---
# System prepended metadata

title: llama 2 整理

---

## 以往 LLM 的規格
| Name | Parameter Size | Maximum token length  | organization |
|-|-|-|-|
| PaLM | 540 B | 4096 | Google |
| Claude v1 | | 100k | anthropic (Google) |
| Cohere | 6 ~ 52 B | 4096 | startup (Former Googler) | 
| Falcon | 7 ~ 40 B |  | TII |

`Table 1, 整理自 [2]`

## Haggingface 對不同 opensource model 的整理
![](https://hackmd.io/_uploads/B1c1Sgac2.png)
`Table 2, Source: [1]`

## 結論
1. 性能提昇
    根據上圖。Llama 2 相比於其他 opensource model 
    - 在較小的 model size 得到相近的性能 (leaderboard score)
        > Llama-2-13B's score > mpt-30B's score
    - 在大 model size 下有最佳的性能 (leaderboard score)
        > Llama-2-70B-caht's score > Falcon-40B's score

2. 允許商用
    相較於 Llama ，本次的 license 允許 `Commercial use`

## Reference
[1] [Llama 2 is here - get it on Hugging Face]( https://huggingface.co/blog/llama2)

[2] [12 Best Large Language Models (LLMs) in 2023](https://beebom.com/best-large-language-models-llms/)

[3] [Tokens](https://docs.cohere.com/docs/tokens)