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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
756 x 322 | 243432 | longmult_3dig | longmult | [
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} | 2025-11-27 | test | main | TAUR-dev/M-1119_newmodels__qwen7b_sft1k_ours-rl@checkpoint-step-100 | 0 | eval_rl | 1 | 2025-11-27T19:21:30.815292 | TAUR-dev/D-EVAL__standard_eval_v3__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig-eval_rl |
726 x 135 | 98010 | longmult_3dig | longmult | [
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520 x 133 | 69160 | longmult_3dig | longmult | [
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} | 2025-11-27 | test | main | TAUR-dev/M-1119_newmodels__qwen7b_sft1k_ours-rl@checkpoint-step-100 | 0 | eval_rl | 1 | 2025-11-27T19:21:30.815307 | TAUR-dev/D-EVAL__standard_eval_v3__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig-eval_rl |
420 x 680 | 285600 | longmult_3dig | 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?\n420 x 680\n\nGive your answer in the following format:\n<answer>\n(yo... | [] | [] | {"metadata": "{\"arg1\": 420, \"arg2\": 680, \"solution\": 285600}", "task_source": "longmult"} | test | {"arg1": 420, "arg2": 680, "solution": 285600} | [
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} | 2025-11-27 | test | main | TAUR-dev/M-1119_newmodels__qwen7b_sft1k_ours-rl@checkpoint-step-100 | 0 | eval_rl | 1 | 2025-11-27T19:21:30.815310 | TAUR-dev/D-EVAL__standard_eval_v3__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig-eval_rl |
835 x 429 | 358215 | longmult_3dig | 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?\n835 x 429\n\nGive your answer in the following format:\n<answer>\n(yo... | [] | [] | {"metadata": "{\"arg1\": 835, \"arg2\": 429, \"solution\": 358215}", "task_source": "longmult"} | test | {"arg1": 835, "arg2": 429, "solution": 358215} | [
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593 x 797 | 472621 | longmult_3dig | longmult | [
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451 x 158 | 71258 | longmult_3dig | longmult | [
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308 x 984 | 303072 | longmult_3dig | longmult | [
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600 x 871 | 522600 | longmult_3dig | longmult | [
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End of preview. Expand in Data Studio
Experiment Tracker: FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig
Experiment Description: Evaluation experiment for task longmult_3dig from FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL
Start Time: 2025-11-27T19:00:51.153917
Tracker Dataset: TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig__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_3arg_OT_ours_1k_atstep100-RL-longmult_3dig__v1', 'experiment_metadata')
# Load complete training datasets
sft_data = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig__v1', 'training_data__sft')
sft_metadata = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig__v1', 'training_data__sft_metadata')
# Load complete configurations
sft_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig__v1', 'hyperparameters__sft')
rl_hyperparams = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig__v1', 'hyperparameters__rl')
# Load stage-specific logs
sft_logs = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig__v1', 'logs__sft')
rl_logs = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig__v1', 'logs__rl')
# Load evaluation results with annotations
sft_eval_results = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig__v1', 'evals_eval_sft')
rl_eval_results = load_dataset('TAUR-dev/D-ExpTracker__FinEval_16k_fulleval_3arg_OT_ours_1k_atstep100-RL-longmult_3dig__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_3arg_OT_ours_1k_atstep100-RL-longmult_3dig - {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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