Why does this model have powerful text generation capabilities for various countries, and the results are very good, most likely in English?
Why does this model have powerful text generation capabilities for various countries, and the results are very good, most likely in English?
Gemma's powerful text generation performance can be due to its strong model decoder-only transformer architecture which includes a large context length of 8192 tokens, deep neural networks, attention mechanisms, and post-norm and pre-norm with RMSNorm. The model is pre-trained on a large amount of training datasets like web documents primarily on English-language content, coding, and mathematical text which makes it english dominance in datasets. Also, Gemma-2-9b-it is instruction tuned(it) model which is fine-tuned on instructions datasets to follow prompts and generate more relevant and informative responses.
You can refer the Gemma-2 technical report for more detailed understanding on Gemma architecture. Thank you.