Instructions to use ravikumar101/minicpm_4bit_ravi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ravikumar101/minicpm_4bit_ravi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ravikumar101/minicpm_4bit_ravi", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ravikumar101/minicpm_4bit_ravi", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from ravikumar101/minicpm_4bit_ravi: direct link, hf CLI and curl.
- Browser
- Download file 599 Bytes
-
https://huggingface.co/ravikumar101/minicpm_4bit_ravi/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://ravikumar101/minicpm_4bit_ravi/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/ravikumar101/minicpm_4bit_ravi/resolve/main/preprocessor_config.json
599 Bytes
| { | |
| "image_processor_type": "MiniCPMVImageProcessor", | |
| "auto_map": { | |
| "AutoProcessor": "processing_minicpmv.MiniCPMVProcessor", | |
| "AutoImageProcessor": "image_processing_minicpmv.MiniCPMVImageProcessor" | |
| }, | |
| "processor_class": "MiniCPMVProcessor", | |
| "max_slice_nums": 9, | |
| "scale_resolution": 448, | |
| "patch_size": 14, | |
| "image_feature_size": 96, | |
| "im_start": "<image>", | |
| "im_end": "</image>", | |
| "slice_start": "<slice>", | |
| "slice_end": "</slice>", | |
| "unk": "<unk>", | |
| "norm_mean": [0.5, 0.5, 0.5], | |
| "norm_std": [0.5, 0.5, 0.5], | |
| "version": 2.5 | |
| } |