| name | peft |
| description | Parameter-Efficient Fine-Tuning (PEFT) library. LoRA, QLoRA, AdaLoRA, IA3, Prefix Tuning, P-Tuning, Prompt Tuning. Fine-tune large models with minimal memory overhead. Hugging Face ecosystem integration. |
| tags | ["peft","lora","qlora","fine-tuning","llm","huggingface","parameter-efficient","zorai"] |
Overview
PEFT (Parameter-Efficient Fine-Tuning) adapts large pretrained models by training only a small subset of parameters. Supports LoRA, QLoRA, AdaLoRA, IA3, Prefix Tuning, P-Tuning, and Prompt Tuning. Reduces GPU memory by 4-16x compared to full fine-tuning.
Installation
uv pip install peft
LoRA
from transformers import AutoModelForCausalLM
from peft import LoraConfig, get_peft_model, TaskType
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
peft_config = LoraConfig(
r=16, lora_alpha=32,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
task_type=TaskType.CAUSAL_LM,
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
Save & Merge
model.save_pretrained("adapter")
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
merged = PeftModel.from_pretrained(base, "adapter").merge_and_unload()
QLoRA
from transformers import BitsAndBytesConfig
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct",
quantization_config=BitsAndBytesConfig(load_in_4bit=True), device_map="auto")
model = get_peft_model(model, peft_config)
References
