LoRA

A comprehensive tutorial has been released detailing an end-to-end supervised fine-tuning (SFT) pipeline designed to enable large language models (LLMs) to effectively call external tools. This process leverages the XYZ-Aquila-SFT dataset, the Hugging Face Transformers library, PyTorch, and the Parameter-Efficient…

AllenAI has released a tutorial detailing an end-to-end post-training pipeline for a compact instruction-tuned language model, utilizing its Open Instruct framework. This tutorial guides users through three primary training stages: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning…

This tutorial presents a comprehensive sentiment analysis workflow utilizing the Stanford NLP IMDb Large Movie Review Dataset, contrasting traditional machine learning approaches with parameter-efficient transformer fine-tuning. The process begins with establishing a reproducible computational environment and conducting a thorough audit…

An end-to-end tutorial released this week details how to fine-tune the Qwen3-0.6B language model using NVIDIA NeMo AutoModel and LoRA (Low-Rank Adaptation) within a Google Colab environment, leveraging a single GPU.

A new tutorial demonstrates an end-to-end workflow for training Google's Gemma-3 language model to perform structured mathematical reasoning on GSM8K math problems. The process utilizes the Tunix framework, JAX, LoRA adapters for efficient training, and custom reward functions to guide…