Key Specifications
| Vendor | meta |
|---|
| Version | code-llama-7b |
|---|
| Release Date | 2023-08-24 |
|---|
| Context Window | 16000 tokens |
|---|
| Input Modalities | text |
|---|
| Output Modalities | text |
|---|
| License | Llama 2 Community License |
|---|
| Documentation | https://llama.meta.com/docs/ |
|---|
Benchmark Performance
| Benchmark | Score | Unit | Evaluated At | Notes | Source |
|---|
| MMLU | 58.5 | % | 2023-08-24 | 5-shot | view |
| HUMANEVAL | 76.4 | pass@1 | 2023-08-24 | — | view |
| GSM8K | 52.2 | % | 2023-08-24 | 0-shot CoT | view |
| MATH | 47.1 | % | 2023-08-24 | 0-shot CoT | view |
| BBH | 66.9 | % | 2023-08-24 | 3-shot CoT | view |
| GPQA | 29.5 | % | 2023-08-24 | 0-shot | view |
| IFEVAL | 61.2 | % | 2023-08-24 | prompt_strict | view |
| ARC | 90.1 | % | 2023-08-24 | challenge | view |
| MUSR | 42.9 | % | 2023-08-24 | 0-shot | view |
| WINOGRANDE | 76.3 | % | 2023-08-24 | 0-shot | view |
Pricing
| Tier | Price | Currency |
|---|
| Input | $0.2 / Mtok | USD |
| Output | $0.2 / Mtok | USD |
| Cache Read | $0 / Mtok | USD |
| Cache Write | $0 / Mtok | USD |
Source:
https://ai.meta.com/blog/
· as of 2023-08-24
Compliance
- Data Residency: self-host
- SOC2: ✗
- HIPAA: ✗
- GDPR: ✗
- ISO 27001: ✗
Code Llama 7B
Modellöversikt
Meta Code Llama 7B 代码专用开源模型, 16K 上下文, 7B 参数, 适合本地代码补全与生成。
Kärnspecifikationer
| Leverantör | Version | Releasedatum | Kontextfönster | Inmatningsmodaliteter | Utmatningsmodaliteter | Licens |
|---|
| Meta | code-llama-7b | 2023-08-24 | 16K | text | text | Llama 2 Community License |
Benchmarkprestanda
| Benchmark | Poäng | Enhet | Anteckningar |
|---|
| MMLU (Massive Multitask Language Understanding) | 58.5 | % | 5-shot |
| HumanEval | 76.4 | pass@1 | — |
| GSM8K (Grade School Math 8K) | 52.2 | % | 0-shot CoT |
| MATH | 47.1 | % | 0-shot CoT |
| BBH (BIG-Bench Hard) | 66.9 | % | 3-shot CoT |
| GPQA | 29.5 | % | 0-shot |
| IFEval | 61.2 | % | prompt_strict |
| ARC | 90.1 | % | challenge |
| MUSR | 42.9 | % | 0-shot |
| WinoGrande | 76.3 | % | 0-shot |
Priser
| Inmatning | Utmatning | Cacheläsning | Cacheskrivning |
|---|
| — | — | — | — |
per miljon tokens
Styrkor
Svagheter
- MMLU 仅 58.5,知识推理偏弱。
- 闭源专有模型,不支持自托管。
- 上下文窗口 16K 偏小。
Användningsområden
Referenser