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Model: appvoid/palmer-006 Source: Original Platform
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157
reports/appvoid_palmer-006_arithmark-3_results.json
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157
reports/appvoid_palmer-006_arithmark-3_results.json
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{
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"model": "appvoid/palmer-006",
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"tokenizer": "appvoid/palmer-006",
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"params": 91131072,
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"dataset": {
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"path": "/home/appvoid/arithmark-3.jsonl",
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"sha256": "bf8ab1a5193d52cdf0e05ff0b3ca226bdfcf416cb6e75562dcbe72e7e4559435",
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"items": 1000
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},
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"runtime": {
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"device": "cuda",
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"dtype": "torch.bfloat16",
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"batch_size": 32,
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"max_context": 1024,
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"attention_implementation": "auto",
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"compiled": false,
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"torch_version": "2.9.1+rocm7.2.0.git7e1940d4"
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},
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"primary_metric": "acc_norm",
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"results": {
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"arithmark-3": {
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"acc": 53.2,
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"acc_norm": 52.7,
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"raw_correct": 532,
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"norm_correct": 527,
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"total": 1000,
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"categories": {
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"elementary_school_math_continuation::addition::grades_1_2::easy": {
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"acc": 32.03125,
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"acc_norm": 31.25,
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"raw_correct": 41,
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"norm_correct": 40,
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"total": 128
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},
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"elementary_school_math_continuation::comparison::grades_2_3::medium": {
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"acc": 40.909090909090914,
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"acc_norm": 43.18181818181818,
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"raw_correct": 18,
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"norm_correct": 19,
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"total": 44
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},
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"elementary_school_math_continuation::comparison_difference::grades_2_3::medium": {
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"acc": 47.91666666666667,
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"acc_norm": 45.83333333333333,
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"raw_correct": 23,
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"norm_correct": 22,
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"total": 48
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},
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"elementary_school_math_continuation::data::grades_2_3::easy": {
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"acc": 32.55813953488372,
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"acc_norm": 30.23255813953488,
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"raw_correct": 14,
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"norm_correct": 13,
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"total": 43
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},
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"elementary_school_math_continuation::division::grades_3_4::medium": {
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"acc": 87.03703703703704,
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"acc_norm": 87.03703703703704,
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"raw_correct": 47,
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"norm_correct": 47,
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"total": 54
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},
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"elementary_school_math_continuation::fractions_counting::grades_3_4::medium": {
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"acc": 28.000000000000004,
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"acc_norm": 26.0,
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"raw_correct": 14,
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"norm_correct": 13,
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"total": 50
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},
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"elementary_school_math_continuation::geometry_area::grades_4_5::medium": {
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"acc": 94.23076923076923,
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"acc_norm": 94.23076923076923,
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"raw_correct": 49,
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"norm_correct": 49,
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"total": 52
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},
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"elementary_school_math_continuation::geometry_perimeter::grades_4_5::medium": {
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"acc": 64.44444444444444,
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"acc_norm": 64.44444444444444,
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"raw_correct": 29,
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"norm_correct": 29,
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"total": 45
