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 E2B, E4B, and 12B) 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 five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from…
Open weights
apache-2.0
13.3B parameters
262,144 tokens
transformers
FP8-dynamic quantized variant of gemma-4-12B-it.
Open weights
apache-2.0
13B parameters
262,144 tokens
transformers
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 E2B, E4B, and 12B) 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 five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from…
Open weights
apache-2.0
12.6B parameters
131,072 tokens
transformers
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 E2B, E4B, and 12B) 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 five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from…
Open weights
apache-2.0
12.6B parameters
131,072 tokens
transformers
Self-quantized FP8 (dynamic) of google/gemma-4-12B-it — Google's encoder-free omni model (text + image + audio + video). Quantized and benchmarked on an NVIDIA DGX Spark (GB10, sm121a). TL;DR: 13 GB on disk (from 23 GB BF16), 15.9 tok/s on a GB10 via vLLM, all four modalities intact. Data-free — no calibration needed. If you want the smallest + fastest build, see the sibling NVFP4 weight-only repo. FP8 is the conservative choice (dynamic activations, no calibration, widest kernel support). I scored all three formats on MMLU (English, 57 subjects) and TMMLU+ (Traditional Chinese, 66 subjects) with lm-evaluation-harness, 5-shot, chat template applied, limit=30 (N ≈ 1,710 EN / 1,980 TC, ±~1.0…
Open weights
apache-2.0
12B parameters
131,072 tokens
transformers
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 E2B, E4B, and 12B) 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 five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from…
Open weights
apache-2.0
12B parameters
262,144 tokens
transformers
