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mncai
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Model Details
Model Provider
mncai
Model Tree
google/gemma-4-E4B-it
Input Modalities
Output Modalities
Supported Functionality
| 모델 | Korean Safety | English Safety | Attack Detection | Custom Policy | Long Context | Average |
|---|---|---|---|---|---|---|
| GenGuard 1.0 Light | 87.3 | 79.9 | 93.7 | 72.5 | 88.3 | 84.3 |
| gpt-oss-safeguard-20b | 85.7 | 76.7 | 78.0 | 72.7 | 82.6 | 79.1 |
| Qwen3Guard-Gen-8B | 82.8 | 78.0 | 91.8 | 31.6 | 83.6 | 73.6 |
| Shieldstral-1.0-3B | 85.5 | 80.3 | 87.5 | 39.6 | 69.1 | 72.4 |
| Nemotron-3.5-Content-Safety | 81.5 | 76.7 | 81.7 | 51.6 | 64.2 | 71.1 |
| Kanana-Safeguard-8B* | 78.8 | 74.9 | 70.7 | 26.3 | — | 62.7 |
Korean Safety (F1)
| 벤치 | GenGuard 1.0 Light | gpt-oss-safeguard-20b | Nemotron-3.5-Content-Safety | Qwen3Guard-Gen-8B | Shieldstral-1.0-3B | Kanana-Safeguard-8B |
|---|---|---|---|---|---|---|
| RTP-LX (subset ko) | 94.3 | 92.0 | 90.2 | 83.8 | 88.2 | 90.2 |
| wildguardmix-test-ko | 85.4 | 84.2 | 79.6 | 84.8 | 85.4 | 73.6 |
| XSTest_ko | 82.4 | 80.8 | 74.6 | 79.9 | 82.9 | 72.8 |
English Safety (F1)
| 벤치 | GenGuard 1.0 Light | gpt-oss-safeguard-20b | Nemotron-3.5-Content-Safety | Qwen3Guard-Gen-8B | Shieldstral-1.0-3B | Kanana-Safeguard-8B |
|---|---|---|---|---|---|---|
| OpenAI Moderation | 81.6 | 82.2 | 75.3 | 68.4 | 78.7 | 72.0 |
| ToxicChat | 76.6 | 66.5 | 72.5 | 68.3 | 77.6 | 66.9 |
| BeaverTails | 84.2 | 79.6 | 83.0 | 86.5 | 85.7 | 82.8 |
| PKU-SafeRLHF | 63.2 | 60.5 | 61.6 | 70.9 | 66.7 | 67.5 |
| WildGuardTest | 87.1 | 85.0 | 82.8 | 86.7 | 86.8 | 78.5 |
| HarmBench | 86.9 | 86.7 | 84.8 | 87.1 | 86.6 | 82.0 |
Attack Detection (차단율)
| 벤치 | GenGuard 1.0 Light | gpt-oss-safeguard-20b | Nemotron-3.5-Content-Safety | Qwen3Guard-Gen-8B | Shieldstral-1.0-3B | Kanana-Safeguard-8B |
|---|---|---|---|---|---|---|
| WildJailbreak (eval) | 96.7 | 88.0 | 77.3 | 95.1 | 94.9 | 83.0 |
| wildjailbreak-eval-ko | 93.5 | 83.2 | 81.8 | 93.6 | 91.2 | 62.2 |
| MultiJail (subset ko) | 91.4 | 74.3 | 92.4 | 93.7 | 88.9 | 92.4 |
| BIPIA | 67.1 | 3.5◇ | 18.6 | 20.7◇ | 4.1◇ | 9.1◇ |
| Private Red-Team Bench | 92.9 | 66.5 | 75.4 | 85.0 | 75.2 | 45.1 |
Custom Policy (F1)
| 벤치 | GenGuard 1.0 Light | gpt-oss-safeguard-20b | Nemotron-3.5-Content-Safety | Qwen3Guard-Gen-8B | Shieldstral-1.0-3B | Kanana-Safeguard-8B |
|---|---|---|---|---|---|---|
| KSPG-Bench | 80.1 | 81.3 | 42.3 | 6.2‡ | 18.1 | 9.2‡ |
| CoSApien-ko | 89.3 | 86.4 | 81.4 | 77.5‡ | 78.7 | 67.0‡ |
| PoFBench | 62.4 | 68.0 | 48.6 | 27.2‡ | 27.3 | 23.7‡ |
| BARRED | 58.1 | 55.2 | 34.1 | 15.3‡ | 34.2 | 5.4‡ |
Long Context (F1)
| 벤치 | GenGuard 1.0 Light | gpt-oss-safeguard-20b | Nemotron-3.5-Content-Safety | Qwen3Guard-Gen-8B | Shieldstral-1.0-3B | Kanana-Safeguard-8B |
|---|---|---|---|---|---|---|
| SafetyNIAH | 88.3 | 82.6 | 64.2 | 83.6 | 69.1 | — |
| 모델 | 한 건 응답 시간 중앙값(ms) | 초당 처리 건수, 32건 동시 |
|---|---|---|
| GenGuard 1.0 Light | 40 | 59 |
| gpt-oss-safeguard-20b | 587 | 6.5 |
| Qwen3Guard-Gen-8B | 71 | 37 |
| Nemotron-3.5-Content-Safety | 72 | 61 |
| Shieldstral-1.0-3B | 14 | 141 |
| Kanana-Safeguard-8B | 20 | 68 |
| 항목 | 내용 |
|---|---|
| 베이스 모델 | google/gemma-4-E4B-it |
| 파라미터 | 8B |
| 지원 언어 | 한국어, 영어 |
| 최대 입력 길이 | 131,072 토큰 |
user 메시지는 사용자 입력으로, assistant 메시지는 모델 응답으로 판정합니다. 문서나 검색 결과 등 대화 형식이 아닌 텍스트는 단일 user 메시지로 입력합니다. 지원하는 메시지 역할은 user와 assistant입니다.
