{
  "base_model": "Qwen/Qwen2.5-0.5B-Instruct",
  "output_dir": "models\\club-slm",
  "corpus": "J:\\MAJOR PROJECT 2026\\MAIN\\data\\clean_corpus.txt",
  "dataset": "data\\python\\train_qa.jsonl",
  "qa_pairs": 7821,
  "train_rows": 7429,
  "eval_rows": 392,
  "epochs": 5,
  "batch_size": 2,
  "grad_accum": 8,
  "effective_bs": 16,
  "lr": 3e-05,
  "max_length": 256,
  "precision": "bf16",
  "device": "cuda",
  "train_time_min": 119.6,
  "eval_loss": 0.335335,
  "perplexity": 1.3984,
  "token_accuracy": 0.32498,
  "exact_match": 0.0,
  "fuzzy_match": 0.0,
  "total_tokens": 99960,
  "correct_tokens": 32485,
  "wrong_tokens": 67475,
  "total_samples": 392,
  "exact_matches": 0,
  "fuzzy_matches": 0,
  "epoch_losses": [
    {
      "epoch": 0.02,
      "train_loss": 3.673507,
      "step": 10
    },
    {
      "epoch": 0.04,
      "train_loss": 3.380849,
      "step": 20
    },
    {
      "epoch": 0.06,
      "train_loss": 2.831332,
      "step": 30
    },
    {
      "epoch": 0.09,
      "train_loss": 2.20228,
      "step": 40
    },
    {
      "epoch": 0.11,
      "train_loss": 1.890123,
      "step": 50
    },
    {
      "epoch": 0.13,
      "train_loss": 1.625928,
      "step": 60
    },
    {
      "epoch": 0.15,
      "train_loss": 1.608759,
      "step": 70
    },
    {
      "epoch": 0.17,
      "train_loss": 1.523835,
      "step": 80
    },
    {
      "epoch": 0.19,
      "train_loss": 1.391224,
      "step": 90
    },
    {
      "epoch": 0.22,
      "train_loss": 1.276159,
      "step": 100
    },
    {
      "epoch": 0.24,
      "train_loss": 1.265831,
      "step": 110
    },
    {
      "epoch": 0.26,
      "train_loss": 1.06008,
      "step": 120
    },
    {
      "epoch": 0.28,
      "train_loss": 1.173187,
      "step": 130
    },
    {
      "epoch": 0.3,
      "train_loss": 1.191657,
      "step": 140
    },
    {
      "epoch": 0.32,
      "train_loss": 1.120185,
      "step": 150
    },
    {
      "epoch": 0.34,
      "train_loss": 1.093464,
      "step": 160
    },
    {
      "epoch": 0.37,
      "train_loss": 0.995333,
      "step": 170
    },
    {
      "epoch": 0.39,
      "train_loss": 1.068814,
      "step": 180
    },
    {
      "epoch": 0.41,
      "train_loss": 1.006557,
      "step": 190
    },
    {
      "epoch": 0.43,
      "train_loss": 1.053628,
      "step": 200
    },
    {
      "epoch": 0.45,
      "train_loss": 0.995194,
      "step": 210
    },
    {
      "epoch": 0.47,
      "train_loss": 0.957164,
      "step": 220
    },
    {
      "epoch": 0.5,
      "train_loss": 0.979695,
      "step": 230
    },
    {
      "epoch": 0.52,
      "train_loss": 0.925564,
      "step": 240
    },
    {
      "epoch": 0.54,
      "train_loss": 0.888211,
      "step": 250
    },
    {
      "epoch": 0.56,
      "train_loss": 0.914199,
      "step": 260
    },
    {
      "epoch": 0.58,
      "train_loss": 0.893902,
      "step": 270
    },
    {
      "epoch": 0.6,
      "train_loss": 0.881656,
      "step": 280
    },
    {
      "epoch": 0.62,
      "train_loss": 0.850408,
      "step": 290
    },
    {
      "epoch": 0.65,
      "train_loss": 0.809787,
      "step": 300
    },
    {
      "epoch": 0.67,
      "train_loss": 0.853976,
      "step": 310
    },
    {
      "epoch": 0.69,
      "train_loss": 0.821917,
      "step": 320
    },
    {
      "epoch": 0.71,
      "train_loss": 0.881752,
      "step": 330
    },
    {
      "epoch": 0.73,
      "train_loss": 0.770348,
      "step": 340
    },
    {
      "epoch": 0.75,
      "train_loss": 0.760463,
      "step": 350
    },
    {
      "epoch": 0.78,
      "train_loss": 0.791171,
      "step": 360
    },
    {
      "epoch": 0.8,
      "train_loss": 0.717607,
      "step": 370
    },
    {
      "epoch": 0.82,
      "train_loss": 0.782005,
      "step": 380
    },
    {
