peft-fine-tuning

You can fine-tune 7B to 70B models on limited hardware by applying QLoRA, which quantizes the base model to reduce memory footprint while training parameter-efficient adapters. This approach enables memory-constrained training on consumer GPUs without requiring datacenter-grade hardware.

LoRA, QLoRA, DoRA, and AdaLoRA are parameter-efficient fine-tuning methods that train small adapter weights rather than the full model. QLoRA adds quantization for lower memory usage, DoRA decomposes weight updates, and AdaLoRA dynamically allocates adapter capacity during training.

Multi-adapter workflows allow you to train, load, merge, switch, and compose adapters for different tasks without retraining the base model. You can ship separate lightweight adapters for support, code generation, and domain chat instead of maintaining multiple full checkpoints.

Out-of-memory errors and degraded inference quality during fine-tuning can stem from improper quantization setup, excessive adapter rank, or memory allocation issues. This Skill provides troubleshooting and optimization recipes to diagnose memory errors, adapter loading issues, and inference slowdowns with practical fixes.

Parameter-efficient fine-tuning supports instruction tuning, code generation, and conversational tuning workflows by applying LoRA, QLoRA, or Prefix Tuning adapters to a base model. The process includes saving, merging, loading, and validating adapters for reproducible deployment across various generation tasks.