Research
65 updates on Research.
RWKV trains like a transformer and runs with constant memory per token
The paper releases pretrained RNN weights from 169M to 14B parameters and reports constant time and memory per token during inference.
LaMini-LM distils models from 61M parameters up on 2.58M instructions
MBZUAI distilled 17 instruction models from 61M to 7B parameters on 2.58M synthetic instructions, and reports the 248M model matching LLaMA-7B.
MobileBERT runs in 62 ms on a Pixel 4 with 25.3M parameters
Carnegie Mellon and Google Brain distilled BERT into a 25.3M-parameter model that answers in 62 ms on a Pixel 4 and scores 77.7 on GLUE.
Hugging Face's DistilBERT is 40% smaller than BERT and 71% faster on an iPhone
The 66M-parameter distilled BERT keeps 97% of its teacher on GLUE and weighs 207 MB inside a question-answering app on an iPhone 7 Plus.
TinyBERT shrinks BERT-base to 14.5M parameters and runs 9.4x faster
Huawei's distilled BERT keeps more than 96.8% of its teacher's GLUE score at 14.5M parameters, 7.5x smaller and 9.4x faster on inference.