How does this skill help with training efficiency?

By identifying your model's specific scaling exponent, you can predict exactly how much more data or compute is required to reach a target performance level, preventing wasted resources.

What data is required to run a scaling analysis?

You need to define a scaling dimension (size, data, or compute) and run at least four experiments spanning 1-2 orders of magnitude to get a reliable power law fit.

Can I compare my results to established papers?

Yes, the skill is designed to help you compare your empirical alpha values against famous benchmarks such as Kaplan (OpenAI) and Chinchilla (DeepMind).

What are scaling laws in machine learning?

Scaling laws are empirical relationships that describe how a model's error or loss decreases as the model size, training dataset size, or total compute budget increases.

Scaling Analysis & Law Investigation

Name: Scaling Analysis & Law Investigation
Author: rHedBull

byrHedBull

データサイエンスとML

Analyzes and predicts AI model performance by empirically testing relationships between model size, dataset volume, and compute budget.

概要

The Scaling Analysis skill provides a rigorous framework for conducting machine learning experiments to determine how model performance scales with varying inputs. It guides developers through the process of designing log-spaced scale points, configuring standardized training runs, and analyzing results using power law fits. By calculating specific scaling exponents and comparing them against established benchmarks like Kaplan or Chinchilla, this skill empowers researchers and engineers to make informed decisions about model architecture, training resource allocation, and performance extrapolation.

主な機能

Power law fitting and scaling exponent calculation
0 GitHub stars
Multidimensional scaling design for model size, data, and compute
Standardized run configuration to ensure experimental integrity
Performance extrapolation and comparison to industry benchmarks
Automated log-space point generation for empirical testing

ユースケース

Predicting the performance impact of increasing training data volume
Benchmarking new model architectures against established scaling laws
Determining the compute-optimal model size for a specific budget

概要

主な機能

Power law fitting and scaling exponent calculation
0 GitHub stars
Multidimensional scaling design for model size, data, and compute
Standardized run configuration to ensure experimental integrity
Performance extrapolation and comparison to industry benchmarks
Automated log-space point generation for empirical testing

ユースケース

Predicting the performance impact of increasing training data volume
Benchmarking new model architectures against established scaling laws
Determining the compute-optimal model size for a specific budget