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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
61662 x 44241 | 2727988542 | longmult_5dig | longmult | [
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98270 x 48469 | 4763048630 | longmult_5dig | longmult | [
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85574 x 60628 | 5188180472 | longmult_5dig | longmult | [
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87369 x 14157 | 1236882933 | longmult_5dig | longmult | [
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28441 x 57792 | 1643662272 | longmult_5dig | longmult | [
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81212 x 52484 | 4262330608 | longmult_5dig | longmult | [
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75436 x 27876 | 2102853936 | longmult_5dig | longmult | [
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96109 x 64265 | 6176444885 | longmult_5dig | longmult | [
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"content": "Answer the following problem. Explain your reasoning step by step. When you are finished, give your answer in this format: <answer>(your answer)</answer>.\n\n# Problem\nWhat is the solution to the multiplication equation below?\n96109 x 64265\n\nGive your answer in the following format:\n<answer>\... | [] | [] | {"metadata": "{\"arg1\": 96109, \"arg2\": 64265, \"solution\": 6176444885}", "task_source": "longmult"} | test | {"arg1": 96109, "arg2": 64265, "solution": 6176444885} | [
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30971 x 61764 | 1912892844 | longmult_5dig | longmult | [
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"content": "Answer the following problem. Explain your reasoning step by step. When you are finished, give your answer in this format: <answer>(your answer)</answer>.\n\n# Problem\nWhat is the solution to the multiplication equation below?\n30971 x 61764\n\nGive your answer in the following format:\n<answer>\... | [] | [] | {"metadata": "{\"arg1\": 30971, \"arg2\": 61764, \"solution\": 1912892844}", "task_source": "longmult"} | test | {"arg1": 30971, "arg2": 61764, "solution": 1912892844} | [
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77674 x 53129 | 4126741946 | longmult_5dig | longmult | [
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"content": "Answer the following problem. Explain your reasoning step by step. When you are finished, give your answer in this format: <answer>(your answer)</answer>.\n\n# Problem\nWhat is the solution to the multiplication equation below?\n77674 x 53129\n\nGive your answer in the following format:\n<answer>\... | [] | [] | {"metadata": "{\"arg1\": 77674, \"arg2\": 53129, \"solution\": 4126741946}", "task_source": "longmult"} | test | {"arg1": 77674, "arg2": 53129, "solution": 4126741946} | [
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93749 x 29118 | 2729783382 | longmult_5dig | longmult | [
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46278 x 49891 | 2308855698 | longmult_5dig | longmult | [
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13031 x 40855 | 532381505 | longmult_5dig | longmult | [
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83847 x 17672 | 1481744184 | longmult_5dig | longmult | [
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23722 x 40011 | 949140942 | longmult_5dig | longmult | [
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10119 x 51970 | 525884430 | longmult_5dig | longmult | [
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57470 x 32279 | 1855074130 | longmult_5dig | longmult | [
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41846 x 64856 | 2713964176 | longmult_5dig | longmult | [
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Experiment Tracker: FinEval_16k_fulleval_3args_bolt-longmult_5dig
Experiment Description: Evaluation experiment for task longmult_5dig from FinEval_16k_fulleval_3args_bolt
Start Time: 2025-10-27T13:31:22.157514
Tracker Dataset: TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_bolt-longmult_5dig__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_bolt-longmult_5dig__v1', 'experiment_metadata')
# Load complete training datasets
sft_data = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_bolt-longmult_5dig__v1', 'training_data__sft')
sft_metadata = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_bolt-longmult_5dig__v1', 'training_data__sft_metadata')
# Load complete configurations
sft_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_bolt-longmult_5dig__v1', 'hyperparameters__sft')
rl_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_bolt-longmult_5dig__v1', 'hyperparameters__rl')
# Load stage-specific logs
sft_logs = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_bolt-longmult_5dig__v1', 'logs__sft')
rl_logs = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_bolt-longmult_5dig__v1', 'logs__rl')
# Load evaluation results with annotations
sft_eval_results = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_bolt-longmult_5dig__v1', 'evals_eval_sft')
rl_eval_results = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3args_bolt-longmult_5dig__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_bolt-longmult_5dig - {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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