Key Specifications
| Vendor | other |
|---|
| Version | yi-large-turbo |
|---|
| Release Date | 2024-07-24 |
|---|
| Context Window | 16384 tokens |
|---|
| Input Modalities | text |
|---|
| Output Modalities | text |
|---|
| License | Proprietary |
|---|
| Documentation | https://huggingface.co/models |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 84.4 | % | 2024-07-24 | 5-shot | view |
| HUMANEVAL | 72.4 | pass@1 | 2024-07-24 | — | view |
| GSM8K | 81 | % | 2024-07-24 | 0-shot CoT | view |
| MATH | 42.2 | % | 2024-07-24 | 0-shot CoT | view |
| BBH | 76.4 | % | 2024-07-24 | 3-shot CoT | view |
| GPQA | 38.2 | % | 2024-07-24 | 0-shot | view |
| IFEVAL | 70.7 | % | 2024-07-24 | prompt_strict | view |
| ARC | 95.6 | % | 2024-07-24 | challenge | view |
| MUSR | 63.7 | % | 2024-07-24 | 0-shot | view |
| WINOGRANDE | 82.3 | % | 2024-07-24 | 0-shot | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $2 / Mtok | USD |
| Output | $2 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://huggingface.co/models
· as of 2024-07-24
Compliance
- Data Residency: self-host
- SOC2: ✗
- HIPAA: ✗
- GDPR: ✗
- ISO 27001: ✗
Yi Large Turbo
모델 개요
01.AI Yi Large Turbo 经济型旗舰, 16K 上下文, 速度快成本低, 性能接近 Yi Large。
핵심 사양
| 공급업체 | 버전 | 출시일 | 컨텍스트 창 | 입력 모달리티 | 출력 모달리티 | 라이선스 |
|---|
| Other | yi-large-turbo | 2024-07-24 | 16K | text | text | Proprietary |
벤치마크 성능
| 벤치마크 | 점수 | 단위 | 비고 |
|---|
| MMLU (Massive Multitask Language Understanding) | 84.4 | % | 5-shot |
| HumanEval | 72.4 | pass@1 | — |
| GSM8K (Grade School Math 8K) | 81.0 | % | 0-shot CoT |
| MATH | 42.2 | % | 0-shot CoT |
| BBH (BIG-Bench Hard) | 76.4 | % | 3-shot CoT |
| GPQA | 38.2 | % | 0-shot |
| IFEval | 70.7 | % | prompt_strict |
| ARC | 95.6 | % | challenge |
| MUSR | 63.7 | % | 0-shot |
| WinoGrande | 82.3 | % | 0-shot |
가격
백만 토큰당
강점
- MMLU score 84.4, strong knowledge reasoning.
약점
- 闭源专有模型，不支持自托管。
- 上下文窗口 16K 偏小。
사용 사례
참고문헌