2nd/10_Wiki/Topics/AI_and_ML/모델 매개변수 제어 (Model Parameter Control).md

---
id: wiki-2026-0508-모델-매개변수-제어-model-parameter-contr
title: 모델 매개변수 제어 (Model Parameter Control)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Model Parameter Control, Inference Parameters, Sampling Parameters]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [parameters, sampling, inference, llm, image-gen]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: openai-anthropic-vllm-comfyui
---

# 모델 매개변수 제어 (Model Parameter Control)

## 매 한 줄
> **"매 parameter 는 model behavior 의 dial — temperature, top_p, top_k, seed 가 매 generation 의 character 를 결정"**. 2026년 production LLM 의 매 endpoint 가 노출하는 sampling knobs (Anthropic, OpenAI, vLLM, Ollama) + image gen 의 cfg/steps/scheduler — 매 정량적 control 의 핵심.

## 매 핵심

### 매 LLM sampling parameters
- **temperature** [0, 2]: logit scaling. 0 = greedy, 1 = raw distribution, >1 = flatten. 매 deterministic task 는 0, creative 는 0.7~1.0.
- **top_p** (nucleus) [0, 1]: cumulative prob mass. 0.9 = 매 top-90% mass tokens 만 sample.
- **top_k**: 매 top-K logits 만 유지. vLLM 은 -1 = disabled.
- **min_p** [0, 1]: relative threshold (vs top token prob). 매 modern alternative to top_p.
- **frequency_penalty** [-2, 2] / **presence_penalty**: repetition control.
- **seed**: reproducibility. 매 same seed + temperature=0 → deterministic (대부분).
- **stop**: 매 stop strings. 매 agent loop 의 turn boundary 제어.
- **max_tokens** / **max_completion_tokens**: output budget.

### 매 image gen parameters (FLUX, SD3.5, Midjourney)
- **cfg / guidance_scale**: prompt adherence vs creativity. FLUX 3.5~5.0, SD 5~9.
- **steps**: denoising steps. FLUX-dev 28, FLUX-schnell 4, SD3.5 28~40.
- **scheduler / sampler**: euler, dpmpp_2m, etc. 매 quality/speed tradeoff.
- **seed**: 매 reproducible composition.
- **denoising_strength** (img2img): 0 = identical, 1 = ignore source.

### 매 응용
1. RAG answer extraction → temperature=0, top_p=1.
2. Brainstorm → temperature=0.9, presence_penalty=0.6.
3. Code completion → temperature=0.2, stop=["\n\n"].
4. Image variation → 매 seed fix + cfg lower.

## 💻 패턴

### Anthropic Claude — deterministic extraction
```python
from anthropic import Anthropic

client = Anthropic()
resp = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=1024,
    temperature=0.0,  # deterministic
    top_p=1.0,
    system="Extract structured data. Output JSON only.",
    messages=[{"role": "user", "content": doc_text}],
)
```

### OpenAI GPT-5 — creative writing knobs
```python
from openai import OpenAI

client = OpenAI()
resp = client.chat.completions.create(
    model="gpt-5",
    temperature=0.9,
    top_p=0.95,
    presence_penalty=0.6,
    frequency_penalty=0.3,
    max_completion_tokens=2000,
    seed=42,  # best-effort reproducibility
    messages=[{"role": "user", "content": "Write a noir opening."}],
)
```

### vLLM — full sampling control (self-host Llama 3.3)
```python
from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-3.3-70B-Instruct", tensor_parallel_size=4)

params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    top_k=50,
    min_p=0.05,           # modern alternative
    repetition_penalty=1.1,
    max_tokens=512,
    stop=["</answer>"],
    seed=2026,
    logprobs=5,           # debugging
)
outputs = llm.generate(["Explain mixture-of-experts."], params)
```

### MLX (Apple Silicon) — local inference with seed
```python
from mlx_lm import load, generate
import mlx.core as mx

model, tok = load("mlx-community/Llama-3.3-70B-Instruct-4bit")
mx.random.seed(42)
text = generate(
    model, tok,
    prompt="Summarize:",
    max_tokens=256,
    temp=0.3,
    top_p=0.9,
    verbose=False,
)
```

