Euria is hosted by Infomaniak. I’m not sure they tell what model they use but given the few times I saw a random Chinese character in the output I’d guess it isn’t an American model.
They also have a pay per token model that you can hook up to everything, that one’s just quite expensive for normal chat use, but at least you select the model you want to use.
Chinese being more token efficient is a myth, and seems to stem from the superficial fact that characters are only visually more space efficient.
The fact that each Chinese character takes up 3 bytes (as opposed to 1 byte of English), words in Chinese typically require compounds of several characters, and that tokenizers have a limited vocabulary limited to mostly English means that Chinese is actually token inefficient.
Euria is hosted by Infomaniak. I’m not sure they tell what model they use but given the few times I saw a random Chinese character in the output I’d guess it isn’t an American model.
They also have a pay per token model that you can hook up to everything, that one’s just quite expensive for normal chat use, but at least you select the model you want to use.
It’s beneficial for reasoning to have models trained in a few languages. Chinese is a good one because one character is one word is one token.
Chinese being more token efficient is a myth, and seems to stem from the superficial fact that characters are only visually more space efficient.
The fact that each Chinese character takes up 3 bytes (as opposed to 1 byte of English), words in Chinese typically require compounds of several characters, and that tokenizers have a limited vocabulary limited to mostly English means that Chinese is actually token inefficient.
No, Chinese Is Not More Token-Efficient Than English for LLMs | markhuang.ai - https://markhuang.ai/blog/chinese-token-myth
I didn’t say it’s more token efficient. I said training multiple languages improves reasoning.