Robotics
Transformers
Safetensors
English
glm4v
image-text-to-text
computer-vision
spatial-reasoning
vision-language-model
multi-modal
fine-tuned
Instructions to use hany01rye/TIGeR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hany01rye/TIGeR with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("hany01rye/TIGeR") model = AutoModelForImageTextToText.from_pretrained("hany01rye/TIGeR") - Notebooks
- Google Colab
- Kaggle
| { | |
| "crop_size": null, | |
| "data_format": "channels_first", | |
| "default_to_square": true, | |
| "device": null, | |
| "disable_grouping": null, | |
| "do_center_crop": null, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "Glm4vImageProcessorFast", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "input_data_format": null, | |
| "merge_size": 2, | |
| "patch_size": 14, | |
| "processor_class": "Glm4vProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null, | |
| "size": { | |
| "longest_edge": 9633792, | |
| "shortest_edge": 12544 | |
| }, | |
| "temporal_patch_size": 2 | |
| } | |