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},
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"elementary_school_math_continuation::measurement::grades_2_3::easy": {
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"acc": 68.42105263157895,
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"acc_norm": 67.10526315789474,
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"raw_correct": 52,
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"norm_correct": 51,
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"total": 76
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},
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"elementary_school_math_continuation::money::grades_3_4::medium": {
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"acc": 37.5,
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"acc_norm": 37.5,
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"raw_correct": 24,
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"norm_correct": 24,
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"total": 64
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},
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"elementary_school_math_continuation::multiplication::grades_3_4::medium": {
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"acc": 81.08108108108108,
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"acc_norm": 81.08108108108108,
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"raw_correct": 60,
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"norm_correct": 60,
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"total": 74
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},
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"elementary_school_math_continuation::patterns::grades_3_4::medium": {
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"acc": 64.15094339622641,
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"acc_norm": 64.15094339622641,
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"raw_correct": 34,
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"norm_correct": 34,
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"total": 53
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},
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"elementary_school_math_continuation::subtraction::grades_1_2::easy": {
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"acc": 37.60683760683761,
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"acc_norm": 37.60683760683761,
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"raw_correct": 44,
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"norm_correct": 44,
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"total": 117
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},
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"elementary_school_math_continuation::time::grades_2_3::easy": {
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"acc": 100.0,
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"acc_norm": 100.0,
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"raw_correct": 55,
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"norm_correct": 55,
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"total": 55
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},
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"elementary_school_math_continuation::two_step_add_subtract::grades_2_3::medium": {
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"acc": 26.08695652173913,
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"acc_norm": 26.08695652173913,
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"raw_correct": 12,
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"norm_correct": 12,
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"total": 46
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},
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"elementary_school_math_continuation::two_step_addition::grades_2_3::medium": {
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"acc": 42.10526315789473,
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"acc_norm": 42.10526315789473,
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"raw_correct": 8,
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"norm_correct": 8,
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"total": 19
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},
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"elementary_school_math_continuation::two_step_subtraction::grades_2_3::medium": {
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"acc": 25.0,
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"acc_norm": 21.875,
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"raw_correct": 8,
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"norm_correct": 7,
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"total": 32
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}
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},
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"timing": {
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"tokenization_seconds": 0.19381939000004422,
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"evaluation_seconds": 17.806661402999907,
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"examples_per_second": 56.15875864475802
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},
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"primary_metric": "acc_norm",
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"primary_acc": 52.7
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}
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}
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}
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19763
reports/bananamid_base_1_1_report.json
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19763
reports/bananamid_base_1_1_report.json
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File diff suppressed because it is too large
Load Diff
94
reports/palmer-006_lm_eval_harness.log
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94
reports/palmer-006_lm_eval_harness.log
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================================================================================
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RUN 1 — ARC-Easy, ARC-Challenge, Winogrande, PIQA
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================================================================================
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2026-07-26:19:07:26 INFO [_cli.run:388] Selected Tasks: ['arc_easy', 'arc_challenge', 'winogrande', 'piqa']
