Feature Extraction
Transformers
Safetensors
English
internvl_chat
multimodal
vision-language-model
image-aesthetics
image-quality
perception-benchmark
iaa
iqa
vqa
internvl
custom_code
Instructions to use widegather/unipercept-mirror with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use widegather/unipercept-mirror with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="widegather/unipercept-mirror", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("widegather/unipercept-mirror", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46bfb7a3f094c260dd050e16d65f09a238852c7cf654bdbd119c84b3afbecc4e
- Size of remote file:
- 11.4 MB
- SHA256:
- 9b536a0320fc6038134dd6bf6108025d41a5db099bb06b7d9bc2281bac37dbbb
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