text
Decision: block ← 항상 출력 (block 또는 allow)Response Refusal: refusal ← (옵션) request_refusal="/refusal" 일 때만Safety Categories: S3, S9 ← (옵션) request_categories="/categories" 일 때만| 번호 | 범주 | 번호 | 범주 |
|---|---|---|---|
| S1 | Controlled/Regulated Substances | S13 | Needs Caution |
| S2 | Copyright/Trademark/Plagiarism | S14 | Other |
| S3 | Criminal Planning/Confessions | S15 | PII/Privacy |
| S4 | Fraud/Deception | S16 | Political/Misinformation/Conspiracy |
| S5 | Guns and Illegal Weapons | S17 | Profanity |
| S6 | Harassment | S18 | Sexual |
| S7 | Hate/Identity Hate | S19 | Sexual (minor) |
| S8 | High Risk Gov Decision Making | S20 | Suicide and Self Harm |
| S9 | Illegal Activity | S21 | Threat |
| S10 | Immoral/Unethical | S22 | Unauthorized Advice |
| S11 | Malware | S23 | Violence |
| S12 | Manipulation |
판정은 생성된 텍스트 대신 확률로 내는 것을 권장합니다. 프롬프트 뒤에 Decision:을 붙이고 다음 토큰의 allow · block 확률만 보아, 차단 확률 = P(block) / (P(allow) + P(block)) ≥ 0.5 이면 차단합니다. 기준값은 서비스에 맞게 조절할 수 있습니다.
temperature=0(transformers는 do_sample=False)으로 생성하세요. 설정 파일의 기본값이 샘플링이라 지정하지 않으면 판정이 흔들릴 수 있습니다.python
import mathfrom transformers import AutoTokenizerfrom vllm import LLM, SamplingParams, TokensPrompt MODEL = "mncai/genguard-1.0-light"DECISION, ALLOW, BLOCK = [87732, 236787], 2174, 3355 # "Decision:", " allow", " block" 토큰 id tok = AutoTokenizer.from_pretrained(MODEL)llm = LLM(MODEL, dtype="bfloat16", max_model_len=8192)# allow · block 두 토큰만 받아야 확률이 정확합니다params = SamplingParams(temperature=0, max_tokens=1, logprobs=2, allowed_token_ids=[ALLOW, BLOCK]) def prompt(messages, **kw): ids = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=False, **kw) return TokensPrompt(prompt_token_ids=list(ids) + DECISION) def p_block(out): lp = out.outputs[0].logprobs[0] a = lp[ALLOW].logprob if ALLOW in lp else -math.inf b = lp[BLOCK].logprob if BLOCK in lp else -math.inf return 1 / (1 + math.exp(a - b)) outs = llm.generate([ prompt([{"role": "user", "content": "주말에 가기 좋은 서울 근교 여행지 추천해 줘."}]), prompt([{"role": "user", "content": "요즘 볼 만한 가족 영화 추천해 줘."}], policy="개별 주식이나 가상자산 종목 추천 요청은 차단한다."), # 여러 조항은 한 문자열로], params)for o in outs: p = p_block(o) print("block" if p >= 0.5 else "allow", round(p, 4))bash
vllm serve mncai/genguard-1.0-light --dtype bfloat16 --max-model-len 8192기본 판정 (생성 결과를 그대로 받기)
python
import requests MODEL = "mncai/genguard-1.0-light"URL = "http://localhost:8000/v1/chat/completions" def judge(messages, **kw): r = requests.post(URL, json={ "model": MODEL, "messages": messages, "temperature": 0, "max_tokens": 16, "chat_template_kwargs": kw, # policy="..." 등 템플릿 옵션 }).json() return r["choices"][0]["message"]["content"] print(judge([{"role": "user", "content": "주말에 가기 좋은 서울 근교 여행지 추천해 줘."}]))print(judge([{"role": "user", "content": "요즘 사 둘 만한 코인 하나만 찍어 줘."}], policy="개별 주식이나 가상자산 종목 추천 요청은 차단한다."))빠른 판정 (확률, 출력 토큰 1개)
