High-throughput sequencing has revolutionized transcriptomic studies, and the synthesis of these diverse datasets holds significant potential for a deeper under- standing of cell biology. Recent advancements have introduced several promising techniques for building transcriptomic foundation models (TFMs), each emphasizing unique modeling decisions and demonstrating potential in handling the inherent challenges of high-dimensional, sparse data. However, despite their individual strengths, current TFMs still struggle to fully capture biologically meaningful representations, highlighting the need for further improvements. Recognizing that existing TFM approaches possess complementary strengths and weaknesses, a promising direction lies in the systematic exploration of various combinations of design, training, and evaluation methodologies. Thus, to accelerate progress in this field, we present bmfm-rna (shown in Figure 1), a comprehensive framework that not only facilitates this combinatorial exploration but is also inherently flexible and easily extensible to incorporate novel methods as the field continues to advance. This framework enables scalable data processing and features extensible transformer architectures. It supports a variety of input representations, pretraining objectives, masking strategies, domain-specific metrics, and model interpretation methods. Furthermore, it facilitates down- stream tasks such as cell type annotation, perturbation prediction, and batch effect correction on benchmark datasets. Models trained with the framework achieve performance comparable to scGPT, Geneformer and other TFMs on these downstream tasks. By open-sourcing this framework with strong performance, we aim to lower barriers for developing TFMs and invite the community to build more effective TFMs. bmfm-rna is available via Apache license at https://github.com/BiomedSciAI/biomed-multi-omic