Phi-3 Mini: Complete Benchmark Performance Guide

other phi-3-mini 2024-04-22 MIT

In-depth analysis of Phi-3 Mini performance on MMLU, HumanEval, GSM8K, MATH, BBH benchmarks, including pricing and use cases.

Key Specifications

Vendorother
Versionphi-3-mini
Release Date2024-04-22
Context Window4096 tokens
Input Modalitiestext
Output Modalitiestext
LicenseMIT
Documentationhttps://huggingface.co/models

Benchmark Performance

BenchmarkScoreUnitEvaluated AtNotesSource
MMLU43.6%2024-04-225-shotview
HUMANEVAL26.9pass@12024-04-22—view
GSM8K40.9%2024-04-220-shot CoTview
MATH17.9%2024-04-220-shot CoTview
BBH55.9%2024-04-223-shot CoTview
GPQA20.7%2024-04-220-shotview
IFEVAL54.9%2024-04-22prompt_strictview
ARC84.4%2024-04-22challengeview
MUSR33.5%2024-04-220-shotview
WINOGRANDE67.1%2024-04-220-shotview

Pricing

TierPriceCurrency
Input$0.1 / MtokUSD
Output$0.1 / MtokUSD
Cache Read$0 / MtokUSD
Cache Write$0 / MtokUSD

Source: https://huggingface.co/models · as of 2024-04-22

Compliance

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

Phi-3 Mini

모델 개요

Microsoft Phi-3 Mini 3.8B 轻量模型, 4K 上下文 (可扩展 128K), 训练数据高质量, 性能超越 Llama 2 7B。

핵심 사양

공급업체버전출시일컨텍스트 창입력 모달리티출력 모달리티라이선스
Otherphi-3-mini2024-04-224KtexttextMIT

벤치마크 성능

벤치마크점수단위비고
MMLU (Massive Multitask Language Understanding)43.6%5-shot
HumanEval26.9pass@1—
GSM8K (Grade School Math 8K)40.9%0-shot CoT
MATH17.9%0-shot CoT
BBH (BIG-Bench Hard)55.9%3-shot CoT
GPQA20.7%0-shot
IFEval54.9%prompt_strict
ARC84.4%challenge
MUSR33.5%0-shot
WinoGrande67.1%0-shot

가격

입력출력캐시 읽기캐시 쓰기
————

백만 토큰당

강점

  • 可靠的通用模型。

약점

  • MMLU 仅 43.6，知识推理偏弱。
  • HumanEval 26.9，代码能力较弱。
  • 闭源专有模型，不支持自托管。
  • 上下文窗口 4K 偏小。

사용 사례

  • 通用对话与问答

참고문헌