[Arctic_fly]

> Llama 7B wasn't up to the task fyi, producing very poor translations.

From what I've read and personally experimented with, none of the Llama 2 models are well-suited to translation in particular (they were mainly trained on English data). Still, there are a number of tasks that they're really good at if fine-tuned correctly, such as classification and data extraction.

> I believe that OpenAI priced GPT-3.5 aggressively cheap in order to make it a non-brainer to rely on them rather than relying on other vendors (even open source models).

I think you're definitely right about that, and in most cases just using GPT 3.5 for one-off tasks makes the most sense. I think when you get into production workflows that scale, that's when using a small fine-tuned models starts making more sense. You can drop the system prompt and get data in the format you'd expect it in, and train on GPT-4's output to sometimes get better accuracy than 3.5 would give you right off the bat. And keep in mind, while you can do the same thing with a fine-tuned 3.5 model, it's going to cost 8x the base 3.5 price per token.

[kelseyfrog]

Is that because translation is typically an encoder-decoder task and llama is decoder only or is there something else about it that makes the last difficult for llama?

[FeepingCreature]

If you don't make it learn other-language texts, it won't be able to speak that language.

[mikewang]

As I learned that 85% of its trainig data is English. Othere languanges composed of 15%.