Building one of the world’s smallest Gemma 4 models from Scratch (37M Parameters)

1. 1Read the original article on Medium to understand the context and motivation behind building a small Gemma 4 model
2. 2Build a small Gemma 4 model from scratch using a deep learning framework such as PyTorch or TensorFlow
3. 3Configure the model architecture to have 37M parameters and compare it to the original Gemma 4 model
4. 4Train the model on a smaller dataset to fine-tune its performance and evaluate its accuracy
5. 5Apply transfer learning to adapt the small Gemma 4 model to a specific task or domain

Machine learning engineers and researchers can benefit from this tutorial to learn about building and fine-tuning small LLM models

💡 Building small LLM models can be an effective way to understand and fine-tune their architecture

🤖 Build a small Gemma 4 model from scratch with 37M parameters! 💻