Researchers have introduced MameLoshnLM, the first open-source 8B-parameter language model specifically engineered for the Yiddish language. To overcome the limitations of noisy, machine-translated multilingual datasets, the authors developed Oytser, a high-quality pretraining corpus that integrates contemporary web-native content with diverse literary materials. MameLoshnLM was developed by performing continued pretraining on the Llama 3.1 8B architecture using this specialized corpus. To evaluate the model's efficacy, the team created Kashes, a novel multi-task benchmark designed to test translation, linguistic analysis, information extraction, and language understanding within the Yiddish context. Experimental results demonstrate that MameLoshnLM outperforms existing open-source baselines of comparable scale across the Kashes benchmark. Furthermore, qualitative analyses indicate that MameLoshnLM more accurately captures essential lexical and morphological patterns of Yiddish compared to general-purpose multilingual models. The study concludes that the superior performance of MameLoshnLM highlights the systemic failure of web-scale multilingual data to support low-resource languages, providing a methodological blueprint for developing specialized models for other historically significant but digitally underrepresented languages.