Capybara 1.5B: Complete Benchmark Performance Guide

other capybara-1-5b 2023-11-15 Apache 2.0

In-depth analysis of Capybara 1.5B performance on MMLU, HumanEval, GSM8K, MATH, BBH benchmarks, including pricing and use cases.

Key Specifications

Vendorother
Versioncapybara-1-5b
Release Date2023-11-15
Context Window4096 tokens
Input Modalitiestext
Output Modalitiestext
LicenseApache 2.0
Documentationhttps://huggingface.co/models

Benchmark Performance

BenchmarkScoreUnitEvaluated AtNotesSource
MMLU51.7%2023-11-155-shotview
HUMANEVAL30.1pass@12023-11-15—view
GSM8K28.5%2023-11-150-shot CoTview
MATH25.6%2023-11-150-shot CoTview
BBH48.3%2023-11-153-shot CoTview
GPQA18.1%2023-11-150-shotview
IFEVAL52.4%2023-11-15prompt_strictview
ARC80.1%2023-11-15challengeview
MUSR34%2023-11-150-shotview
WINOGRANDE68.9%2023-11-150-shotview

Pricing

TierPriceCurrency
Input$0.08 / MtokUSD
Output$0.08 / MtokUSD
Cache Read$0 / MtokUSD
Cache Write$0 / MtokUSD

Source: https://huggingface.co/models · as of 2023-11-15

Compliance

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

Capybara 1.5B

모델 개요

IntrinsicaAI Capybara 1.5B 轻量对话模型, 4K 上下文, 1.5B 参数, 适合边缘设备对话场景。

핵심 사양

공급업체버전출시일컨텍스트 창입력 모달리티출력 모달리티라이선스
Othercapybara-1-5b2023-11-154KtexttextApache 2.0

벤치마크 성능

벤치마크점수단위비고
MMLU (Massive Multitask Language Understanding)51.7%5-shot
HumanEval30.1pass@1—
GSM8K (Grade School Math 8K)28.5%0-shot CoT
MATH25.6%0-shot CoT
BBH (BIG-Bench Hard)48.3%3-shot CoT
GPQA18.1%0-shot
IFEval52.4%prompt_strict
ARC80.1%challenge
MUSR34.0%0-shot
WinoGrande68.9%0-shot

가격

입력출력캐시 읽기캐시 쓰기
————

백만 토큰당

강점

  • 可靠的通用模型。

약점

  • MMLU 仅 51.7，知识推理偏弱。
  • HumanEval 30.1，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