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
File size: 370 Bytes
d10ad42 9ea2421 d10ad42 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"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
}
|