Fine-Tuning Gemma Models in Hugging Face
Hugging Face published a guide on fine-tuning Google's Gemma models using parameter-efficient fine-tuning (PEFT) techniques. The post covers practical workflows for adapting Gemma to downstream tasks within the Hugging Face ecosystem. This represents part of the broader tooling support rollout accompanying Gemma's release in February 2024.
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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.
Parameter-Efficient Fine-Tuning using 🤗 PEFT
Hugging Face introduces the PEFT library, which enables parameter-efficient fine-tuning of large language models using techniques such as LoRA, prefix tuning, and prompt tuning. The library allows practitioners to adapt large pretrained models to downstream tasks while updating only a small fraction of model parameters, dramatically reducing compute and memory requirements. This lowers the barrier to fine-tuning frontier-scale models on consumer hardware.
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 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.
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.
Google releases Gemma 2 2B, ShieldGemma and Gemma Scope
Google released three new additions to the Gemma ecosystem: Gemma 2 2B, a small open-weights language model; ShieldGemma, a safety-focused classifier model; and Gemma Scope, an interpretability toolset. These releases expand the Gemma family with a smaller, more accessible model alongside dedicated safety and interpretability infrastructure. The announcement was published on the Hugging Face blog, indicating integration with the HF ecosystem.
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.