      "epoch": 0.84,
      "train_loss": 0.772476,
      "step": 390
    },
    {
      "epoch": 0.86,
      "train_loss": 0.727276,
      "step": 400
    },
    {
      "epoch": 0.88,
      "train_loss": 0.781023,
      "step": 410
    },
    {
      "epoch": 0.9,
      "train_loss": 0.752274,
      "step": 420
    },
    {
      "epoch": 0.93,
      "train_loss": 0.717247,
      "step": 430
    },
    {
      "epoch": 0.95,
      "train_loss": 0.720272,
      "step": 440
    },
    {
      "epoch": 0.97,
      "train_loss": 0.737959,
      "step": 450
    },
    {
      "epoch": 0.99,
      "train_loss": 0.697645,
      "step": 460
    },
    {
      "epoch": 1.01,
      "train_loss": 0.548655,
      "step": 470
    },
    {
      "epoch": 1.03,
      "train_loss": 0.550381,
      "step": 480
    },
    {
      "epoch": 1.05,
      "train_loss": 0.569225,
      "step": 490
    },
    {
      "epoch": 1.08,
      "train_loss": 0.538405,
      "step": 500
    },
    {
      "epoch": 1.1,
      "train_loss": 0.520008,
      "step": 510
    },
    {
      "epoch": 1.12,
      "train_loss": 0.525656,
      "step": 520
    },
    {
      "epoch": 1.14,
      "train_loss": 0.555653,
      "step": 530
    },
    {
      "epoch": 1.16,
      "train_loss": 0.554701,
      "step": 540
    },
    {
      "epoch": 1.18,
      "train_loss": 0.56037,
      "step": 550
    },
    {
      "epoch": 1.2,
      "train_loss": 0.571677,
      "step": 560
    },
    {
      "epoch": 1.23,
      "train_loss": 0.506848,
      "step": 570
    },
    {
      "epoch": 1.25,
      "train_loss": 0.503055,
      "step": 580
    },
    {
      "epoch": 1.27,
      "train_loss": 0.560167,
      "step": 590
    },
    {
      "epoch": 1.29,
      "train_loss": 0.528751,
      "step": 600
    },
    {
      "epoch": 1.31,
      "train_loss": 0.475181,
      "step": 610
    },
    {
      "epoch": 1.33,
      "train_loss": 0.537284,
      "step": 620
    },
    {
      "epoch": 1.36,
      "train_loss": 0.518218,
      "step": 630
    },
    {
      "epoch": 1.38,
      "train_loss": 0.528746,
      "step": 640
    },
    {
      "epoch": 1.4,
      "train_loss": 0.464697,
      "step": 650
    },
    {
      "epoch": 1.42,
      "train_loss": 0.494603,
      "step": 660
    },
    {
      "epoch": 1.44,
      "train_loss": 0.502719,
      "step": 670
    },
    {
      "epoch": 1.46,
      "train_loss": 0.477892,
      "step": 680
    },
    {
      "epoch": 1.48,
      "train_loss": 0.489148,
      "step": 690
    },
    {
      "epoch": 1.51,
      "train_loss": 0.478489,
      "step": 700
    },
    {
      "epoch": 1.53,
      "train_loss": 0.519881,
      "step": 710
    },
    {
      "epoch": 1.55,
      "train_loss": 0.468913,
      "step": 720
    },
    {
      "epoch": 1.57,
      "train_loss": 0.530156,
      "step": 730
    },
    {
      "epoch": 1.59,
      "train_loss": 0.449579,
      "step": 740
    },
    {
      "epoch": 1.61,
      "train_loss": 0.433122,
      "step": 750
    },
    {
      "epoch": 1.64,
      "train_loss": 0.466816,
      "step": 760
    },
    {
      "epoch": 1.66,
      "train_loss": 0.466718,
      "step": 770
    },
    {
      "epoch": 1.68,
      "train_loss": 0.449744,
      "step": 780
    },
    {
      "epoch": 1.7,
      "train_loss": 0.429334,
      "step": 790
    },
    {
      "epoch": 1.72,
      "train_loss": 0.465894,
      "step": 800
    },
    {
      "epoch": 1.74,
      "train_loss": 0.392776,
      "step": 810
    },
    {
      "epoch": 1.76,
      "train_loss": 0.470184,
      "step": 820
    },
    {
      "epoch": 1.79,
      "train_loss": 0.463271,
      "step": 830
    },
    {
      "epoch": 1.81,
      "train_loss": 0.427761,
      "step": 840
    },
    {
      "epoch": 1.83,
      "train_loss": 0.421049,
      "step": 850
    },
    {
