Tag

unsloth/gemma-4-12b-it-GGUF

A GGUF-quantized, locally runnable build of Gemma 4 12B Unified (image-text-to-text) packaged by unsloth; preserves multimodal (image/audio) input support under an Apache-2.0 license and is compatible with common GGUF runtimes and Unsloth Studio.

google/gemma-4-12B-it-qat-q4_0-gguf

Provides a GGUF-ready QAT (Q4_0) quantized build of Gemma 4 12B that preserves near-bfloat16 quality while reducing memory footprint for local inference; compatible with Transformers-based and GGUF runtimes.

unsloth/gemma-4-12B-it-qat-GGUF

GGUF-format QAT (quantization-aware training) build of Gemma 4 12B that reduces memory needs for local or lightweight inference while preserving near bfloat16 quality. Ready for any-to-any conversational pipelines and ecosystem deployment.

DiffusionGemma 26B A4B

Generates text from interleaved text, image, and short-video inputs using discrete diffusion and block‑autoregressive multi‑canvas sampling; built on a sparse MoE (8/128) Gemma 4 backbone and optimized for low‑latency inference and very long contexts (up to 256K tokens).

Video Generation Models are General-Purpose Vision Learners

Uses large-scale text-to-video generative pretraining to create GenCeption, a feed-forward perception model that performs diverse vision tasks from text instructions—depth, surface normals, camera pose, referring segmentation, and 3D keypoints—often matching or surpassing specialized models while requiring far less task-specific data.