This paper describes the submission of Team DevLake for the LoResMT 2026 Shared Task on Russian-Bashkir machine translation. We conducted a comprehensive comparative study of three distinct neural architectures: NLLB-200 (1.3B), M2M-100 (418M), and MarianMT (77M). To overcome hardware constraints, we employed parameter-efficient fine-tuning techniques (QLoRA) and extensive data filtering using a domain-specific BERT-based classifier. Our experiments demonstrate that the presence of the target language (Bashkir) in the model’s pre-training data is the decisive factor for performance. Our best system, a fine-tuned NLLB-200-1.3B model augmented with exact match retrieval, achieved a CHRF++ score of 52.67. We also report on negative results with custom tokenization for smaller models, providing insights into the limitations of vocabulary adaptation without extensive pre-training.