      "epoch": 1.85,
      "train_loss": 0.41458,
      "step": 860
    },
    {
      "epoch": 1.87,
      "train_loss": 0.425634,
      "step": 870
    },
    {
      "epoch": 1.89,
      "train_loss": 0.412593,
      "step": 880
    },
    {
      "epoch": 1.92,
      "train_loss": 0.417373,
      "step": 890
    },
    {
      "epoch": 1.94,
      "train_loss": 0.403305,
      "step": 900
    },
    {
      "epoch": 1.96,
      "train_loss": 0.411155,
      "step": 910
    },
    {
      "epoch": 1.98,
      "train_loss": 0.427315,
      "step": 920
    },
    {
      "epoch": 2.0,
      "train_loss": 0.402502,
      "step": 930
    },
    {
      "epoch": 2.02,
      "train_loss": 0.304085,
      "step": 940
    },
    {
      "epoch": 2.04,
      "train_loss": 0.285231,
      "step": 950
    },
    {
      "epoch": 2.06,
      "train_loss": 0.290745,
      "step": 960
    },
    {
      "epoch": 2.09,
      "train_loss": 0.270124,
      "step": 970
    },
    {
      "epoch": 2.11,
      "train_loss": 0.283372,
      "step": 980
    },
    {
      "epoch": 2.13,
      "train_loss": 0.284532,
      "step": 990
    },
    {
      "epoch": 2.15,
      "train_loss": 0.270921,
      "step": 1000
    },
    {
      "epoch": 2.17,
      "train_loss": 0.282726,
      "step": 1010
    },
    {
      "epoch": 2.19,
      "train_loss": 0.284413,
      "step": 1020
    },
    {
      "epoch": 2.22,
      "train_loss": 0.2682,
      "step": 1030
    },
    {
      "epoch": 2.24,
      "train_loss": 0.268169,
      "step": 1040
    },
    {
      "epoch": 2.26,
      "train_loss": 0.265762,
      "step": 1050
    },
    {
      "epoch": 2.28,
      "train_loss": 0.274262,
      "step": 1060
    },
    {
      "epoch": 2.3,
      "train_loss": 0.267679,
      "step": 1070
    },
    {
      "epoch": 2.32,
      "train_loss": 0.276804,
      "step": 1080
    },
    {
      "epoch": 2.34,
      "train_loss": 0.276497,
      "step": 1090
    },
    {
      "epoch": 2.37,
      "train_loss": 0.28569,
      "step": 1100
    },
    {
      "epoch": 2.39,
      "train_loss": 0.26311,
      "step": 1110
    },
    {
      "epoch": 2.41,
      "train_loss": 0.25099,
      "step": 1120
    },
    {
      "epoch": 2.43,
      "train_loss": 0.274988,
      "step": 1130
    },
    {
      "epoch": 2.45,
      "train_loss": 0.261295,
      "step": 1140
    },
    {
      "epoch": 2.47,
      "train_loss": 0.245447,
      "step": 1150
    },
    {
      "epoch": 2.5,
      "train_loss": 0.287245,
      "step": 1160
    },
    {
      "epoch": 2.52,
      "train_loss": 0.260615,
      "step": 1170
    },
    {
      "epoch": 2.54,
      "train_loss": 0.270639,
      "step": 1180
    },
    {
      "epoch": 2.56,
      "train_loss": 0.263159,
      "step": 1190
    },
    {
      "epoch": 2.58,
      "train_loss": 0.243453,
      "step": 1200
    },
    {
      "epoch": 2.6,
      "train_loss": 0.242194,
      "step": 1210
    },
    {
      "epoch": 2.62,
      "train_loss": 0.230759,
      "step": 1220
    },
    {
      "epoch": 2.65,
      "train_loss": 0.243978,
      "step": 1230
    },
    {
      "epoch": 2.67,
      "train_loss": 0.257585,
      "step": 1240
    },
    {
      "epoch": 2.69,
      "train_loss": 0.251543,
      "step": 1250
    },
    {
      "epoch": 2.71,
      "train_loss": 0.230343,
      "step": 1260
    },
    {
      "epoch": 2.73,
      "train_loss": 0.274376,
      "step": 1270
    },
    {
      "epoch": 2.75,
      "train_loss": 0.261679,
      "step": 1280
    },
    {
      "epoch": 2.78,
      "train_loss": 0.253968,
      "step": 1290
    },
    {
      "epoch": 2.8,
      "train_loss": 0.239549,
      "step": 1300
    },
    {
      "epoch": 2.82,
      "train_loss": 0.238915,
      "step": 1310
    },
    {
      "epoch": 2.84,
      "train_loss": 0.234489,