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2026-07-26:19:07:28 INFO [evaluator:214] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 | Setting fewshot manual seed to 1234
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2026-07-26:19:07:28 INFO [evaluator:239] Initializing hf model, with arguments: {'pretrained': 'appvoid/palmer-006'}
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2026-07-26:19:07:49 INFO [models.huggingface:286] Using device 'cuda:0'
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config.json: 1.62kB [00:00, 3.38MB/s]
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tokenizer_config.json: 99.6kB [00:00, 54.3MB/s]
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tokenizer.json: 2.35MB [00:00, 11.4MB/s]
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special_tokens_map.json: 7.42kB [00:00, 19.4MB/s]
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2026-07-26:19:07:52 INFO [models.huggingface:579] Model parallel was set to False, max memory was not set, and device map was set to {'': 'cuda:0'}
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model.safetensors: 100%|█████████████████████| 182M/182M [00:08<00:00, 22.1MB/s]
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[transformers] The fast path is not available because one of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)` is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d
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Loading weights: 100%|██████████████████████| 386/386 [00:00<00:00, 2314.98it/s]
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generation_config.json: 100%|███████████████████| 133/133 [00:00<00:00, 571kB/s]
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README.md: 9.00kB [00:00, 161kB/s]
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ARC-Easy/train-00000-of-00001.parquet: 100%|██| 331k/331k [00:00<00:00, 803kB/s]
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ARC-Easy/test-00000-of-00001.parquet: 100%|██| 346k/346k [00:00<00:00, 1.63MB/s]
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ARC-Easy/validation-00000-of-00001.parqu(…): 100%|█| 86.1k/86.1k [00:00<00:00, 4
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Generating train split: 100%|█████| 2251/2251 [00:00<00:00, 24351.27 examples/s]
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Generating test split: 100%|█████| 2376/2376 [00:00<00:00, 393371.21 examples/s]
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Generating validation split: 100%|█| 570/570 [00:00<00:00, 154851.56 examples/s]
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ARC-Challenge/train-00000-of-00001.parqu(…): 100%|█| 190k/190k [00:00<00:00, 896
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ARC-Challenge/test-00000-of-00001.parque(…): 100%|█| 204k/204k [00:00<00:00, 959
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ARC-Challenge/validation-00000-of-00001.(…): 100%|█| 55.7k/55.7k [00:00<00:00, 2
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Generating train split: 100%|████| 1119/1119 [00:00<00:00, 177512.34 examples/s]
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Generating test split: 100%|█████| 1172/1172 [00:00<00:00, 224575.10 examples/s]
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Generating validation split: 100%|█| 299/299 [00:00<00:00, 101431.32 examples/s]
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README.md: 11.2kB [00:00, 25.5MB/s]
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winogrande_xl/train-00000-of-00001.parqu(…): 100%|█| 2.06M/2.06M [00:00<00:00, 4
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winogrande_xl/test-00000-of-00001.parque(…): 100%|█| 118k/118k [00:00<00:00, 284
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winogrande_xl/validation-00000-of-00001.(…): 100%|█| 85.9k/85.9k [00:00<00:00, 2
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Generating train split: 100%|█| 40398/40398 [00:00<00:00, 1299756.78 examples/s]
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Generating test split: 100%|█████| 1767/1767 [00:00<00:00, 430541.14 examples/s]
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Generating validation split: 100%|█| 1267/1267 [00:00<00:00, 403108.79 examples/
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piqa_train.parquet: 100%|██████████████████| 2.64M/2.64M [00:00<00:00, 3.25MB/s]
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piqa_validation.parquet: 100%|████████████████| 300k/300k [00:00<00:00, 726kB/s]
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piqa_test.parquet: 100%|█████████████████████| 496k/496k [00:00<00:00, 1.20MB/s]
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Generating train split: 100%|██| 16113/16113 [00:00<00:00, 686092.14 examples/s]
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Generating validation split: 100%|█| 1838/1838 [00:00<00:00, 398137.21 examples/
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Generating test split: 100%|█████| 3084/3084 [00:00<00:00, 528054.93 examples/s]
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2026-07-26:19:08:16 INFO [evaluator_utils:446] Selected tasks:
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2026-07-26:19:08:16 INFO [evaluator_utils:480] Task: arc_challenge (arc/arc_challenge.yaml)
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2026-07-26:19:08:16 INFO [evaluator_utils:480] Task: arc_easy (arc/arc_easy.yaml)
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2026-07-26:19:08:16 INFO [evaluator_utils:480] Task: piqa (piqa/piqa.yaml)
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2026-07-26:19:08:16 INFO [evaluator_utils:480] Task: winogrande (winogrande/default.yaml)
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2026-07-26:19:08:16 INFO [api.task:312] Building contexts for arc_easy on rank 0...
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100%|██████████████████████████████████████| 2376/2376 [00:04<00:00, 507.62it/s]
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2026-07-26:19:08:21 INFO [api.task:312] Building contexts for arc_challenge on rank 0...
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100%|██████████████████████████████████████| 1172/1172 [00:02<00:00, 505.21it/s]
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2026-07-26:19:08:24 INFO [api.task:312] Building contexts for winogrande on rank 0...
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100%|████████████████████████████████████| 1267/1267 [00:00<00:00, 73584.28it/s]
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2026-07-26:19:08:24 INFO [api.task:312] Building contexts for piqa on rank 0...
