Fine-Tuning Gemma Models in Hugging Face
Hugging Face Blog ● Covered by 2 sources
Google Deepmind's Gemma language models are now available via Hugging Face in 2 billion and 7 billion parameter sizes, optimized for fine-tuning using parameter-efficient techniques on both GPUs and TPUs. The article demonstrates Low-Rank Adaptation (LoRA) fine-tuning, which reduces memory requirements by training only adapter layers rather than all model weights, enabling users to adapt Gemma models on platforms like Colab or Kaggle. Users can now customize Gemma's responses for specific tasks—such as formatting quote completion—without the computational cost of full model retraining.
