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Found 3 papers, 2 papers with code
Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates
Benchmarking the tradeoff between neural network accuracy and training time is computationally expensive.
LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms
We find that subsets of 1k-6k instruction finetuning samples are sufficient to achieve good performance on both (1) traditional NLP benchmarks and (2) model-based evaluation.
MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining
Here, we introduce MosaicBERT, a BERT-style encoder architecture and training recipe that is empirically optimized for fast pretraining.
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