Constructed for builders and researchers
Gemma 2 isn’t solely extra highly effective, it is designed to extra simply combine into your workflows:
Open and accessible: Similar to the unique Gemma fashions, Gemma 2 is on the market below our commercially-friendly Gemma license, giving builders and researchers the flexibility to share and commercialize their improvements.Broad framework compatibility: Simply use Gemma 2 together with your most popular instruments and workflows due to its compatibility with main AI frameworks like Hugging Face Transformers, and JAX, PyTorch and TensorFlow by way of native Keras 3.0, vLLM, Gemma.cpp, Llama.cpp and Ollama. As well as, Gemma is optimized with NVIDIA TensorRT-LLM to run on NVIDIA- accelerated infrastructure or as an NVIDIA NIM inference microservice. You’ll be able to fine-tune at this time with Keras and Hugging Face. We’re actively working to allow further parameter-efficient fine-tuning choices.Easy deployment: Beginning subsequent month, Google Cloud prospects will have the ability to simply deploy and handle Gemma 2 on Vertex AI.
Discover the brand new Gemma Cookbook, a group of sensible examples and recipes to information you thru constructing your personal purposes and fine-tuning Gemma 2 fashions for particular duties. Uncover the right way to simply use Gemma together with your tooling of selection, together with for frequent duties like retrieval-augmented technology.
Accountable AI improvement
We’re dedicated to offering builders and researchers with the assets they should construct and deploy AI responsibly, together with by way of our Accountable Generative AI Toolkit. The just lately open-sourced LLM Comparator helps builders and researchers with in-depth analysis of language fashions. Beginning at this time, you should use the companion Python library to run comparative evaluations together with your mannequin and information, and visualize the ends in the app. Moreover, we’re actively engaged on open sourcing our textual content watermarking know-how, SynthID, for Gemma fashions.
When coaching Gemma 2, we adopted our sturdy inner security processes, filtering pre-training information and performing rigorous testing and analysis towards a complete set of metrics to determine and mitigate potential biases and dangers. We publish our outcomes on a big set of public benchmarks associated to security and representational harms.