model - Model Development and Fine-Tuning for AI Algorithms
Algorithm and model development skill for dataset design, fine-tuning, evaluation, and deployment
Tags
Updated: 2026-02-15Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Design datasets
- Clean datasets
- Perform supervised fine-tuning
- Implement preference optimization
- Use LoRA/QLoRA techniques
- Configure training parameters
- Execute offline evaluation
- Execute online evaluation
- Conduct safety checks
- Package deployments
- Balance cost/performance
Inputs
- Task definition
- Success metrics
- Base model
- License constraints
- GPU hardware
- Target inference stack
- Dataset
- Labeling guidelines
- Training configuration
- Evaluation set
Outputs
- Data specification
- Training plan
- Evaluation plan
- Deployment plan
- Model checkpoints
- Adapter weights
- Merged weights
- Deployment documentation
- Benchmark results
Requirements
- Base model and license
- GPU hardware
- Inference stack
- Experiment tracking system
- Deployment environment