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electra-base-discriminator
ELECTRA：以判别器而非生成器方式预训练文本编码器
gemma-4-31B-it
image-text-to-textHugging Face | GitHub | Launch Blog | Documentation License: Apache 2.0 | Authors: Google DeepMind
gemma-4-26B-A4B-it
image-text-to-textHugging Face | GitHub | Launch Blog | Documentation License: Apache 2.0 | Authors: Google DeepMind
gemma-4-E4B-it
any-to-anyHugging Face | GitHub | Launch Blog | Documentation License: Apache 2.0 | Authors: Google DeepMind
vit-base-patch16-224
image-classificationVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 22
gemma-4-E2B-it
any-to-anyHugging Face | GitHub | Launch Blog | Documentation License: Apache 2.0 | Authors: Google DeepMind
mobilebert-uncased
MobileBERT：面向资源受限设备的紧凑型任务无关BERT
gemma-3-12b-it
image-text-to-textimage-text-to-text
vit-base-patch16-224-in21k
image-feature-extractionVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Ima
gemma-3-4b-it
image-text-to-textimage-text-to-text
flan-t5-base
0. TL;DR 1. Model Details 2. Usage 3. Uses 4. Bias, Risks, and Limitations 5. Training Details 6. Evaluation 7. Environmental Impact 8. Citation 9. Model Card Authors
siglip-so400m-patch14-384
zero-shot-image-classificationSigLIP model pre-trained on WebLi at resolution 384x384. It was introduced in the paper Sigmoid Loss for Language Image Pre-Training by Zhai et al. and first released in this repository.