Welcome EmbeddingGemma, Google's new efficient embedding model
Google has released EmbeddingGemma, a new embedding model announced via the Hugging Face blog. The model appears to be positioned as an efficient option for generating text embeddings, likely derived from or related to the Gemma model family. Details on architecture, benchmarks, and use cases are expected in the full post.
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Welcome Gemma 2 - Google's new open LLM
Google released Gemma 2, a new open-weights large language model, announced via the Hugging Face blog. The post covers integration with the Hugging Face ecosystem and highlights the model's capabilities. Gemma 2 represents Google's continued investment in open-weight model releases to compete in the open-source LLM space.
Welcome Gemma 3: Google's All-New Multimodal, Multilingual, Long-Context Open LLM
Google has released Gemma 3, a new family of open-weights large language models featuring multimodal capabilities, multilingual support, and extended context windows. The Hugging Face blog post introduces the model family and its key features. Gemma 3 represents a significant update to Google's open-weights model line, expanding beyond text-only capabilities to include vision and broader language coverage.
Welcome Gemma - Google's new open LLM
Google released Gemma, a family of open-weight large language models, announced via the Hugging Face blog. The models are positioned as Google's entry into the open-weights LLM space, following the success of models like Llama 2. This release marks a significant strategic move by Google to compete in the open-source AI ecosystem.
Welcome PaliGemma 2 – New vision language models by Google
Google has released PaliGemma 2, a new family of vision-language models announced via the Hugging Face blog. The release follows the original PaliGemma and represents an updated generation of Google's open-weights multimodal models. The blog post covers model capabilities, sizes, and integration with the Hugging Face ecosystem.
Gemma 3n Fully Available in the Open-Source Ecosystem
Google's Gemma 3n model has been integrated into the open-source ecosystem via Hugging Face, making it broadly accessible for developers and researchers. The announcement covers availability of the model weights and tooling support within the Hugging Face platform. Gemma 3n is designed for efficient on-device inference, targeting mobile and edge deployment scenarios. This release extends the open-weights frontier model landscape with a multimodal-capable, efficiency-focused architecture.
Welcome Gemma 4: Frontier Multimodal Intelligence on Device
Google has released Gemma 4, a new open-weights multimodal model family announced via the Hugging Face blog. The release positions Gemma 4 as capable of frontier-level multimodal intelligence while being deployable on-device. As a tier-2 source commentary, the post likely covers model capabilities, availability on Hugging Face Hub, and integration details.
T5Gemma: A new collection of encoder-decoder Gemma models
DeepMind has announced T5Gemma, a new collection of encoder-decoder large language models under the Gemma family. The release extends the Gemma model line beyond its existing decoder-only architecture to include encoder-decoder variants, following the T5 paradigm. Further technical details are sparse in the announcement but the models represent a notable architectural expansion of the open Gemma ecosystem.
Introducing Gemma 3
Google DeepMind has released Gemma 3, described as the most capable model runnable on a single GPU or TPU. The announcement comes from DeepMind's official blog, indicating a new generation of the open-weights Gemma model family. Specific capability details, parameter counts, and benchmark results are not included in the provided body text.