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100%|██████████████████████████████████████| 1838/1838 [00:02<00:00, 691.67it/s]
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2026-07-26:19:08:26 INFO [evaluator:585] Running loglikelihood requests
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Tokenizing inputs: 0%| | 0/20398 [00:00<?, ?it/s][transformers] Ignoring clean_up_tokenization_spaces=True for BPE tokenizer TokenizersBackend. The clean_up_tokenization post-processing step is designed for WordPiece tokenizers and is destructive for BPE (it strips spaces before punctuation). Set clean_up_tokenization_spaces=False to suppress this warning, or set clean_up_tokenization_spaces_for_bpe_even_though_it_will_corrupt_output=True to force cleanup anyway.
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Tokenizing inputs: 100%|████████████████| 20398/20398 [00:06<00:00, 3197.87it/s]
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Running loglikelihood requests: 100%|█████| 20398/20398 [04:21<00:00, 78.09it/s]
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2026-07-26:19:13:03 INFO [loggers.evaluation_tracker:316] Output path not provided, skipping saving results aggregated
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hf ({'pretrained': 'appvoid/palmer-006'}), gen_kwargs: ({}), limit: None, num_fewshot: None, batch_size: 16
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| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
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|-------------|------:|------|-----:|--------|---|-----:|---|-----:|
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|arc_challenge| 1|none | 0|acc |↑ |0.2619|± |0.0128|
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| | |none | 0|acc_norm|↑ |0.2927|± |0.0133|
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|arc_easy | 1|none | 0|acc |↑ |0.5097|± |0.0103|
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| | |none | 0|acc_norm|↑ |0.4482|± |0.0102|
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|piqa | 1|none | 0|acc |↑ |0.6376|± |0.0112|
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| | |none | 0|acc_norm|↑ |0.6360|± |0.0112|
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|winogrande | 1|none | 0|acc |↑ |0.5036|± |0.0141|
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================================================================================
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RUN 2 — HellaSwag
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================================================================================
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2026-07-26:20:16:54 INFO [_cli.run:388] Selected Tasks: ['hellaswag']
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2026-07-26:20:16:56 INFO [evaluator:214] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 | Setting fewshot manual seed to 1234
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2026-07-26:20:16:56 INFO [evaluator:239] Initializing hf model, with arguments: {'pretrained': 'appvoid/palmer-006'}
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2026-07-26:20:17:01 INFO [models.huggingface:286] Using device 'cuda:0'
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2026-07-26:20:17:02 INFO [models.huggingface:579] Model parallel was set to False, max memory was not set, and device map was set to {'': 'cuda:0'}
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The fast path is not available because one of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)` is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d
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Loading weights: 100%|█| 386/386 [00:00<00:00, 1408.36it/s, Materializing param=
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2026-07-26:20:17:11 INFO [evaluator_utils:446] Selected tasks:
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2026-07-26:20:17:11 INFO [evaluator_utils:480] Task: hellaswag (hellaswag/hellaswag.yaml)
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2026-07-26:20:17:11 INFO [api.task:312] Building contexts for hellaswag on rank 0...
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100%|███████████████████████████████████| 10042/10042 [00:07<00:00, 1317.53it/s]
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2026-07-26:20:17:19 INFO [evaluator:585] Running loglikelihood requests
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Tokenizing inputs: 100%|████████████████| 40168/40168 [00:23<00:00, 1726.55it/s]
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Running loglikelihood requests: 100%|█████| 40168/40168 [17:38<00:00, 37.96it/s]
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2026-07-26:20:35:31 INFO [loggers.evaluation_tracker:316] Output path not provided, skipping saving results aggregated
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hf ({'pretrained': 'appvoid/palmer-006'}), gen_kwargs: ({}), limit: None, num_fewshot: None, batch_size: 64
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| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
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|---------|------:|------|-----:|--------|---|-----:|---|-----:|
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|hellaswag| 1|none | 0|acc |↑ |0.3299|± |0.0047|
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| | |none | 0|acc_norm|↑ |0.3841|± |0.0049|
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