model-optimization
CommunityTune models for speed, size, and accuracy.
Authorpluginagentmarketplace
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Quantization, pruning, AutoML, and hyperparameter tuning are used to improve model performance, reduce size, and enable automated ML workflows.
Core Features & Use Cases
- Hyperparameter tuning and AutoML to find optimal models efficiently.
- Model compression and performance optimization for faster inference in production.
- Use cases include deploying lean, accurate models in constrained environments.
Quick Start
Run the Optuna-based optimizer on your dataset to identify the best hyperparameters for your model.
Dependency Matrix
Required Modules
optunanumpyscikit-learn
Components
scriptsreferencesassets
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: model-optimization Download link: https://github.com/pluginagentmarketplace/custom-plugin-ai-data-scientist/archive/main.zip#model-optimization Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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