Instructions to use baskra/tiny-random-Blip2Model-opt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baskra/tiny-random-Blip2Model-opt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="baskra/tiny-random-Blip2Model-opt")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("baskra/tiny-random-Blip2Model-opt") model = AutoModel.from_pretrained("baskra/tiny-random-Blip2Model-opt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from baskra/tiny-random-Blip2Model-opt: direct link, hf CLI and curl.
- Browser
- Download file 410 Bytes
-
https://huggingface.co/baskra/tiny-random-Blip2Model-opt/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://baskra/tiny-random-Blip2Model-opt/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/baskra/tiny-random-Blip2Model-opt/resolve/main/preprocessor_config.json
410 Bytes
| { | |
| "crop_size": 30, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "BlipImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 30, | |
| "width": 30 | |
| } | |
| } | |