developers can monitor how efficiently smaller language models allocate computation toward logical deduction before generating a response, ultimately bridging the gap between compact models and heavy reasoning architectures.
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Beyond basic statistical exploration, the pipeline applies lightweight heuristic rules to automatically categorize dataset records into distinct task types:
- Code tasks identified by programming syntax and function definitions.
- Mathematics tasks triggered by equations, integrals, and theorems.
- Medical tasks detected through clinical terminology and patient symptoms.
- Multiple-choice and logic tasks flagged by standardized option formats.
- General tasks covering all remaining unclassified examples.
Following data preparation, the workflow initializes the SmolLM2 causal language model and configures LoRA adapters with parameters such as r=16, lora_alpha=32, and lora_dropout=0.05. Training is managed through TRL's SFTConfig, utilizing a maximum sequence length of 2,048 tokens, a batch size of 2, and a learning rate of 2e-4, achieving completion in approximately 10 to 20 minutes on a T4 GPU.
Source: MarkTechPost
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