Argilla Notus 7B v1: Complete Benchmark Performance Guide

other notus-7b-v1 2023-12-15 Apache 2.0

In-depth analysis of Argilla Notus 7B v1 performance on MMLU, HumanEval, GSM8K, MATH, BBH benchmarks, including pricing and use cases.

Key Specifications

Vendorother
Versionnotus-7b-v1
Release Date2023-12-15
Context Window4096 tokens
Input Modalitiestext
Output Modalitiestext
LicenseApache 2.0
Documentationhttps://huggingface.co/models

Benchmark Performance

BenchmarkScoreUnitEvaluated AtNotesSource
MMLU71.4%2023-12-155-shotview
HUMANEVAL36.8pass@12023-12-15—view
GSM8K58.8%2023-12-150-shot CoTview
MATH31%2023-12-150-shot CoTview
BBH62.4%2023-12-153-shot CoTview
GPQA32.7%2023-12-150-shotview
IFEVAL42.8%2023-12-15prompt_strictview
ARC89.8%2023-12-15challengeview
MUSR30.1%2023-12-150-shotview
WINOGRANDE69.8%2023-12-150-shotview

Pricing

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

Source: https://huggingface.co/models · as of 2023-12-15

Compliance

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

Argilla Notus 7B v1

모델 개요

Argilla Notus 7B v1 对话微调模型, 4K 上下文, 基于 Zephyr 改进, 通过高质量反馈数据提升性能。

핵심 사양

공급업체버전출시일컨텍스트 창입력 모달리티출력 모달리티라이선스
Othernotus-7b-v12023-12-154KtexttextApache 2.0

벤치마크 성능

벤치마크점수단위비고
MMLU (Massive Multitask Language Understanding)71.4%5-shot
HumanEval36.8pass@1—
GSM8K (Grade School Math 8K)58.8%0-shot CoT
MATH31.0%0-shot CoT
BBH (BIG-Bench Hard)62.4%3-shot CoT
GPQA32.7%0-shot
IFEval42.8%prompt_strict
ARC89.8%challenge
MUSR30.1%0-shot
WinoGrande69.8%0-shot

가격

입력출력캐시 읽기캐시 쓰기
————

백만 토큰당

강점

  • 可靠的通用模型。

약점

  • HumanEval 36.8，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