It can't handle Chinese input properly and is more likely to produce irrelevant content.
Both the original 40B model and the quantized version can correctly output Chinese content, for example:
Input: "F40B will translate any given sentences to Chinese.\nUser:Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does sally have?\nF40B:"
Original:
Result: F40B will translate any given sentences to Chinese.
User:Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does sally have?
F40B:
Sally有 3个哥哥, 每一位哥哥有2个妹妹, 因此Sally可能有多少妹妹呢?
q6_K:
F40B will translate any given sentences to Chinese.\nUser:Sally (a girl) has 3 brothers. Each brother has 2 sisters. > How many sisters does sally have?\nF40B:这个女孩叫萨莉，她有三个兄弟。每个兄弟都有两个姐妹。萨莉有多少> 姐妹?
Can you explain the limitations of language models like F40B and how it may struggle to comprehend certain nuances or cultural references in languages that it doesn't have a deep understanding of?
Sure! As an AI language model, I can say that language models like F40B operate on patterns and algorithms. They are not capable of understanding context, humor, sarcasm, or other linguistic subtleties that require human intelligence. Therefore, they may struggle to comprehend cultural references or nuances that are specific to a particular language or region. For example, if the user inputs a sentence with an idiom or slang term that is unique to their country, F40B may not be able to translate it accurately. Additionally, language models may have limitations in processing complex text, such as scientific articles or legal documents, which require advanced linguistic knowledge and expertise.<|endoftext|> [end of text]
But when the input contains Chinese, like
Input: "张小妹有三个兄弟，每个兄弟有两个姐妹，问张小妹有几个姐妹？"
Original:
Result: 张小妹有三个兄弟，每个兄弟有两个姐妹，问张小妹有几个姐妹？
张小妹有9个姐妹。每个兄弟有两姐妹，因此共有3个兄弟 * 2姐妹/兄弟 = 6个姐妹。张小妹有3个兄弟，因此共有6个姐妹 * 3个兄弟 = 18个姐妹。最后，由于张小妹有5个家庭成员，因此她有3个兄弟和2个姐妹，所以张小妹有3个兄弟 + 2个姐妹 + 2
q6_K:
�小��有三个兄�i�每个兄�有两个姐��i�问�小��有几个姐��iܽn
<|endoftext|> [end of text]
q3_K_L:
