Key Specifications
| Vendor | other |
|---|
| Version | yi-vision |
|---|
| Release Date | 2024-05-13 |
|---|
| Context Window | 16384 tokens |
|---|
| Input Modalities | text, image |
|---|
| Output Modalities | text |
|---|
| License | Proprietary |
|---|
| Documentation | https://huggingface.co/models |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 85.5 | % | 2024-05-13 | 5-shot | view |
| HUMANEVAL | 76.1 | pass@1 | 2024-05-13 | — | view |
| GSM8K | 89.9 | % | 2024-05-13 | 0-shot CoT | view |
| MATH | 58.7 | % | 2024-05-13 | 0-shot CoT | view |
| BBH | 78.3 | % | 2024-05-13 | 3-shot CoT | view |
| GPQA | 50.3 | % | 2024-05-13 | 0-shot | view |
| IFEVAL | 81.4 | % | 2024-05-13 | prompt_strict | view |
| ARC | 95.1 | % | 2024-05-13 | challenge | view |
| MUSR | 61.4 | % | 2024-05-13 | 0-shot | view |
| WINOGRANDE | 82.3 | % | 2024-05-13 | 0-shot | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $3 / Mtok | USD |
| Output | $3 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://huggingface.co/models
· as of 2024-05-13
Compliance
- Data Residency: self-host
- SOC2: ✗
- HIPAA: ✗
- GDPR: ✗
- ISO 27001: ✗
Yi Vision
Przegląd modelu
01.AI Yi Vision 多模态模型, 16K 上下文, 支持图像理解, 中英文视觉问答能力突出。
Podstawowe specyfikacje
| Dostawca | Wersja | Data wydania | Okno kontekstowe | Modalności wejściowe | Modalności wyjściowe | Licencja |
|---|
| Other | yi-vision | 2024-05-13 | 16K | text, image | text | Proprietary |
Wydajność benchmarków
| Benchmark | Wynik | Jednostka | Uwagi |
|---|
| MMLU (Massive Multitask Language Understanding) | 85.5 | % | 5-shot |
| HumanEval | 76.1 | pass@1 | — |
| GSM8K (Grade School Math 8K) | 89.9 | % | 0-shot CoT |
| MATH | 58.7 | % | 0-shot CoT |
| BBH (BIG-Bench Hard) | 78.3 | % | 3-shot CoT |
| GPQA | 50.3 | % | 0-shot |
| IFEval | 81.4 | % | prompt_strict |
| ARC | 95.1 | % | challenge |
| MUSR | 61.4 | % | 0-shot |
| WinoGrande | 82.3 | % | 0-shot |
Ceny
| Wejście | Wyjście | Odczyt pamięci podręcznej | Zapis pamięci podręcznej |
|---|
| — | — | — | — |
za milion tokenów
Mocne strony
- MMLU score 85.5, strong knowledge reasoning.
- GSM8K 89.9, robust math reasoning.
- 支持文本、图像、音频多模态输入。
Słabe strony
- 闭源专有模型，不支持自托管。
- 上下文窗口 16K 偏小。
Przypadki użycia
Referencje