training-optimization

What problem does it solve?

This Skill provides a structured approach to optimizing LLM fine-tuning, helping teams achieve better model performance with efficient use of compute and data.

Core Features & Use Cases

• Learning rate optimization: Select schedulers, warmup, and values that promote stable convergence.
• LoRA configuration: Tune rank, alpha, and target modules to balance capacity and efficiency.
• Batch size, sequence length, and precision: Balance memory and throughput for different hardware.
• Hyperparameter tuning workflows: Grid search and Bayesian optimization to find strong defaults.
• Monitoring & reproducibility: Integrate with WandB/TensorBoard to compare runs and track experiments.
• Use Case: Fine-tune a 7B model on a budget GPU while achieving target eval metrics.

Quick Start

• Install dependencies: pip install transformers trl wandb accelerate
• Prepare a small dataset and a baseline model checkpoint
• Run a starter training script with recommended defaults (lr=2e-4, r=16, target_modules all, cosine warmup)