507 citations · 676 across the 5 of their papers we have counts for
3 papers · 1 filter
QLoRA: Efficient Finetuning of Quantized LLMs
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman +1
We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning t…
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Tim Dettmers, Mike Lewis, Younes Belkada +1
Large language models have been widely adopted but require significant GPU memory for inference. We develop a procedure for Int8 matrix multiplication for feed-forward and attentio…
Training Transformers Together
Alexander Borzunov, Max Ryabinin, Tim Dettmers +5
The infrastructure necessary for training state-of-the-art models is becoming overly expensive, which makes training such models affordable only to large corporations and instituti…