A side-by-side comparison of release history, capabilities, licensing and model sizes.
| Attribute |
 Alpaca
|
 Koala
|
|---|---|---|
| Developer | Stanford | BAIR |
| Current generation | – | – |
| Initial release | 2023-03-13 | 2023-04-03 |
| Instruct tuned | Yes | Yes |
| Coding | No | No |
| Finetuning | Yes | Yes |
| Open source | Yes | Yes |
| License | Noncommercial | Noncommercial |
| Model sizes | 7B | 7B, 13B |
| Links |
Alpaca is an instruction-finetuned LLM based off of LLaMA.
The first of many instruct-finetuned versions of LLaMA, Alpaca is an instruction-following model introduced by Stanford researchers. Impressively, with only $600 of compute spend, the researchers demonstrated that on qualitative benchmarks Alpaca performed similarly to OpenAI's text-davinci-003, a significantly larger model.
Released alongside Vicuna, Koala is one of many descendants of the Meta LLaMA model trained on dialogue data collected from the web. On the developers' benchmarks, Koala outperforms its sibling Alpaca, though its adoption has been significantly less than that of its other sibling, Vicuna. Due to its use of LLaMA, only research use is permitted.
