Key Specifications

Vendormeta
Versionguard-3-8b
Release Date2024-07-23
Context Window128000 tokens
Input Modalitiestext
Output Modalitiestext
LicenseLlama 3.2 Community License
Documentationhttps://llama.meta.com/docs/

Benchmark Performance

BenchmarkScoreUnitEvaluated AtNotesSource
MMLU40.1%2024-07-235-shotview
HUMANEVAL28pass@12024-07-23view
GSM8K30.9%2024-07-230-shot CoTview
MATH8.4%2024-07-230-shot CoTview
BBH44.1%2024-07-233-shot CoTview
GPQA27.9%2024-07-230-shotview
IFEVAL50%2024-07-23prompt_strictview
ARC76%2024-07-23challengeview
MUSR22.8%2024-07-230-shotview
WINOGRANDE68%2024-07-230-shotview

Pricing

TierPriceCurrency
Input$0.18 / MtokUSD
Output$0.18 / MtokUSD
Cache Read$0 / MtokUSD
Cache Write$0 / MtokUSD

Source: https://ai.meta.com/blog/ · as of 2024-07-23

Compliance

  • Data Residency: self-host
  • SOC2: ✗
  • HIPAA: ✗
  • GDPR: ✗
  • ISO 27001: ✗

Llama Guard 3 8B

모델 개요

Meta Llama Guard 3 8B 内容安全分类模型, 128K 上下文, 支持 MLCommons 安全分类标准。

핵심 사양

공급업체버전출시일컨텍스트 창입력 모달리티출력 모달리티라이선스
Metaguard-3-8b2024-07-23128KtexttextLlama 3.2 Community License

벤치마크 성능

벤치마크점수단위비고
MMLU (Massive Multitask Language Understanding)40.1%5-shot
HumanEval28.0pass@1
GSM8K (Grade School Math 8K)30.9%0-shot CoT
MATH8.4%0-shot CoT
BBH (BIG-Bench Hard)44.1%3-shot CoT
GPQA27.9%0-shot
IFEval50.0%prompt_strict
ARC76.0%challenge
MUSR22.8%0-shot
WinoGrande68.0%0-shot

가격

입력출력캐시 읽기캐시 쓰기

백만 토큰당

강점

  • 可靠的通用模型。

약점

  • MMLU 仅 40.1,知识推理偏弱。
  • HumanEval 28.0,代码能力较弱。
  • 闭源专有模型,不支持自托管。

사용 사례

  • 通用对话与问答

참고문헌