      "step": 1320
    },
    {
      "epoch": 2.86,
      "train_loss": 0.232685,
      "step": 1330
    },
    {
      "epoch": 2.88,
      "train_loss": 0.231499,
      "step": 1340
    },
    {
      "epoch": 2.9,
      "train_loss": 0.223252,
      "step": 1350
    },
    {
      "epoch": 2.93,
      "train_loss": 0.244325,
      "step": 1360
    },
    {
      "epoch": 2.95,
      "train_loss": 0.255999,
      "step": 1370
    },
    {
      "epoch": 2.97,
      "train_loss": 0.264762,
      "step": 1380
    },
    {
      "epoch": 2.99,
      "train_loss": 0.246688,
      "step": 1390
    },
    {
      "epoch": 3.01,
      "train_loss": 0.204261,
      "step": 1400
    },
    {
      "epoch": 3.03,
      "train_loss": 0.212866,
      "step": 1410
    },
    {
      "epoch": 3.05,
      "train_loss": 0.18749,
      "step": 1420
    },
    {
      "epoch": 3.08,
      "train_loss": 0.187718,
      "step": 1430
    },
    {
      "epoch": 3.1,
      "train_loss": 0.184144,
      "step": 1440
    },
    {
      "epoch": 3.12,
      "train_loss": 0.189121,
      "step": 1450
    },
    {
      "epoch": 3.14,
      "train_loss": 0.201176,
      "step": 1460
    },
    {
      "epoch": 3.16,
      "train_loss": 0.189117,
      "step": 1470
    },
    {
      "epoch": 3.18,
      "train_loss": 0.186818,
      "step": 1480
    },
    {
      "epoch": 3.2,
      "train_loss": 0.198263,
      "step": 1490
    },
    {
      "epoch": 3.23,
      "train_loss": 0.183168,
      "step": 1500
    },
    {
      "epoch": 3.25,
      "train_loss": 0.181653,
      "step": 1510
    },
    {
      "epoch": 3.27,
      "train_loss": 0.18247,
      "step": 1520
    },
    {
      "epoch": 3.29,
      "train_loss": 0.19287,
      "step": 1530
    },
    {
      "epoch": 3.31,
      "train_loss": 0.203817,
      "step": 1540
    },
    {
      "epoch": 3.33,
      "train_loss": 0.191013,
      "step": 1550
    },
    {
      "epoch": 3.36,
      "train_loss": 0.172727,
      "step": 1560
    },
    {
      "epoch": 3.38,
      "train_loss": 0.185981,
      "step": 1570
    },
    {
      "epoch": 3.4,
      "train_loss": 0.188215,
      "step": 1580
    },
    {
      "epoch": 3.42,
      "train_loss": 0.179959,
      "step": 1590
    },
    {
      "epoch": 3.44,
      "train_loss": 0.18324,
      "step": 1600
    },
    {
      "epoch": 3.46,
      "train_loss": 0.1866,
      "step": 1610
    },
    {
      "epoch": 3.48,
      "train_loss": 0.187446,
      "step": 1620
    },
    {
      "epoch": 3.51,
      "train_loss": 0.187642,
      "step": 1630
    },
    {
      "epoch": 3.53,
      "train_loss": 0.185423,
      "step": 1640
    },
    {
      "epoch": 3.55,
      "train_loss": 0.181782,
      "step": 1650
    },
    {
      "epoch": 3.57,
      "train_loss": 0.189735,
      "step": 1660
    },
    {
      "epoch": 3.59,
      "train_loss": 0.209074,
      "step": 1670
    },
    {
      "epoch": 3.61,
      "train_loss": 0.192625,
      "step": 1680
    },
    {
      "epoch": 3.64,
      "train_loss": 0.188765,
      "step": 1690
    },
    {
      "epoch": 3.66,
      "train_loss": 0.191266,
      "step": 1700
    },
    {
      "epoch": 3.68,
      "train_loss": 0.194638,
      "step": 1710
    },
    {
      "epoch": 3.7,
      "train_loss": 0.182128,
      "step": 1720
    },
    {
      "epoch": 3.72,
      "train_loss": 0.190359,
      "step": 1730
    },
    {
      "epoch": 3.74,
      "train_loss": 0.181668,
      "step": 1740
    },
    {
      "epoch": 3.76,
      "train_loss": 0.177796,
      "step": 1750
    },
    {
      "epoch": 3.79,
      "train_loss": 0.184682,
      "step": 1760
    },
    {
      "epoch": 3.81,
      "train_loss": 0.173,
      "step": 1770
    },
    {
      "epoch": 3.83,
      "train_loss": 0.175888,
      "step": 1780
    },
    {
      "epoch": 3.85,
