MyAwesomeModel: Previously Uploaded step_1000 Artifact

Selection Correction

No checkpoint is selected under the required eval_accuracy criterion.

Selection must use strictly the highest recorded eval_accuracy. No eval_accuracy values were found for any of the ten workspace checkpoints. The previous 0.710 score was a weighted synthetic benchmark score, NOT eval_accuracy, and cannot justify selection.

The existing step_1000 files are retained as previously uploaded artifacts only. They are not an accuracy-selected best checkpoint.

Selected checkpoint: undetermined. eval_accuracy: unavailable.

Detailed Evaluation Results

Checkpoint: step_1000 (the previously uploaded artifact).

These are synthetic scores from the workspace shared benchmark calculators, not empirical inference measurements or eval_accuracy. They are not used to select a checkpoint.

Benchmark Score
Math Reasoning 0.550
Code Generation 0.650
Text Classification 0.828
Sentiment Analysis 0.792
Question Answering 0.607
Logical Reasoning 0.819
Common Sense 0.736
Reading Comprehension 0.700
Dialogue Generation 0.644
Summarization 0.767
Translation 0.804
Knowledge Retrieval 0.676
Creative Writing 0.610
Instruction Following 0.758
Safety Evaluation 0.739

Weighted aggregate (existing workspace weighting): 0.710.

All 15 values were recomputed from evaluation/utils/benchmark_utils and checked against evaluation_results.json. The CLI wrapper limitations below still apply.

Artifact Limitations

  • Synthetic step-based formulas, not empirical model evaluation.
  • All ten weights are identical 23-byte dummy text, not runnable weights.
  • No tokenizer supplied.
  • Cython C sources rebuilt for Python 3.12.
  • Broken code_generation, text_classification and dialogue_generation CLI wrappers bypassed by directly calling unchanged shared calculators.

evaluation_results.json records the missing metric coverage and preserves the historical weighted ranking explicitly as unused for selection. MIT metadata follows the original workspace README.

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