This article provides a comprehensive guide on fine-tuning the Llama 3.1 language model using Unsloth for efficient parameter-efficient training. It covers concepts like supervised fine-tuning, LoRA, QLoRA, and practical steps for training on a high-quality dataset.
"The paper introduces a technique called LoReFT (Low-rank Linear Subspace ReFT). Similar to LoRA (Low Rank Adaptation), it uses low-rank approximations to intervene on hidden representations. It shows that linear subspaces contain rich semantics that can be manipulated to steer model behaviors."
