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 TEST_REPORT.json from Maincode/matilda-jev-v1: direct link, hf CLI and curl.
- Browser
- Download file 2.64 kB
-
https://huggingface.co/Maincode/matilda-jev-v1/resolve/main/TEST_REPORT.json
- Command line
-
hf download hf://Maincode/matilda-jev-v1/TEST_REPORT.json
-
curl -L -o TEST_REPORT.json https://huggingface.co/Maincode/matilda-jev-v1/resolve/main/TEST_REPORT.json
2.64 kB
| { | |
| "created_utc": "2026-09-30T13:28:46.503362+00:00", | |
| "passed": true, | |
| "model_directory": "/shared/model-cache/matilda-jev-v1-matilda-config", | |
| "edition": "MATILDA configuration aliases for existing matilda-jev-v1 weights", | |
| "source_repository": "Maincode/matilda-jev-v1", | |
| "changed_configs": [ | |
| "config.json", | |
| "decision_config.json", | |
| "processor_config.json", | |
| "tokenizer_config.json" | |
| ], | |
| "upstream_name_absent_from_changed_configs": true, | |
| "weights_and_tokenizer_unchanged": true, | |
| "source_hash_verified_files": 15, | |
| "configuration_and_processor_tests": { | |
| "passed": true, | |
| "classes": [ | |
| "MatildaJevConfig", | |
| "MatildaJevProcessor", | |
| "MatildaJevTokenizer", | |
| "MatildaJevImageProcessor", | |
| "MatildaJevVideoProcessor" | |
| ], | |
| "checkpoint": "/shared/model-cache/matilda-jev-v1-matilda-config", | |
| "architecture_parameters_identical": true, | |
| "tokenization_identical": true, | |
| "image_preprocessing_identical": true, | |
| "config_processor_save_reload_passed": true | |
| }, | |
| "gpu_job": "71652", | |
| "device": "AMD Instinct MI355X", | |
| "loaded_classes": { | |
| "model": "MatildaJevModel", | |
| "config": "MatildaJevConfig", | |
| "processor": "MatildaJevProcessor" | |
| }, | |
| "questions_passed": 25, | |
| "questions_total": 25, | |
| "semantic_summary": { | |
| "smoke": { | |
| "correct": 18, | |
| "total": 18 | |
| }, | |
| "identity": { | |
| "correct": 6, | |
| "total": 6 | |
| }, | |
| "image": { | |
| "correct": 1, | |
| "total": 1 | |
| } | |
| }, | |
| "probability_parity": { | |
| "passed": true, | |
| "max_probability_delta": 0.0, | |
| "matched_requests": 23, | |
| "total_requests": 23, | |
| "tolerance": 1e-06 | |
| }, | |
| "training_backward_smoke": { | |
| "passed": true, | |
| "loss": 2.276871782669332e-05, | |
| "finite_nonzero_gradients": [ | |
| "readout", | |
| "embedding" | |
| ], | |
| "optimizer_step_performed": false | |
| }, | |
| "request_validation_checks": { | |
| "unknown_model_rejected": true, | |
| "empty_options_rejected": true, | |
| "overlong_input_rejected_without_truncation": true | |
| }, | |
| "load_seconds": 61.5, | |
| "peak_memory_gib_including_backward": 107.215, | |
| "limitations": [ | |
| "25-question inference smoke test, not a full benchmark rerun.", | |
| "Backward pass validated without optimizer update or a full continued-training run.", | |
| "Tested with Transformers 5.17.0 and PyTorch 2.14.0 on AMD MI355X; other backends are untested." | |
| ], | |
| "usage": "Use bundled runtime or AutoModel/AutoProcessor with trust_remote_code=True. AutoModel returns the backbone; bundled runtime also loads the decision head.", | |
| "colleague_readable": true, | |
| "independent_weight_files": true, | |
| "uploaded_at_validation": false | |
| } | |