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supergemma4-26b-uncensored-v2
Hugging Face | GitHub | Launch Blog | Documentation License: Apache 2.0 | Authors: Google DeepMind Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: **E2B**, **E4B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI. Gemma 4 introduces key **capability and architectural advancements**: * **Reasoning** – All models in the family are designed as highly capable reasoners, with configurable thinking modes. ...

Repository: localaiLicense: gemma

gemma-4-26b-a4b-it
Google Gemma 4 26B-A4B-IT is an open-source multimodal Mixture-of-Experts model with 26B total parameters and 4B active parameters. It handles text and image input, generating text output, with a 256K context window and support for 140+ languages. The MoE architecture provides strong performance with efficient inference. Well-suited for question answering, summarization, reasoning, and image understanding tasks.

Repository: localaiLicense: apache-2.0

gemma-4-e2b-it
Google Gemma 4 E2B-IT is a lightweight open-source multimodal model with 5B total parameters and 2B effective parameters using selective parameter activation. It handles text and image input, generating text output, with a 256K context window and support for 140+ languages. Optimized for efficient execution on low-resource devices including mobile and laptops.

Repository: localaiLicense: apache-2.0

gemma-4-e4b-it
Google Gemma 4 E4B-IT is an open-source multimodal model with 8B total parameters and 4B effective parameters using selective parameter activation. It handles text and image input, generating text output, with a 256K context window and support for 140+ languages. Offers a good balance of performance and efficiency for deployment on consumer hardware.

Repository: localaiLicense: apache-2.0

gemma-4-31b-it
Google Gemma 4 31B-IT is the largest dense model in the Gemma 4 family with 31B parameters. It handles text and image input, generating text output, with a 256K context window and support for 140+ languages. Provides the highest quality outputs in the Gemma 4 lineup, well-suited for complex reasoning, summarization, and image understanding tasks.

Repository: localaiLicense: apache-2.0

gemma-4-e2b-it:sglang-mtp
Google Gemma 4 E2B-IT served by SGLang with Multi-Token Prediction (MTP) speculative decoding. The companion drafter google/gemma-4-E2B-it-assistant lets the target accept several tokens per step. Flags are a 1:1 transcription of the SGLang cookbook's MTP command (NEXTN algorithm, num_steps=5, num_draft_tokens=6, eagle_topk=1, mem_fraction_static=0.85). The E2B variant has 5B total / 2B effective parameters and targets the smaller end of consumer GPUs.

Repository: localaiLicense: gemma

gemma-4-e4b-it:sglang-mtp
Google Gemma 4 E4B-IT served by SGLang with Multi-Token Prediction (MTP) speculative decoding. The companion drafter google/gemma-4-E4B-it-assistant lets the target accept several tokens per step. Flags are a 1:1 transcription of the SGLang cookbook's MTP command (NEXTN algorithm, num_steps=5, num_draft_tokens=6, eagle_topk=1, mem_fraction_static=0.85). The E4B variant has 8B total / 4B effective parameters — the natural pick for consumer GPUs in the 16–24 GB range.

Repository: localaiLicense: gemma