Low-resource languages often lack sufficient training data for developing small language models, limiting the application of recent language acquisition benchmarks beyond English. Moreover, cross-language comparison is made harder given differences in training data composition and quality. We investigate whether machine translation can be used to create BabyLM-scale training corpora for low-resource languages by translating the English BabyLM corpus into Hindi and Telugu using IndicTrans2. We train GPT-BERT and GPT-Wee models and evaluate them using perplexity and linguistic benchmarks. GPT-BERT consistently outperforms GPT-Wee across both monolingual and bilingual settings. Although perplexity is lower on in-domain test data, this pattern is consistent across English and the translated languages, suggesting that the effect reflects domain similarity rather than artifacts introduced by translation. Furthermore, the models achieve competitive performance on linguistic evaluations relative to pretrained language models. These findings suggest that translation warrants further analysis to obtain data for developing and evaluating small language models in low-resource settings.