Recent advances in Multilingual Large Language Models (MLLMs) have improved cross-lingual conversational capabilities, yet generating reliable, institutionally usable text in low-resource languages like Bangla remains a critical bottleneck, particularly for compact, low-resource LLMs. Beyond surface fluency, state-of-the-art and small open-weight models frequently exhibit deeper generation failures in Bangla: incoherent or off-topic output, structural misformatting, and inconsistent honorific registers, that render text unusable in formal contexts. To address this, we introduce BLADE (BangLa Application and DialoguE generation), a culturally aligned instruction-tuning dataset and benchmarking framework comprising 4,196 curated interaction pairs. We use this resource to fine-tune and evaluate compact open-weight architectures, including DeepSeek-8B, LLaMA-3.2-3B, Qwen2, and TigerLLM-1B, via LoRA adapters under 4-bit NormalFloat (NF4) quantization on limited hardware. Our evaluations show that targeted fine-tuning on BLADE yields substantial improvements in generation reliability, structural fidelity, and register consistency even for the smallest models, establishing a rigorous benchmark for closing generation and pragmatic gaps in low-resource multilingual text generation. Code and dataset: https://github.com/ashuvo25/Bangla_Application_LLM