      "train_loss": 0.172485,
      "step": 1790
    },
    {
      "epoch": 3.87,
      "train_loss": 0.189007,
      "step": 1800
    },
    {
      "epoch": 3.89,
      "train_loss": 0.206134,
      "step": 1810
    },
    {
      "epoch": 3.92,
      "train_loss": 0.18332,
      "step": 1820
    },
    {
      "epoch": 3.94,
      "train_loss": 0.195303,
      "step": 1830
    },
    {
      "epoch": 3.96,
      "train_loss": 0.195526,
      "step": 1840
    },
    {
      "epoch": 3.98,
      "train_loss": 0.188107,
      "step": 1850
    },
    {
      "epoch": 4.0,
      "train_loss": 0.195283,
      "step": 1860
    },
    {
      "epoch": 4.02,
      "train_loss": 0.176317,
      "step": 1870
    },
    {
      "epoch": 4.04,
      "train_loss": 0.170773,
      "step": 1880
    },
    {
      "epoch": 4.06,
      "train_loss": 0.180002,
      "step": 1890
    },
    {
      "epoch": 4.09,
      "train_loss": 0.169515,
      "step": 1900
    },
    {
      "epoch": 4.11,
      "train_loss": 0.182661,
      "step": 1910
    },
    {
      "epoch": 4.13,
      "train_loss": 0.169262,
      "step": 1920
    },
    {
      "epoch": 4.15,
      "train_loss": 0.175039,
      "step": 1930
    },
    {
      "epoch": 4.17,
      "train_loss": 0.17197,
      "step": 1940
    },
    {
      "epoch": 4.19,
      "train_loss": 0.174948,
      "step": 1950
    },
    {
      "epoch": 4.22,
      "train_loss": 0.186722,
      "step": 1960
    },
    {
      "epoch": 4.24,
      "train_loss": 0.183905,
      "step": 1970
    },
    {
      "epoch": 4.26,
      "train_loss": 0.175902,
      "step": 1980
    },
    {
      "epoch": 4.28,
      "train_loss": 0.178009,
      "step": 1990
    },
    {
      "epoch": 4.3,
      "train_loss": 0.189156,
      "step": 2000
    },
    {
      "epoch": 4.32,
      "train_loss": 0.172458,
      "step": 2010
    },
    {
      "epoch": 4.34,
      "train_loss": 0.174846,
      "step": 2020
    },
    {
      "epoch": 4.37,
      "train_loss": 0.171839,
      "step": 2030
    },
    {
      "epoch": 4.39,
      "train_loss": 0.174276,
      "step": 2040
    },
    {
      "epoch": 4.41,
      "train_loss": 0.172959,
      "step": 2050
    },
    {
      "epoch": 4.43,
      "train_loss": 0.176811,
      "step": 2060
    },
    {
      "epoch": 4.45,
      "train_loss": 0.17267,
      "step": 2070
    },
    {
      "epoch": 4.47,
      "train_loss": 0.179714,
      "step": 2080
    },
    {
      "epoch": 4.5,
      "train_loss": 0.179473,
      "step": 2090
    },
    {
      "epoch": 4.52,
      "train_loss": 0.172328,
      "step": 2100
    },
    {
      "epoch": 4.54,
      "train_loss": 0.176313,
      "step": 2110
    },
    {
      "epoch": 4.56,
      "train_loss": 0.177838,
      "step": 2120
    },
    {
      "epoch": 4.58,
      "train_loss": 0.183181,
      "step": 2130
    },
    {
      "epoch": 4.6,
      "train_loss": 0.176651,
      "step": 2140
    },
    {
      "epoch": 4.62,
      "train_loss": 0.169534,
      "step": 2150
    },
    {
      "epoch": 4.65,
      "train_loss": 0.167478,
      "step": 2160
    },
    {
      "epoch": 4.67,
      "train_loss": 0.178978,
      "step": 2170
    },
    {
      "epoch": 4.69,
      "train_loss": 0.176534,
      "step": 2180
    },
    {
      "epoch": 4.71,
      "train_loss": 0.175687,
      "step": 2190
    },
    {
      "epoch": 4.73,
      "train_loss": 0.178287,
      "step": 2200
    },
    {
      "epoch": 4.75,
      "train_loss": 0.178707,
      "step": 2210
    },
    {
      "epoch": 4.78,
      "train_loss": 0.187475,
      "step": 2220
    },
    {
      "epoch": 4.8,
      "train_loss": 0.184983,
      "step": 2230
    },
    {
      "epoch": 4.82,
      "train_loss": 0.17736,
      "step": 2240
    },
    {
      "epoch": 4.84,
      "train_loss": 0.171934,