### FLUX.1-dev via diffusers — image gen knobs
```python
import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16,
).to("cuda")

img = pipe(
    prompt="cinematic neo-tokyo alley, neon, rain",
    guidance_scale=3.5,           # FLUX prefers low CFG
    num_inference_steps=28,
    generator=torch.Generator("cuda").manual_seed(42),
    width=1024, height=1024,
).images[0]
```

### ComfyUI API — programmatic SD3.5 with full control
```python
import json, requests

workflow = {
    "sampler": {
        "class_type": "KSampler",
        "inputs": {
            "seed": 42, "steps": 30, "cfg": 7.0,
            "sampler_name": "dpmpp_2m", "scheduler": "karras",
            "denoise": 1.0,
            "model": ["loader", 0],
            "positive": ["pos_clip", 0],
            "negative": ["neg_clip", 0],
            "latent_image": ["empty_latent", 0],
        },
    },
    # ... rest of graph
}
r = requests.post("http://localhost:8188/prompt", json={"prompt": workflow})
```

### Sweep parameters with Optuna for prompt+param tuning
```python
import optuna
from anthropic import Anthropic

client = Anthropic()
EVAL_SET = load_eval()  # list[(prompt, expected)]

def objective(trial):
    temp = trial.suggest_float("temperature", 0.0, 1.2)
    tp   = trial.suggest_float("top_p", 0.5, 1.0)
    score = 0
    for q, exp in EVAL_SET:
        out = client.messages.create(
            model="claude-opus-4-7",
            max_tokens=512, temperature=temp, top_p=tp,
            messages=[{"role": "user", "content": q}],
        ).content[0].text
        score += grade(out, exp)
    return score / len(EVAL_SET)

study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=40)
print(study.best_params)
```

## 매 결정 기준
| Task | temperature | top_p | 기타 |
|---|---|---|---|
| Extraction / classification | 0.0 | 1.0 | seed 고정 |
| Code completion | 0.2 | 0.95 | stop tokens |
| Summarization | 0.3 | 0.9 | — |
| Q&A (RAG) | 0.0~0.3 | 1.0 | — |
| Brainstorming | 0.8~1.0 | 0.95 | presence_penalty 0.6 |
| Creative fiction | 0.9~1.1 | 0.95 | frequency_penalty 0.3 |
| FLUX image | cfg 3.5 | steps 28 | bf16 |
| SD3.5 image | cfg 7.0 | steps 30 | dpmpp_2m karras |

**기본값**: temperature=0.7, top_p=0.9, seed=42 (debugging), max_tokens=task-budgeted.

## 🔗 Graph
- 부모: [[Parameter]] · [[LLM_Inference]]
- 변형: [[Sampling_Strategies]] · [[Speculative_Decoding]]
- 응용: [[Iterative Prompting]] · [[Midjourney]] · [[RAG]]
- Adjacent: [[Prompt_Engineering]] · [[Eval_Harness]]

## 🤖 LLM 활용
**언제**: 매 deterministic 결과 필요 (RAG, extraction) — temp=0. 매 creative output — temp 0.7+. 매 reproduce bug — seed 고정.
**언제 X**: 매 model 마다 seed 의 strict determinism 보장 X (특히 multi-GPU). 매 production 에서 seed 의존 X.

## ❌ 안티패턴
- **temperature=0 + top_p<1**: 매 redundant (greedy 가 이미 top-1).
- **temperature 1.5+ in production**: 매 hallucination/incoherence spike.
- **seed 만 고정 + temperature 0.7**: 매 batched inference 에서 비결정적.
- **max_tokens=4096 default**: 매 cost blowup. Task-budgeted.
- **frequency_penalty 1.5+**: 매 vocabulary collapse.

## 🧪 검증 / 중복
- Verified (Anthropic Messages API, OpenAI Chat Completions, vLLM SamplingParams, diffusers FluxPipeline, Stability SD3.5 docs, ComfyUI API).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — LLM/image sampling params + 7 working patterns |