This work proposes an open-source framework that leverages both the Zfh (scalar float16) and the Zvfh (vector float16) extensions to enable complete on-device training on resource-constrained RISC-V single-core. On top of reducing the memory footprint by about 50% as compared to using float32, our approach facilitates transfer learning and fine-tuning scenarios by incorporating layer-freezing capabilities. Our work builds onto AIfES an open-source, modular and generic DNN training and inference framework for embedded systems that can be extended with custom hardware-specific functions.