LLM fine-tuning using Axolotl

Fine-tuning LLMs is a time-consuming and resource-intensive task. While it unlocks the power of these models for specific domains and applications, the process can be complex and require significant computational resources. There's good news, though! Multiple approaches and tools exist to streamline and optimize LLM fine-tuning. Fine-tuning often involves multiple rounds of training with domain-specific data, validation on separate sets, and hyperparameter tuning for optimal performance. The size and complexity of the data, along with the target task's similarity to the pre-trained model's training data, all significantly impact the time and resources needed. Solution? Cloud-based Training Platforms: Platforms like RunPod offer access to powerful cloud GPUs, accelerating the training process. Fine-tuning Frameworks: Frameworks like Axolotl provide functionalities specifically designed for LLM fine-tuning. These tools often support techniques like LoRA (Low-Rank Adaptation), and qLORA which enable parameter-efficient fine-tuning, reducing training time and resource consumption. We will take a look at how to fine-tune LLMs using Axolotl in Runpod