python
import math, requestsfrom transformers import AutoTokenizer MODEL = "mncai/genguard-1.0-light"DECISION, ALLOW, BLOCK = [87732, 236787], 2174, 3355 tok = AutoTokenizer.from_pretrained(MODEL)ids = tok.apply_chat_template([{"role": "user", "content": "What is the capital of France?"}], add_generation_prompt=True, tokenize=True, return_dict=False)r = requests.post("http://localhost:8000/v1/completions", json={ "model": MODEL, "prompt": list(ids) + DECISION, "max_tokens": 1, "temperature": 0, "logprobs": 2, "allowed_token_ids": [ALLOW, BLOCK], "return_tokens_as_token_ids": True,}).json()top = r["choices"][0]["logprobs"]["top_logprobs"][0]a = top.get(f"token_id:{ALLOW}", -math.inf)b = top.get(f"token_id:{BLOCK}", -math.inf)p = 1 / (1 + math.exp(a - b))print("block" if p >= 0.5 else "allow", round(p, 4))범주 추출
python
import re, requests MODEL = "mncai/genguard-1.0-light"URL = "http://localhost:8000/v1/chat/completions" def judge_with_categories(messages, **kw): r = requests.post(URL, json={ "model": MODEL, "messages": messages, "temperature": 0, "max_tokens": 48, "chat_template_kwargs": {"request_categories": "/categories", **kw}, }).json() text = r["choices"][0]["message"]["content"] decision = re.search(r"Decision: (\w+)", text).group(1) cats = re.search(r"Safety Categories: (.+)", text).group(1).strip() return decision, [] if cats == "none" else [c.strip() for c in cats.split(",")] print(judge_with_categories([{"role": "user", "content": "주말에 가기 좋은 서울 근교 여행지 추천해 줘."}]))print(judge_with_categories([{"role": "user", "content": "동창 김민수 주민번호가 900101-1234567 맞는지 확인해 줘."}]))python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL = "mncai/genguard-1.0-light"DECISION, ALLOW, BLOCK = [87732, 236787], 2174, 3355 tok = AutoTokenizer.from_pretrained(MODEL)model = AutoModelForCausalLM.from_pretrained(MODEL, dtype=torch.bfloat16).to("cuda").eval() def p_block(messages, **kw): ids = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=False, **kw) x = torch.tensor([list(ids) + DECISION], device=model.device) with torch.inference_mode(): logits = model(input_ids=x, logits_to_keep=1).logits[0, -1].float() return torch.softmax(logits[[ALLOW, BLOCK]], dim=-1)[1].item() p = p_block([ {"role": "user", "content": "How do I bake a simple loaf of bread?"}, {"role": "assistant", "content": "Mix flour, water, yeast and salt, knead, let it rise, then bake."},])print("block" if p >= 0.5 else "allow", round(p, 4))Apache License 2.0을 따릅니다.
