Key Specifications

Vendormistral
Version7b-v0.1
Release Date2023-09-27
Context Window8192 tokens
Input Modalitiestext
Output Modalitiestext
LicenseApache 2.0
Documentationhttps://docs.mistral.ai/

Benchmark Performance

BenchmarkScoreUnitEvaluated AtNotesSource
MMLU68.4%2023-09-275-shotview
HUMANEVAL56.4pass@12023-09-27view
GSM8K63.1%2023-09-270-shot CoTview
MATH26.5%2023-09-270-shot CoTview
BBH65.4%2023-09-273-shot CoTview
GPQA25.3%2023-09-270-shotview
IFEVAL58.9%2023-09-27prompt_strictview
ARC88.7%2023-09-27challengeview
MUSR37.4%2023-09-270-shotview
WINOGRANDE79.6%2023-09-270-shotview

Pricing

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

Source: https://mistral.ai/technology/ · as of 2023-09-27

Compliance

  • Data Residency: EU
  • SOC2: ✓
  • HIPAA: ✗
  • GDPR: ✓
  • ISO 27001: ✓

Mistral 7B v0.1

Aperçu du modèle

Mistral 7B v0.1 首版开源模型, 8K 上下文, 7B 参数, 在所有 7B 模型中表现领先, Apache 2.0 可商用。

Spécifications principales

FournisseurVersionDate de sortieFenêtre de contexteModalités d’entréeModalités de sortieLicence
Mistral7b-v0.12023-09-278KtexttextApache 2.0

Performance aux benchmarks

BenchmarkScoreUnitéNotes
MMLU (Massive Multitask Language Understanding)68.4%5-shot
HumanEval56.4pass@1
GSM8K (Grade School Math 8K)63.1%0-shot CoT
MATH26.5%0-shot CoT
BBH (BIG-Bench Hard)65.4%3-shot CoT
GPQA25.3%0-shot
IFEval58.9%prompt_strict
ARC88.7%challenge
MUSR37.4%0-shot
WinoGrande79.6%0-shot

Tarification

EntréeSortieLecture cacheÉcriture cache

par million de jetons

Forces

  • 可靠的通用模型。

Faiblesses

  • 闭源专有模型,不支持自托管。
  • 上下文窗口 8K 偏小。

Cas d’usage

  • 通用对话与问答

Références