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Gemma is a family of open-weight language models released by Google for text generation and related NLP tasks.
The ability to run local agents with access to proprietary data doesn't has particular benefits. For one, while these models are open, there is still a degree of lock-in. Any agents built with these models will have system prompts and tooling that have been tuned to that specific architecture. It's about being able to reach markets that bigger models can't, Buss explained. "If you have people developing using your technologies and approaches and IP, they're more likely to migrate up and stay in your ecosystem," he said. "It's a matter of basically having a product at the entry point... If you
The AI divide putting open weights models in spotlight… However, this may be worthwhile if running on devices with slower memory, like a notebook or consumer graphics card. Both of these models feature a 256,000-token context window, making them appropriate for local code assistants, a use case Google was keen to highlight in its launch announcement. …
… The same logic could be applied in a disaggregated fashion, where a routing model running locally could direct prompts requiring access to proprietary data to a local LLM, while less sensitive requests could be offloaded to an API provider. …