Author: Ricardo

In this tutorial, we implement an end-to-end supervised fine-tuning pipeline for the XYZ-Aquila-SFT dataset, Hugging Face Transformers, PyTorch, and PEFT. We stream and examine the dataset, parse multi-turn tool-use trajectories, extract structured instrument calls, analyze corpus traits, and protect embedded reasoning and remark patterns. We then convert instrument schemas between message-embedded and structured codecs, render…

In this tutorial, we construct an end-to-end workflow for working with the SupraLabs reasoning corpus. We stream a consultant subset instantly from the Hugging Face Hub, examine its supply distribution, token-length patterns, activity composition, and reasoning-to-answer ratios, and then apply a sequence of high quality filters to take away unsuitable coaching examples. We rework the…