Zero-Shot Classification
Transformers
Safetensors
English
matilda_jev
feature-extraction
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("zero-shot-classification", model="Maincode/matilda-jev-v1", trust_remote_code=True)# pip install -U transformers accelerate # 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
Update TEST_REPORT.json
Browse files- TEST_REPORT.json +2 -4
TEST_REPORT.json
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@@ -1,7 +1,7 @@
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{
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"created_utc": "2026-09-30T13:28:46.503362+00:00",
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"passed": true,
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"model_directory": "
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"edition": "MATILDA configuration aliases for existing matilda-jev-v1 weights",
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"source_repository": "Maincode/matilda-jev-v1",
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"changed_configs": [
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"MatildaJevImageProcessor",
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"MatildaJevVideoProcessor"
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],
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"checkpoint": "
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"architecture_parameters_identical": true,
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"tokenization_identical": true,
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"image_preprocessing_identical": true,
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"config_processor_save_reload_passed": true
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},
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"gpu_job": "71652",
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"device": "AMD Instinct MI355X",
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"loaded_classes": {
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"model": "MatildaJevModel",
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"Tested with Transformers 5.17.0 and PyTorch 2.14.0 on AMD MI355X; other backends are untested."
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],
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"usage": "Use bundled runtime or AutoModel/AutoProcessor with trust_remote_code=True. AutoModel returns the backbone; bundled runtime also loads the decision head.",
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"colleague_readable": true,
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"independent_weight_files": true,
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"uploaded_at_validation": false
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}
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{
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"created_utc": "2026-09-30T13:28:46.503362+00:00",
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"passed": true,
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"model_directory": ".",
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"edition": "MATILDA configuration aliases for existing matilda-jev-v1 weights",
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"source_repository": "Maincode/matilda-jev-v1",
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"changed_configs": [
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"MatildaJevImageProcessor",
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"MatildaJevVideoProcessor"
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],
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"checkpoint": ".",
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"architecture_parameters_identical": true,
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"tokenization_identical": true,
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"image_preprocessing_identical": true,
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"config_processor_save_reload_passed": true
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},
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"device": "AMD Instinct MI355X",
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"loaded_classes": {
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"model": "MatildaJevModel",
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"Tested with Transformers 5.17.0 and PyTorch 2.14.0 on AMD MI355X; other backends are untested."
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],
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"usage": "Use bundled runtime or AutoModel/AutoProcessor with trust_remote_code=True. AutoModel returns the backbone; bundled runtime also loads the decision head.",
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"independent_weight_files": true,
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"uploaded_at_validation": false
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}
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