教程用SupraLabs语料库+SmolLM2+LoRA，教你在小显卡上微调出推理模型，比追大模型省资源。
本教程演示了从Hugging Face流式获取SupraLabs推理语料库，并完成质量过滤与数据策展。以SmolLM2-135M-Instruct为基座、LoRA为微调方法，进行监督微调（SFT）并跑通端到端流程。该方案在135M参数规模下验证，无需过多GPU资源即可打造专用推理模型。
Create a Reasoning-Focused LLM: A Practical Guide to Streaming, Curating, and Fine-Tuning the SupraLabs Reasoning Corpus
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 end-to-end pipeline—from dataset analysis and heuristic cleaning to efficient training and inference—enabling the development of specialized small models without excessive resource requirements The post Create a Reasoning-Focused LLM: A Practical Guide to Streaming, Curating, and Fine-Tuning the SupraLabs Reasoning Corpus appeared first on MarkTechPost .