      "step": 2250
    },
    {
      "epoch": 4.86,
      "train_loss": 0.196926,
      "step": 2260
    },
    {
      "epoch": 4.88,
      "train_loss": 0.175234,
      "step": 2270
    },
    {
      "epoch": 4.9,
      "train_loss": 0.18753,
      "step": 2280
    },
    {
      "epoch": 4.93,
      "train_loss": 0.166468,
      "step": 2290
    },
    {
      "epoch": 4.95,
      "train_loss": 0.170573,
      "step": 2300
    },
    {
      "epoch": 4.97,
      "train_loss": 0.177368,
      "step": 2310
    },
    {
      "epoch": 4.99,
      "train_loss": 0.162907,
      "step": 2320
    }
  ],
  "eval_losses": [
    {
      "epoch": 1.0,
      "eval_loss": 0.727038
    },
    {
      "epoch": 2.0,
      "eval_loss": 0.457646
    },
    {
      "epoch": 3.0,
      "eval_loss": 0.353403
    },
    {
      "epoch": 4.0,
      "eval_loss": 0.335049
    },
    {
      "epoch": 5.0,
      "eval_loss": 0.335335
    },
    {
      "epoch": 5.0,
      "eval_loss": 0.335335
    }
  ],
  "sample_log": [
    {
      "sample": 1,
      "reference": "You are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: Wh",
      "predicted": " are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: What ",
      "exact": false,
      "fuzzy": false,
      "tok_acc": "47/255"
    },
    {
      "sample": 2,
      "reference": "You are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: Wh",
      "predicted": " are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: What ",
      "exact": false,
      "fuzzy": false,
      "tok_acc": "78/255"
    },
    {
      "sample": 3,
      "reference": "You are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: Wh",
      "predicted": " are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: What ",
      "exact": false,
      "fuzzy": false,
      "tok_acc": "109/255"
    },
    {
      "sample": 4,
      "reference": "You are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: Ex",
      "predicted": " are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: What ",
      "exact": false,
      "fuzzy": false,
      "tok_acc": "75/255"
    },
    {
      "sample": 5,
      "reference": "You are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: Wh",
      "predicted": " are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: What ",
      "exact": false,
      "fuzzy": false,
      "tok_acc": "132/255"
    },
    {
      "sample": 6,
      "reference": "You are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: Ex",
      "predicted": " are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: What ",
      "exact": false,
      "fuzzy": false,
      "tok_acc": "149/255"
    },
    {
      "sample": 7,
      "reference": "You are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: Wh",
      "predicted": " are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: What ",
      "exact": false,
      "fuzzy": false,
      "tok_acc": "38/255"
    },
    {
      "sample": 8,
      "reference": "You are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: Wh",
      "predicted": " are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: What ",
      "exact": false,
      "fuzzy": false,
      "tok_acc": "49/255"
    },
    {
      "sample": 9,
      "reference": "You are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: Ex",
      "predicted": " are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: What ",
      "exact": false,
      "fuzzy": false,
      "tok_acc": "108/255"
    },
    {
      "sample": 10,
      "reference": "You are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: Ex",
      "predicted": " are a small domain model trained on one university corpus.\nAnswer briefly and exactly from the corpus.\n\nQuestion: What ",
      "exact": false,
      "fuzzy": false,
      "tok_acc": "91/255"
    }
  ]
}