Robotics
Transformers
Safetensors
paligemma
image-text-to-text
vision-language-action
chain-of-thought
embodied-ai
text-generation-inference
Instructions to use yinchenghust/deepthinkvla_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yinchenghust/deepthinkvla_base with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("yinchenghust/deepthinkvla_base") model = AutoModelForMultimodalLM.from_pretrained("yinchenghust/deepthinkvla_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_valid_processor_keys": [ | |
| "images", | |
| "do_resize", | |
| "size", | |
| "resample", | |
| "do_rescale", | |
| "rescale_factor", | |
| "do_normalize", | |
| "image_mean", | |
| "image_std", | |
| "return_tensors", | |
| "data_format", | |
| "input_data_format", | |
| "do_convert_rgb" | |
| ], | |
| "do_convert_rgb": null, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "SiglipImageProcessor", | |
| "image_seq_length": 256, | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "PaliGemmaProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } | |