Key Specifications
| Vendor | google |
|---|
| Version | gemma-2b |
|---|
| Release Date | 2024-02-21 |
|---|
| Context Window | 8192 tokens |
|---|
| Input Modalities | text |
|---|
| Output Modalities | text |
|---|
| License | Gemma License |
|---|
| Documentation | https://ai.google.dev/gemma/docs |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 48 | % | 2024-02-21 | 5-shot | view |
| HUMANEVAL | 43.2 | pass@1 | 2024-02-21 | — | view |
| GSM8K | 27.8 | % | 2024-02-21 | 0-shot CoT | view |
| MATH | 27.5 | % | 2024-02-21 | 0-shot CoT | view |
| BBH | 53 | % | 2024-02-21 | 3-shot CoT | view |
| GPQA | 18 | % | 2024-02-21 | 0-shot | view |
| IFEVAL | 46.4 | % | 2024-02-21 | prompt_strict | view |
| ARC | 77.7 | % | 2024-02-21 | challenge | view |
| MUSR | 30.6 | % | 2024-02-21 | 0-shot | view |
| WINOGRANDE | 72 | % | 2024-02-21 | 0-shot | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $0.1 / Mtok | USD |
| Output | $0.1 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://ai.google.dev/pricing
· as of 2024-02-21
Compliance
- Data Residency: US
- SOC2: ✓
- HIPAA: ✗
- GDPR: ✓
- ISO 27001: ✓
Gemma 2B
Przegląd modelu
Google Gemma 2B 轻量开源模型, 8K 上下文, 2B 参数, 适合资源受限环境与边缘部署。
Podstawowe specyfikacje
| Dostawca | Wersja | Data wydania | Okno kontekstowe | Modalności wejściowe | Modalności wyjściowe | Licencja |
|---|
| Google | gemma-2b | 2024-02-21 | 8K | text | text | Gemma License |
Wydajność benchmarków
| Benchmark | Wynik | Jednostka | Uwagi |
|---|
| MMLU (Massive Multitask Language Understanding) | 48.0 | % | 5-shot |
| HumanEval | 43.2 | pass@1 | — |
| GSM8K (Grade School Math 8K) | 27.8 | % | 0-shot CoT |
| MATH | 27.5 | % | 0-shot CoT |
| BBH (BIG-Bench Hard) | 53.0 | % | 3-shot CoT |
| GPQA | 18.0 | % | 0-shot |
| IFEval | 46.4 | % | prompt_strict |
| ARC | 77.7 | % | challenge |
| MUSR | 30.6 | % | 0-shot |
| WinoGrande | 72.0 | % | 0-shot |
Ceny
| Wejście | Wyjście | Odczyt pamięci podręcznej | Zapis pamięci podręcznej |
|---|
| — | — | — | — |
za milion tokenów
Mocne strony
Słabe strony
- MMLU 仅 48.0，知识推理偏弱。
- HumanEval 43.2，代码能力较弱。
- 闭源专有模型，不支持自托管。
- 上下文窗口 8K 偏小。
Przypadki użycia
Referencje