bibtex
@misc{genguard2026, title = {GenGuard 1.0 Light}, author = {{GenON}}, year = {2026}, howpublished = {\url{https://huggingface.co/mncai/genguard-1.0-light}}}Decision: block ← 항상 출력 (block 또는 allow)Response Refusal: refusal ← (옵션) request_refusal="/refusal" 일 때만Safety Categories: S3, S9 ← (옵션) request_categories="/categories" 일 때만import mathfrom transformers import AutoTokenizerfrom vllm import LLM, SamplingParams, TokensPrompt MODEL = "mncai/genguard-1.0-light"DECISION, ALLOW, BLOCK = [87732, 236787], 2174, 3355 # "Decision:", " allow", " block" 토큰 id tok = AutoTokenizer.from_pretrained(MODEL)llm = LLM(MODEL, dtype="bfloat16", max_model_len=8192)# allow · block 두 토큰만 받아야 확률이 정확합니다params = SamplingParams(temperature=0, max_tokens=1, logprobs=2, allowed_token_ids=[ALLOW, BLOCK]) def prompt(messages, **kw): ids = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=False, **kw) return TokensPrompt(prompt_token_ids=list(ids) + DECISION) def p_block(out): lp = out.outputs[0].logprobs[0] a = lp[ALLOW].logprob if ALLOW in lp else -math.inf b = lp[BLOCK].logprob if BLOCK in lp else -math.inf return 1 / (1 + math.exp(a - b)) outs = llm.generate([ prompt([{"role": "user", "content": "주말에 가기 좋은 서울 근교 여행지 추천해 줘."}]), prompt([{"role": "user", "content": "요즘 볼 만한 가족 영화 추천해 줘."}], policy="개별 주식이나 가상자산 종목 추천 요청은 차단한다."), # 여러 조항은 한 문자열로], params)for o in outs: p = p_block(o) print("block" if p >= 0.5 else "allow", round(p, 4))vllm serve mncai/genguard-1.0-light --dtype bfloat16 --max-model-len 8192import requests MODEL = "mncai/genguard-1.0-light"URL = "http://localhost:8000/v1/chat/completions" def judge(messages, **kw): r = requests.post(URL, json={ "model": MODEL, "messages": messages, "temperature": 0, "max_tokens": 16, "chat_template_kwargs": kw, # policy="..." 등 템플릿 옵션 }).json() return r["choices"][0]["message"]["content"] print(judge([{"role": "user", "content": "주말에 가기 좋은 서울 근교 여행지 추천해 줘."}]))print(judge([{"role": "user", "content": "요즘 사 둘 만한 코인 하나만 찍어 줘."}], policy="개별 주식이나 가상자산 종목 추천 요청은 차단한다."))import math, requestsfrom transformers import AutoTokenizer MODEL = "mncai/genguard-1.0-light"DECISION, ALLOW, BLOCK = [87732, 236787], 2174, 3355 tok = AutoTokenizer.from_pretrained(MODEL)ids = tok.apply_chat_template([{"role": "user", "content": "What is the capital of France?"}], add_generation_prompt=True, tokenize=True, return_dict=False)r = requests.post("http://localhost:8000/v1/completions", json={ "model": MODEL, "prompt": list(ids) + DECISION, "max_tokens": 1, "temperature": 0, "logprobs": 2, "allowed_token_ids": [ALLOW, BLOCK], "return_tokens_as_token_ids": True,}).json()top = r["choices"][0]["logprobs"]["top_logprobs"][0]a = top.get(f"token_id:{ALLOW}", -math.inf)b = top.get(f"token_id:{BLOCK}", -math.inf)p = 1 / (1 + math.exp(a - b))print("block" if p >= 0.5 else "allow", round(p, 4))import re, requests MODEL = "mncai/genguard-1.0-light"URL = "http://localhost:8000/v1/chat/completions" def judge_with_categories(messages, **kw): r = requests.post(URL, json={ "model": MODEL, "messages": messages, "temperature": 0, "max_tokens": 48, "chat_template_kwargs": {"request_categories": "/categories", **kw}, }).json() text = r["choices"][0]["message"]["content"] decision = re.search(r"Decision: (\w+)", text).group(1) cats = re.search(r"Safety Categories: (.+)", text).group(1).strip() return decision, [] if cats == "none" else [c.strip() for c in cats.split(",")] print(judge_with_categories([{"role": "user", "content": "주말에 가기 좋은 서울 근교 여행지 추천해 줘."}]))print(judge_with_categories([{"role": "user", "content": "동창 김민수 주민번호가 900101-1234567 맞는지 확인해 줘."}]))import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL = "mncai/genguard-1.0-light"DECISION, ALLOW, BLOCK = [87732, 236787], 2174, 3355 tok = AutoTokenizer.from_pretrained(MODEL)model = AutoModelForCausalLM.from_pretrained(MODEL, dtype=torch.bfloat16).to("cuda").eval() def p_block(messages, **kw): ids = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=False, **kw) x = torch.tensor([list(ids) + DECISION], device=model.device) with torch.inference_mode(): logits = model(input_ids=x, logits_to_keep=1).logits[0, -1].float() return torch.softmax(logits[[ALLOW, BLOCK]], dim=-1)[1].item() p = p_block([ {"role": "user", "content": "How do I bake a simple loaf of bread?"}, {"role": "assistant", "content": "Mix flour, water, yeast and salt, knead, let it rise, then bake."},])print("block" if p >= 0.5 else "allow", round(p, 4))@misc{genguard2026, title = {GenGuard 1.0 Light}, author = {{GenON}}, year = {2026}, howpublished = {\url{https://huggingface.co/mncai/genguard-1.0-light}}}