code for Scaling Laws for Language Transfer Learning
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Updated
Apr 18, 2021 - Python
code for Scaling Laws for Language Transfer Learning
Code for reproducing the experiments on large-scale pre-training and transfer learning for the paper "Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images" (https://arxiv.org/abs/2106.00116)
Code for CoNLL BabyLM workshop Mini Minds: Exploring Bebeshka and Zlata Baby Models
[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.
Reproducible scaling laws for contrastive language-image learning (https://arxiv.org/abs/2212.07143)
[ICML 2023] "Data Efficient Neural Scaling Law via Model Reusing" by Peihao Wang, Rameswar Panda, Zhangyang Wang
A toolkit for scaling law research ⚖
A method for calculating scaling laws for LLMs from publicly available models
Scaling laws web calculator to get a model's training compute flops, costs and energy utilization.
Dimensionless learning codes for our paper called "Data-driven discovery of dimensionless numbers and governing laws from scarce measurements".
🌹[ICML 2024] Selecting Large Language Model to Fine-tune via Rectified Scaling Law
Scaling Data-Constrained Language Models
First temporal graph foundation model dataset and benchmark
[NeurIPS'24 Spotlight] Observational Scaling Laws
[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.
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