Instructions to use PhatcatDK/t5gemma-2-1b-1b-encoder-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PhatcatDK/t5gemma-2-1b-1b-encoder-only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PhatcatDK/t5gemma-2-1b-1b-encoder-only")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("PhatcatDK/t5gemma-2-1b-1b-encoder-only") model = AutoModelForMultimodalLM.from_pretrained("PhatcatDK/t5gemma-2-1b-1b-encoder-only") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pan_and_scan": null, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Gemma3ImageProcessor", | |
| "image_seq_length": 256, | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "pan_and_scan_max_num_crops": null, | |
| "pan_and_scan_min_crop_size": null, | |
| "pan_and_scan_min_ratio_to_activate": null, | |
| "processor_class": "Gemma3Processor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 896, | |
| "width": 896 | |
| } | |
| }, | |
| "image_seq_length": 256, | |
| "processor_class": "Gemma3Processor" | |
| } | |