菜单
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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mkurman/zorai --skill peft命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| 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"] |
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.
uv pip install peft
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()
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()
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)