How To Finetune GPT Like Large Language Models on a Custom Dataset

Lit-GPT, Lightning AI’s optimized open-source framework for large language model training and inference, supports models including LLaMA 2, Falcon, StableLM, Vicuna, and RedPajama and provides parameter-efficient finetuning methods such as LoRA and adapters. The tutorial demonstrates how to install Lit-GPT and its PyTorch and FlashAttention dependencies, download and convert RedPajama-INCITE 3B weights, and prepare Databricks’ Dolly 2.0 instruction dataset by mapping its context and response fields to the required input and output format. It then explains how to modify a preparation script to tokenize prompts, divide the data into training and validation sets, and save processed files before launching adapter-based finetuning. After training, the resulting adapter can be used with Lit-GPT’s generation script to test prompts, while model settings such as GPU count, batch size, and other hyperparameters can be adjusted for available hardware. The process illustrates that efficient finetuning can adapt an open-source LLM to specialized tasks, such as domain-specific question answering, potentially using only a single GPU.

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