507 citations · 1.1k across the 17 of their papers we have counts for
6 papers · 1 filter
The case for 4-bit precision: k-bit Inference Scaling Laws
Tim Dettmers, Luke Zettlemoyer
Quantization methods reduce the number of bits required to represent each parameter in a model, trading accuracy for smaller memory footprints and inference latencies. However, the…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Petals: Collaborative Inference and Fine-tuning of Large Models
Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers +5
Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion parameters. With the release of BLOOM-176B and OPT-175B, everyone can download…
Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models
Margaret Li, Suchin Gururangan, Tim Dettmers +4
We present Branch-Train-Merge (BTM), a communication-efficient algorithm for embarrassingly parallel training of large language models (LLMs). We show it is possible to independent…
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…