refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-peft-parameter-efficient-fine-tu
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title: PEFT (Parameter-Efficient Fine-Tuning)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [PEFT, Parameter-Efficient Fine-Tuning, LoRA fine-tuning]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.95
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verification_status: applied
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tags: [peft, lora, qlora, fine-tuning, llm]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: peft, transformers, bitsandbytes
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---
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# PEFT (Parameter-Efficient Fine-Tuning)
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## 매 한 줄
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> **"매 frozen base + tiny trainable delta"**. 매 full fine-tuning 의 ~0.1-1% parameter 만 학습. 2026 standard: LoRA / QLoRA — 매 70B model 도 single 24GB GPU 에서 fine-tune 가능. HuggingFace `peft` library 의 사실상 표준.
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## 매 핵심
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### 매 동기
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- Full FT: 70B model = 280GB (fp32) gradient + optimizer state → multi-A100 cluster 필요.
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- Storage: 매 task 마다 full checkpoint 저장 시 비용 폭발.
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- Catastrophic forgetting: 매 full FT 가 base capability 손상.
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- PEFT: 매 base frozen, delta 만 학습 → 1 base + N tiny adapters.
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### 매 family
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- **LoRA** (Hu et al. 2021): low-rank decomposition `ΔW = BA`, rank r=4-64.
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- **QLoRA** (Dettmers et al. 2023): 4-bit NF4 quantized base + LoRA adapters.
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- **Prefix Tuning** (Li & Liang 2021): learnable prefix tokens prepended to keys/values.
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- **Prompt Tuning** (Lester et al. 2021): learnable soft prompts at input.
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- **IA³** (Liu et al. 2022): scale activations via learned vectors (multiply, not add).
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- **Adapters** (Houlsby et al. 2019): small bottleneck MLPs inserted between layers.
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- **DoRA** (2024): magnitude + direction decomposition, LoRA 보다 우수.
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### 매 LoRA 수학
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- `W' = W + αBA/r` where `B ∈ R^{d×r}`, `A ∈ R^{r×k}`, r ≪ min(d,k).
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- Trainable: `2dr` params instead of `dk`. 매 d=k=4096, r=8 → 65k vs 16M (250× 감소).
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- Inference: 매 merge `W ← W + αBA/r` → zero overhead.
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### 매 응용
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1. Domain adaptation (legal, medical LLM).
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2. Instruction tuning (Alpaca-style).
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3. Style transfer (FLUX LoRA for art style).
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4. Multi-tenant serving (1 base + N customer LoRAs).
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## 💻 패턴
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### LoRA with peft library
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```python
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from peft import LoraConfig, get_peft_model, TaskType
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from transformers import AutoModelForCausalLM
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base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")
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config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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r=16, lora_alpha=32, lora_dropout=0.05,
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
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)
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model = get_peft_model(base, config)
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model.print_trainable_parameters() # ~0.5% trainable
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```
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### QLoRA (4-bit base + LoRA)
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```python
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from transformers import BitsAndBytesConfig
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import torch
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bnb = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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)
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base = AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-3.1-70B", quantization_config=bnb, device_map="auto",
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)
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model = get_peft_model(base, lora_config) # 70B on single 48GB GPU
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```
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### Save / load adapter only
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```python
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model.save_pretrained("./my-lora") # ~50MB, not 140GB
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from peft import PeftModel
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loaded = PeftModel.from_pretrained(base, "./my-lora")
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```
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### Merge for inference
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```python
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merged = model.merge_and_unload() # W ← W + αBA/r
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merged.save_pretrained("./merged-model") # standard HF model, no peft dep
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```
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### Multi-LoRA serving (vLLM)
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```python
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# vLLM 0.6+ supports dynamic LoRA loading
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from vllm import LLM, SamplingParams
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from vllm.lora.request import LoRARequest
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llm = LLM(model="meta-llama/Llama-3.1-8B", enable_lora=True, max_loras=8)
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out = llm.generate(prompts, sampling_params,
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lora_request=LoRARequest("customer-42", 1, "./customer-42-lora"))
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```
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### DoRA (2024)
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```python
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config = LoraConfig(r=16, lora_alpha=32, use_dora=True, # peft >= 0.10
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target_modules=["q_proj", "v_proj"])
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```
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### Prompt tuning
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```python
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from peft import PromptTuningConfig, PromptTuningInit
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config = PromptTuningConfig(
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task_type=TaskType.CAUSAL_LM,
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prompt_tuning_init=PromptTuningInit.TEXT,
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num_virtual_tokens=20,
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prompt_tuning_init_text="Classify sentiment:",
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tokenizer_name_or_path="meta-llama/Llama-3.1-8B",
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)
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| 1 GPU, large base (70B) | QLoRA |
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| Multi-task, single base | LoRA + multi-adapter serving |
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| Tiny VRAM, frozen base OK | Prompt tuning |
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| Best quality, less compute saving | DoRA |
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| Diffusion model style | LoRA (rank 4-32) |
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| Production accuracy critical | Full FT (if 가능) |
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**기본값**: QLoRA (4-bit NF4 + r=16 LoRA on q/k/v/o projections).
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## 🔗 Graph
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- 부모: [[Fine-Tuning]]
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- 변형: [[LoRA]] · [[QLoRA]] · [[DoRA]]
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- 응용: [[Fine-tuning|Instruction-Tuning]] · [[Domain-Adaptation]]
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- Adjacent: [[LLM_Optimization_and_Deployment_Strategies|Quantization]] · [[RLHF]]
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## 🤖 LLM 활용
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**언제**: 매 single GPU 에서 large model fine-tune, multi-tenant LoRA serving, rapid task iteration.
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**언제 X**: 매 base model 의 fundamental capability 변경 필요 (continued pretraining → full FT or full pretraining).
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## ❌ 안티패턴
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- **Rank too low**: r=1-2 → underfitting. 매 r=8-32 starting point.
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- **Wrong target modules**: only `q_proj`/`v_proj` skip → degraded. 매 all attention + MLP modules 가 best.
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- **Forgetting alpha**: 매 alpha=2r convention 무시 → unstable training.
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- **Saving full model**: `model.save_pretrained()` on PeftModel 만 saves adapter. Don't merge unnecessarily.
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- **QLoRA + bf16 base**: 매 NF4 quantization 의 redundant. 매 fp16 or bf16 base 둘 중 하나.
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## 🧪 검증 / 중복
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- Verified (HuggingFace `peft` docs, Hu et al. 2021 LoRA, Dettmers et al. 2023 QLoRA).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — PEFT family, LoRA/QLoRA patterns, decision matrix |
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