Instructions to use HaochenWang/GAR-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HaochenWang/GAR-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="HaochenWang/GAR-1B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HaochenWang/GAR-1B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from HaochenWang/GAR-1B: direct link, hf CLI and curl.
- Browser
- Download file 274 Bytes
-
https://huggingface.co/HaochenWang/GAR-1B/resolve/main/processor_config.json
- Command line
-
hf download hf://HaochenWang/GAR-1B/processor_config.json
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curl -L -o processor_config.json https://huggingface.co/HaochenWang/GAR-1B/resolve/main/processor_config.json
274 Bytes
| { | |
| "patch_size": 14, | |
| "pooling_ratio": 2, | |
| "processor_class": "GARPerceptionLMProcessor", | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_perception_lm_fast.PerceptionLMImageProcessorFast", | |
| "AutoProcessor": "processing_gar.GARPerceptionLMProcessor" | |
| } | |
| } | |