Key Specifications
| Vendor | other |
|---|
| Version | stablelm-zephyr-3b |
|---|
| Release Date | 2024-01-19 |
|---|
| Context Window | 4096 tokens |
|---|
| Input Modalities | text |
|---|
| Output Modalities | text |
|---|
| License | Stability AI Community License |
|---|
| Documentation | https://huggingface.co/models |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 44.3 | % | 2024-01-19 | 5-shot | view |
| HUMANEVAL | 47.1 | pass@1 | 2024-01-19 | — | view |
| GSM8K | 36.2 | % | 2024-01-19 | 0-shot CoT | view |
| MATH | 14.9 | % | 2024-01-19 | 0-shot CoT | view |
| BBH | 43.9 | % | 2024-01-19 | 3-shot CoT | view |
| GPQA | 21 | % | 2024-01-19 | 0-shot | view |
| IFEVAL | 55.3 | % | 2024-01-19 | prompt_strict | view |
| ARC | 82.1 | % | 2024-01-19 | challenge | view |
| MUSR | 23.5 | % | 2024-01-19 | 0-shot | view |
| WINOGRANDE | 71.6 | % | 2024-01-19 | 0-shot | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $0.12 / Mtok | USD |
| Output | $0.12 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://huggingface.co/models
· as of 2024-01-19
Compliance
- Data Residency: self-host
- SOC2: ✗
- HIPAA: ✗
- GDPR: ✗
- ISO 27001: ✗
StableLM Zephyr 3B
Przegląd modelu
Stability AI StableLM Zephyr 3B 对话微调模型, 4K 上下文, 基于 StableLM 2 1.6B DPO 微调。
Podstawowe specyfikacje
| Dostawca | Wersja | Data wydania | Okno kontekstowe | Modalności wejściowe | Modalności wyjściowe | Licencja |
|---|
| Other | stablelm-zephyr-3b | 2024-01-19 | 4K | text | text | Stability AI Community License |
Wydajność benchmarków
| Benchmark | Wynik | Jednostka | Uwagi |
|---|
| MMLU (Massive Multitask Language Understanding) | 44.3 | % | 5-shot |
| HumanEval | 47.1 | pass@1 | — |
| GSM8K (Grade School Math 8K) | 36.2 | % | 0-shot CoT |
| MATH | 14.9 | % | 0-shot CoT |
| BBH (BIG-Bench Hard) | 43.9 | % | 3-shot CoT |
| GPQA | 21.0 | % | 0-shot |
| IFEval | 55.3 | % | prompt_strict |
| ARC | 82.1 | % | challenge |
| MUSR | 23.5 | % | 0-shot |
| WinoGrande | 71.6 | % | 0-shot |
Ceny
| Wejście | Wyjście | Odczyt pamięci podręcznej | Zapis pamięci podręcznej |
|---|
| — | — | — | — |
za milion tokenów
Mocne strony
Słabe strony
- MMLU 仅 44.3，知识推理偏弱。
- HumanEval 47.1，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。
Przypadki użycia
Referencje