We release all model generations with human and GPT-4 annotations to facilitate further study. We
open-source our codebase and CUDA kernels and integrate our methods into the Hugging Face
transformers stack [64], making them easily accessible to all. We release a collection of adapters
for 7/13/33/65B size models, trained on 8 different instruction following datasets, for a total of 32
different open sourced, finetuned models.

2

Figure 1: Different finetuning methods and their memory requirements. QLORA improves over LoRA by
quantizing the transformer model to 4-bit precision and using paged optimizers to handle memory spikes.
QLORA