AgentSkillsCN

extract-hyperparameters

从论文中提取并记录模型的超参数配置，为训练任务的顺利开展奠定基础。

SKILL.md
--- frontmatter
name: extract-hyperparameters
description: "Identify and document model hyperparameters from papers. Use when setting up training configurations."
mcp_fallback: none
category: analysis
tier: 2

Extract Hyperparameters

Locate and document all hyperparameters mentioned in research papers including learning rates, batch sizes, and model configurations.

When to Use

  • •Reproducing paper results
  • •Setting up model training configurations
  • •Comparing hyperparameter choices across papers
  • •Planning hyperparameter tuning experiments

Quick Reference

bash
# Extract numeric values and parameters from papers
pdftotext paper.pdf - | grep -i "learning rate\|batch\|epochs\|weight decay\|dropout" | head -20

# Common pattern search
grep -E "\\b(lr|batch_size|epochs|momentum|dropout|layers)\\s*[=:]" config.py

Workflow

  1. •Find hyperparameter table: Look for "Table 1" or "Hyperparameters" section
  2. •Document architecture parameters: Layer sizes, activation functions, normalization
  3. •Extract training parameters: Learning rate, batch size, epochs, optimizers
  4. •Note regularization: Dropout, weight decay, batch normalization
  5. •Create configuration file: Translate to implementation format (YAML/JSON/Mojo)

Output Format

Hyperparameter documentation:

  • •Model architecture (layers, sizes, activations)
  • •Training parameters (LR, batch size, epochs)
  • •Optimizer configuration (type, momentum, decay)
  • •Regularization settings (dropout, L1/L2)
  • •Data preprocessing (normalization, augmentation)
  • •Hardware and precision (float32, float64)

References

  • •See prepare-dataset skill for data configuration
  • •See train-model skill for training implementation
  • •See /notes/review/mojo-ml-patterns.md for Mojo configuration patterns