llm-fine-tuning-guide

Fine-tune large language models using parameter-efficient techniques like LoRA and QLoRA through the provided scripts. The lora_fine_tuning.py script initiates training by adapting model weights efficiently while significantly reducing computational overhead.

Full fine-tuning updates all model weights, requiring substantial memory, whereas PEFT methods like LoRA and QLoRA train only incremental adapters. This Skill provides practical examples covering both approaches to help optimize LLM deployment costs.

Prepare datasets for instruction tuning using included utilities for validation, augmentation, and splitting. This Skill leverages libraries like nlpaug and datasets to ensure your training data is properly structured and optimized for specialized domain adaptation.

Evaluate model performance using provided methods for calculating perplexity and task-specific metrics. This Skill integrates scikit-learn to measure how well your fine-tuned LLM understands and generates specialized content for your target domain.

Adapt transformers models for specialized domains like legal jargon using instruction fine-tuning techniques. This Skill addresses domain specialization by guiding you through task-specific performance enhancement for accurate internal document analysis.

Running the fine-tuning scripts requires installing torch, transformers, peft, datasets, scikit-learn, and nlpaug. These dependencies provide the foundational environment for training, evaluating, and augmenting your large language models.