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4793 x 9915 | 47522595 | longmult_4dig | longmult | [
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4507 x 8243 | 37151201 | longmult_4dig | longmult | [
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} | 2025-11-09 | test | main | TAUR-dev/M-0918__random_3args_grpo-rl | 0 | eval_rl | 1 | 2025-11-09T12:00:33.623590 | TAUR-dev/D-EVAL__standard_eval_v3__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig-eval_rl |
7645 x 2867 | 21918215 | longmult_4dig | longmult | [
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5560 x 2589 | 14394840 | longmult_4dig | longmult | [
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5291 x 7587 | 40142817 | longmult_4dig | longmult | [
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7176 x 7208 | 51724608 | longmult_4dig | longmult | [
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Experiment Tracker: FinEval_16k_fulleval_3args_Random-RL-longmult_4dig
Experiment Description: Evaluation experiment for task longmult_4dig from FinEval_16k_fulleval_3args_Random-RL
Start Time: 2025-11-09T11:42:11.211595
Tracker Dataset: TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig__v1
Stages Completed
Total stages: 1
Models Created
Dataset Configurations
This tracker dataset contains the following configurations with immediate upload as stages complete:
Training Data (Complete Datasets)
Hyperparameters (Complete Configurations)
Logs (Stage-Specific)
Evaluation Results (Complete with Annotations)
Metadata
- experiment_metadata: Timeline and stage information
Usage
Load specific configurations with:
from datasets import load_dataset
# Load experiment metadata
metadata = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig__v1', 'experiment_metadata')
# Load complete training datasets
sft_data = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig__v1', 'training_data__sft')
sft_metadata = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig__v1', 'training_data__sft_metadata')
# Load complete configurations
sft_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig__v1', 'hyperparameters__sft')
rl_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig__v1', 'hyperparameters__rl')
# Load stage-specific logs
sft_logs = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig__v1', 'logs__sft')
rl_logs = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig__v1', 'logs__rl')
# Load evaluation results with annotations
sft_eval_results = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig__v1', 'evals_eval_sft')
rl_eval_results = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_Random-RL-longmult_4dig__v1', 'evals_eval_rl')
Models
Registry
All models from this experiment are automatically registered in the SkillFactory Model Registry with:
- Complete training configuration (hyperparameters, datasets, methods)
- Experiment lineage (links back to this tracker dataset)
- Stage-specific metadata (SFT vs RL training details)
- Structured input data references (training datasets and configurations)
Registry entries follow the naming pattern: Model - FinEval_16k_fulleval_3args_Random-RL-longmult_4dig - {stage_name} - {SFT/RL}
Generated by SkillFactory Experiment Management System All artifacts uploaded immediately as stages complete with perfect data provenance
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