Feature Extraction
Transformers
Safetensors
English
matilda_jev
decision-model
typed-decisions
jev
maincode
custom_code
Instructions to use Maincode/matilda-jev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/matilda-jev-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maincode/matilda-jev-v1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Maincode/matilda-jev-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download CPU_TEST.json from Maincode/matilda-jev-v1: direct link, hf CLI and curl.
- Browser
- Download file 370 Bytes
-
https://huggingface.co/Maincode/matilda-jev-v1/resolve/main/CPU_TEST.json
- Command line
-
hf download hf://Maincode/matilda-jev-v1/CPU_TEST.json
-
curl -L -o CPU_TEST.json https://huggingface.co/Maincode/matilda-jev-v1/resolve/main/CPU_TEST.json
370 Bytes
| { | |
| "passed": true, | |
| "classes": [ | |
| "MatildaJevConfig", | |
| "MatildaJevProcessor", | |
| "MatildaJevTokenizer", | |
| "MatildaJevImageProcessor", | |
| "MatildaJevVideoProcessor" | |
| ], | |
| "checkpoint": ".", | |
| "architecture_parameters_identical": true, | |
| "tokenization_identical": true, | |
| "image_preprocessing_identical": true, | |
| "config_processor_save_reload_passed": true | |
| } | |