Language Models


Introduced by Zhang et al. in OPT: Open Pre-trained Transformer Language Models

OPT is a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters. The model uses an AdamW optimizer and weight decay of 0.1. It follows a linear learning rate schedule, warming up from 0 to the maximum learning rate over the first 2000 steps in OPT-175B, or over 375M tokens in the smaller models, and decaying down to 10% of the maximum LR over 300B tokens. The batch sizes range from 0.5M to 4M depending on the model size and is kept constant throughout the course of training.

Source: OPT: Open Pre-trained Transformer Language Models


Paper Code Results Date Stars


Task Papers Share
Language Modelling 22 12.57%
Question Answering 11 6.29%
Quantization 7 4.00%
Large Language Model 7 4.00%
Machine Translation 5 2.86%
Text Generation 4 2.29%
Object Detection 4 2.29%
Instruction Following 3 1.71%
Retrieval 3 1.71%


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign