peft-fine-tuning

# PEFT (Parameter-Efficient Fine-Tuning) Fine-tune LLMs by training <1% of parameters using LoRA, QLoRA, and 25+ adapter methods. ## When to use PEFT **Use PEFT/LoRA when:** - Fine-tuning 7B-70B models on consumer GPUs (RTX 4090, A100) - Need to train <1% parameters (6MB adapters vs 14GB full model) - Want fast iteration with multiple task-specific adapters - Deploying multiple fine-tuned variants from one base model **Use QLoRA (PEFT + quantization) when:** - Fine-tuning 70B models on single 24GB GPU - Memory is the primary constraint - Can accept ~5% quality trade-off vs full fine-tuning **Use full fine-tuning instead when:** - Training small models (<1B parameters) - Need maximum quality and have compute budget - Significant domain shift requires updating all weights ## Quick start ### Installation ```bash # Basic installation pip install peft # With quantization support (recommended) pip install peft bitsandbytes # Full stack pip install peft transformers accelerate bitsandbytes datasets ``` ### LoRA fine-tuning (standard) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer from peft import get_peft_model, LoraConfig, TaskType from datasets import load_dataset # Load base model model_name = "meta-llama/Llama-3.1-8B" model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.pad_token = tokenizer.eos_token # LoRA ...