Instructions to use tiny-random/minicpm-v-4.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiny-random/minicpm-v-4.6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tiny-random/minicpm-v-4.6")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tiny-random/minicpm-v-4.6") model = AutoModelForMultimodalLM.from_pretrained("tiny-random/minicpm-v-4.6") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "downsample_mode": "16x", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "MiniCPMV4_6ImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_slice_nums": 9, | |
| "patch_size": 14, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "scale_resolution": 448, | |
| "slice_mode": true, | |
| "use_image_id": true | |
| }, | |
| "processor_class": "MiniCPMV4_6Processor", | |
| "video_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_sample_frames": true, | |
| "downsample_mode": "16x", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_num_frames": 128, | |
| "max_slice_nums": 9, | |
| "patch_size": 14, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_metadata": false, | |
| "scale_resolution": 448, | |
| "slice_mode": true, | |
| "stack_frames": 1, | |
| "use_image_id": true, | |
| "video_processor_type": "MiniCPMV4_6VideoProcessor" | |
| } | |
| } | |