ravin kumar

It has been a transformative year for the Gemma family of models. In 2025, we have grown from 100 million to over 300 million downloads while demonstrating the transformative potential of open models, from defining state-of-the-art single-accelerator performance with Gemma 3 to advancing cancer research through the C2S Scale initiative. Since launching the Gemma 3 270M model, the number one request we’ve received from developers is for native function calling capabilities.

It has been a transformative year for the Gemma family of models. In 2025, we have grown from 100 million to over 300 million downloads while demonstrating the transformative potential of open models, from defining state-of-the-art single-accelerator performance with Gemma 3 to advancing cancer research through the C2S Scale initiative. Since launching the Gemma 3 270M model, the number one request we’ve received from developers is for native function calling capabilities.

Author: Ravin Kumar Paper: APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation GitHub: APTx Neuron | APTx Activation | APTx Neuron: MNIST Experimentation PyPI: pip install aptx-neuron Most neural networks today use a two-step neuron design: Linear computation — weighted sum of inputs. Non-linear activation — transformation using ReLU, Swish, Mish, etc. This works, but it’s structurally redundant — two separate steps that could be merged.