Key Specifications
| Vendor | other |
|---|
| Version | baichuan2-13b-chat |
|---|
| Release Date | 2023-09-06 |
|---|
| Context Window | 4096 tokens |
|---|
| Input Modalities | text |
|---|
| Output Modalities | text |
|---|
| License | Baichuan 2 License |
|---|
| Documentation | https://huggingface.co/models |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 58.6 | % | 2023-09-06 | 5-shot | view |
| HUMANEVAL | 49.6 | pass@1 | 2023-09-06 | — | view |
| GSM8K | 41 | % | 2023-09-06 | 0-shot CoT | view |
| MATH | 23.4 | % | 2023-09-06 | 0-shot CoT | view |
| BBH | 64.2 | % | 2023-09-06 | 3-shot CoT | view |
| GPQA | 34.7 | % | 2023-09-06 | 0-shot | view |
| IFEVAL | 48.5 | % | 2023-09-06 | prompt_strict | view |
| ARC | 83.2 | % | 2023-09-06 | challenge | view |
| MUSR | 37.9 | % | 2023-09-06 | 0-shot | view |
| WINOGRANDE | 69.5 | % | 2023-09-06 | 0-shot | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $0.3 / Mtok | USD |
| Output | $0.3 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://huggingface.co/models
· as of 2023-09-06
Compliance
- Data Residency: self-host
- SOC2: ✗
- HIPAA: ✗
- GDPR: ✗
- ISO 27001: ✗
Baichuan2 13B Chat
Modellübersicht
百川智能 Baichuan2 13B Chat 对话模型, 4K 上下文, 中英双语能力突出, 适合中文场景部署。
Kernspezifikationen
| Anbieter | Version | Veröffentlichungsdatum | Kontextfenster | Eingabemodalitäten | Ausgabemodalitäten | Lizenz |
|---|
| Other | baichuan2-13b-chat | 2023-09-06 | 4K | text | text | Baichuan 2 License |
Benchmark-Leistung
| Benchmark | Ergebnis | Einheit | Notizen |
|---|
| MMLU (Massive Multitask Language Understanding) | 58.6 | % | 5-shot |
| HumanEval | 49.6 | pass@1 | — |
| GSM8K (Grade School Math 8K) | 41.0 | % | 0-shot CoT |
| MATH | 23.4 | % | 0-shot CoT |
| BBH (BIG-Bench Hard) | 64.2 | % | 3-shot CoT |
| GPQA | 34.7 | % | 0-shot |
| IFEval | 48.5 | % | prompt_strict |
| ARC | 83.2 | % | challenge |
| MUSR | 37.9 | % | 0-shot |
| WinoGrande | 69.5 | % | 0-shot |
Preise
| Eingabe | Ausgabe | Cache-Lesen | Cache-Schreiben |
|---|
| — | — | — | — |
pro Million Token
Stärken
Schwächen
- MMLU 仅 58.6,知识推理偏弱。
- HumanEval 49.6,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。
Anwendungsfälle
Referenzen