Instructions to use apus-ailab/APUS-OpenJev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apus-ailab/APUS-OpenJev-v1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("apus-ailab/APUS-OpenJev-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "scope": "80 fixed requests, both explicit efforts; merged-model comparison, not independent generalization evidence", | |
| "checkpoint_step": 3000, | |
| "base_id": "Qwen/Qwen3.5-9B", | |
| "base_revision": "c202236235762e1c871ad0ccb60c8ee5ba337b9a", | |
| "comparison": { | |
| "passed": false, | |
| "gate": "identical160_argmax_and_max_probability_delta_le_0.05", | |
| "depths": { | |
| "16": { | |
| "before_correct": 63, | |
| "after_correct": 63, | |
| "changed_decisions": [], | |
| "max_probability_abs_difference": 0.08810508251190186, | |
| "mean_probability_abs_difference": 0.0019464968157012663, | |
| "max_logit_abs_difference": 0.265625, | |
| "mean_logit_abs_difference": 0.02963104248046875 | |
| }, | |
| "32": { | |
| "before_correct": 68, | |
| "after_correct": 68, | |
| "changed_decisions": [], | |
| "max_probability_abs_difference": 0.062176525592803955, | |
| "mean_probability_abs_difference": 0.001304240933347387, | |
| "max_logit_abs_difference": 0.25, | |
| "mean_logit_abs_difference": 0.03828125 | |
| } | |
| }, | |
| "before_sha256": "28412c70d8b456b35bb884a2fba4e85801b0135fc7957190927994a56d268a6f", | |
| "after_sha256": "3f01a868d4fba91a8a1749df37a87f10f0874823eec5a978291c93231773c365", | |
| "verified_at_unix": 1789984423.7235599 | |
| }, | |
| "runtime_validation": { | |
| "status": "passed", | |
| "decisions": 160, | |
| "max_probability_delta": 0.0, | |
| "no_jev_import": true, | |
| "long_input_rejected": true, | |
| "invalid_effort_rejected": true, | |
| "text_scope": "Execution smoke only; not TYPE accuracy or speed validation", | |
| "source_sha256": "70d686828090965063966dddac6d8454cf981c6614a406c1647617986e534c5d" | |
| }, | |
| "decision_release": { | |
| "passed": true, | |
| "scope": "Independent BF16 merged variant, frozen80 decision parity; NOT a probability-equivalent replacement", | |
| "post_observation_scope_amendment": true, | |
| "original_numerical_gate_passed": false, | |
| "original_gate": "identical160_argmax_and_max_probability_delta_le_0.05", | |
| "original_comparison_sha256": "d2ea8a7af10f09fdfde166384ffb18fb5a19ebf1a1281c58cb72806a45878c41", | |
| "decision_agreement": 160, | |
| "total_decisions": 160, | |
| "independent_questions": 80, | |
| "probability_calibration_transfer_validated": false, | |
| "automatic_routing_validated": false, | |
| "required_followup": "Recalibrate all probability, rejection and routing thresholds; independently evaluate new tasks", | |
| "depths": { | |
| "16": { | |
| "before_correct": 63, | |
| "after_correct": 63, | |
| "changed_decisions": [], | |
| "max_probability_abs_difference": 0.08810508251190186, | |
| "mean_probability_abs_difference": 0.0019464968157012663, | |
| "max_logit_abs_difference": 0.265625, | |
| "mean_logit_abs_difference": 0.02963104248046875 | |
| }, | |
| "32": { | |
| "before_correct": 68, | |
| "after_correct": 68, | |
| "changed_decisions": [], | |
| "max_probability_abs_difference": 0.062176525592803955, | |
| "mean_probability_abs_difference": 0.001304240933347387, | |
| "max_logit_abs_difference": 0.25, | |
| "mean_logit_abs_difference": 0.03828125 | |
| } | |
| }, | |
| "publication_requires": "Further portable-runtime160, weight arithmetic, full file hashes and fresh HF reload gates" | |
| }, | |
| "reviewed_variant": {}, | |
| "decision_parity_passed": true, | |
| "decision_agreement": 160, | |
| "weight_arithmetic_status": "passed", | |
| "raw_inputs_included": false | |
| } | |